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Home > Blogs > How Data Science and Machine Learning is transforming the E-commerce industry in 2019
According to Barilliance, personalized product recommendation account for almost 31% of the revenues in the global E-commerce industry. The conversion rate for shoppers who do not go through recommendations stands at a measly 1.02%, while that percentage increases to a massive 288% after the first interaction. A separate study made by Salesforce found that online shoppers are 4.5 times more likely to add items to the shopping cart and complete a purchase after clicking on any product recommendation.
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Personalized product recommendations on popular E-commerce websites like Amazon and Netflix is just one the many ways in which data science technologies like Machine learning (ML) is transforming customer experience from simply good to exceptional.
More on Personalization
So, why is product personalization so critical for online retailers? A 2017 study conducted by Segment reveals that only around 22% of online shoppers are satisfied with the level of personalized shopping experience that they receive with E-commerce brands. A market study conducted by technology company, Infosys concludes that 31% of the surveyed customers are wishing for a more personalized shopping experience. Despite the ongoing debate about protecting user privacy on the Internet, a Salesforce research found that 52% of online shoppers are willing to share their personal data in return for more personalized product recommendations.
Product recommendations based on “what customers ultimately buy” or the “best-selling product” along with sending customer e-mails with personal product recommendations are also improving conversions particularly among first-time customers.
Predictive forecasting and intelligence
Enabled by artificial intelligence (or AI), predictive forecasting is a technique that can disrupt E-commerce sales forecasting on the basis of Big data and seasonal indicators. For example, AI technology can use the current weather forecast data to predict the short-term demand and sales trends.
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To make its predictions, predictive forecasting uses a variety of data sources including:
1. History of previous sales
2. Economic indicators
3. Customer searches
4. Demographic data
Along with predictive forecasting, AI-powered predictive intelligence technology is being used to predict and deliver what online customers need even before they look for a product. Among the many customer success stories for Salesforce, predictive intelligence enabled online furniture retailer, Room & Board to increase its return on investment by a whopping 2900% simply by predicting and recommending additional purchases to its customers. B2B analytics companies like Lattice Engines and Mintigo combine customer data with individual activities on social media and websites to accurately identify sales prospects for their customers.
Customer Behaviour and Shopping Patterns
Apart from the business benefits of personalization, Big data analytics can be beneficial in determining customer behaviour and shopping patterns. For example, which are the retail brands that are most in demand among online shoppers? When do customers shop more for the type of products that you offer? When do online shoppers make high-value purchases?
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Based on these insights, E-commerce retailers can predict the market demand for products (or services) and devise more appropriate marketing strategies to tap into this demand.
Online shopping patterns are also useful in determining the right inventory level for a line of products. Online retailers can optimize their stock levels by predicting if the products in demand are going to be overstocked or understocked. Based on the insights provided by Big data analytics, you can manage your E-commerce operations such as supply chain, inventory, marketing channels, and product pricings more efficiently.
Customer-related KPIs and metrics
Among the important metrics (or KPIs) for E-commerce business, Customer Lifetime Value (or CLV) determines the overall value of revenue that each customer will bring during their association with the company.
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CLV benefits E-commerce retailers in the multiple ways, including:
1. Determine the right marketing strategies.
2. Determine the average cost of acquiring customers or Customer Acquisition Cost.
3. Set business objectives for future growth, expenses, revenue, and net profit.
4. Personalize customer purchases through up-selling and cross-selling.
5. Optimize business spending on marketing campaigns and online advertisements.
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As an E-commerce retailer, you know the challenges of acquiring a new customer. At the same time, after customer acquisition, customer retention is an important objective for online retailers. This is because loyal and repeat purchase customers generate around 40% of the company’s revenue. Customer retention is also key to increasing your CLV.
A customer churn model is effective for retailers to identify the customer who are more likely to switch to their competitor’s products and to take measures to retain these customers. Based on metrics such as number (and percentage) of lost customer and value (and percentage) of lost recurring business, the customer churn model can help E-commerce shops to:
1. Identify potential churn customers and devise retention campaigns.
2. Maintain and increase the CLV.
3. Minimize customer churn.
Be it through identity thefts, phishing, or account thefts, online frauds grew at a rate of 30% in the year 2017 making it almost twice the percentage growth in retail sales. Apart from these types of thefts, shipping and billing-related frauds are also on the rise.
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Besides providing good products and exceptional customer experience, online retailers must ensure customers of the safety aspect of online transactions performed on their website. Online fraud can cause loss of revenue and also create a negative perception about the business among online shoppers leading them to avoid making online purchases with the concerned retailer.
A combination of data science and machine learning can be used to detect suspicious behaviour through the following indicators:
1. Different shipping and billing address
2. Large value orders
3. Use of multiple modes of payment for the same shipping address
4. International orders
According to this November 2016 study conducted by Deloitte, 72% of companies can effectively use Big data analytics to improve customer experience. This article reports that 72% of companies believe that Speech analytics can be an effective tool in improving customer experience and delivering business benefits.
So, how can data science help in improving customer service? While traditional forms of customer service comprised of product (or service) feedback from customers or reaching out to customers through phone or e-mail, the rise of data analytics has provided online retailers with valuable insights that is helping them provide better services.
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Enabled by natural language processing (or NLP), Sentiment analysis is an effective tool that can derive valuable insights from the large number of online customer reviews and ratings about a given product or brand. Data analytics tools such as the Word Cloud and N-grams can be used to make sense of user reviews by looking for selected words or word associations that convey what users think about the product or brand.
Data analytics can help E-commerce retailers to identify and resolve issues in products or services, thus enhancing the overall customer experience.
The benefits of using data science technologies including AI, machine learning, and natural language processing are immense and are driving the phenomenal growth of the global E-commerce industry. This article outlines 6 of the crucial areas where data science is making an impact. Be it a small or a global E-commerce retailer, investing in data science technologies can enable you to understand customer needs, improve customer service, design better products or services, and prevent online fraud, among other benefits.
And that completes our thoughts about the use of data science in E-commerce! We hope this article has been informative for your business. Do you agree with the multiple benefits of data sciences for E-commerce players as outlined in this article? We would love to hear your feedback in the comments section provided below. In the meantime, you can also check out our certification courses in Data sciences and Big data analytics.
Home > Blogs > Digital Marketing vs. Traditional Marketing -The Bottom Line
There was a time when the term Marketing meant a very limited set of things. It was about newspaper ads, huge-enough billboards, flyers, television ads, and radio announcements. Indeed, organizations were ready to shell out thousands of dollars in the marketing department, trying to smash the competitors. A number of catchphrases and rather stereotyped advertisements used to dominate the industry. Amidst of all this came the rise of the digital revolution.
Digital Revolution and Digital Marketing
The digital revolution has changed our lives, but that’s another story. As far as marketers were concerned, it did start a new strand — Digital Marketing. Like almost everything else in the world, Marketing became the so-called Traditional Marketing. Now, after more than a decade later, we need to renovate how we reach the right audience at the right time. That is also why the discussion of digital marketing versus traditional marketing comes to the limelight.
Here, we are attempting to have a brief-enough comparison between digital and traditional marketing. We will be considering the major factors, such as affordability, audience control, customizability and the number of potential results, etc. But, before we do that, we have to make the distinction clear-cut. So, to begin with, we will have an introduction to both traditional marketing and digital marketing.
What Is Traditional Marketing?
Traditional marketing, earlier known as marketing, is a bunch of promotional methods using channels such as newspapers, television, flyers, billboards, magazines, radio, etc. The history of traditional marketing goes back to centuries, where people used to distribute pamphlets for commercial and informational purposes. I would love to not get into that old story, though.
In the modern world, traditional marketing found its roots after the bloom of television culture, popularization of newspapers and overall growth of a capitalist society. As we said earlier, before the digital world became popular, marketing meant only these things. You had to potentially spend thousands of dollars if you wanted to run a TV or radio commercial, you know. But, that’s another aspect of it all.
There are thousands of examples to talk about. It’s no surprise that technology companies like Google and Apple have to sometimes depend on traditional marketing channels, to ensure the right client-base. And, it is also an allusion to the countless full-page newspaper ads you see, for iPhone XS or the brand-new vehicle from General Motors.
Here are some brands/influencers that have used traditional marketing at its best:
1) See Zomato’s creative billboards Ads here.
2) 50 brilliant billboards Ads and what you can learn from them.
3) See 40 traffic-stopping billboards Ads here.
4) Get to know 64 more print Ads here.
5) See 15 interactive print Ads here.
What Is Digital Marketing?
Often considered the sibling of the digital revolution, digital marketing is the use of channels such as the Internet, social media, world wide web, Search Engines, and emails, etc. for promoting a product, service, brand or a cause. As it happens, the history goes back to the origins of the Internet. Or, specifically, the point in the history of the Internet where people understood the monetization capacity.
In the course of time, digital marketing has come a long way. From disruptive advertisements to well-planned content marketing campaigns, the growth has been impressive indeed. More importantly, this opened up a gateway for thousands of business owners across the world to reach the targeted audience. Because digital marketing is based on a global network, its globality comes as something default.
Digital Marketing too take different forms nowadays. Let’s start with the routine marketing newsletters you receive on your email and to the taste-curated Facebook Ads you see while scrolling. And yes, we can never ignore the rather impressive way how Google understands what you are looking for and delivers the right ads at the right time.
Here are some brands/influencers that have used digital marketing at its best:
3) Watch some of the best YouTube Ads here.
4) Get to know 30 brands with best digital marketing campaigns here.
6) Get to know the 75 best content writing examples that will give you major SEO boosting goals, here.
Now, we don’t want to do the pro-con deal here. Instead, we will consider the major aspects of both strands. Shall we begin?
Factor #1 — Effort
By effort, we mean a variety of things, including but not limited to the affordability, the amount of work you have to put in.
Traditional marketing has been quite expensive from the very beginning. You would have to dedicate some thousands of dollars if you want a top-spot advertisement in a newspaper. The story is the same when you’re trying to include a second-long in the primetime TV commercial section.
In addition to this money, you will have to take extra efforts as well. Because there is so little space on both TV and newspapers, you will have to craft the advertisement at the most precise level. In most cases, you will have to rely on a professional to do this, costing more money than ever.
Overall, the effort you put into traditional marketing is way too high.
Digital Marketing was introduced as an affordable alternative to traditional marketing that could reach a large amount of audience. Compared to what you spend in traditional ways, digital marketing costs only a fraction of everything. The more you’re willing to pay, there is a potential for results though.
You don’t have to be an advertising tycoon to master Digital Marketing either. Nowadays, you can find a lot of simplified Digital Marketing courses that can make you an expert in a variety of things, such as SEM, SEO, content marketing and email marketing. And, once you master these, you’re basically the king.
As it happens, you take less effort and pay less.
Factor #2 — Reach
Of course, you want to know where and how deep your marketing campaign reach. Here too, we can spot a big difference between both.
It’s very difficult and nearly impossible to predict the exact reach of your traditional marketing campaign. As you sign the contract, the television channel or newspaper may talk about its active viewers and number of subscribers. But, these don’t really correspond with the number of people who actually see your ad.
More importantly, any interaction via traditional marketing medium is passive. Be it a billboard, commercial on TV or in a newspaper ad, you never know what your audience think about the stuff. Of course, you may be knowing this, but the time will be very late.
The whole deal of reach lacks precision when you’re going traditional.
Extensive reach is often the main reason why people go for digital marketing instead of traditional marketing. As we said earlier, the whole deal of digital marketing is based on a global network. So, by paying a fraction of the amount that you normally pay, you can reach out to a relatively huge population.
The nature of the reach is also important — it’s interactive. When you launch an email marketing campaign or content marketing stream, you have the ability to get instant (at least later) feedback. You can also bring in various methods like A/B Testing and multivariate testing to optimize the potential reach.
There is obviously a bigger upgrade here.
Exception: There is, however, one thing to note. There are still areas where digital mediums don’t have extensive reach. But, it finally depends upon whether you’re focusing on that particular population. If that’s the case, you may have to fall back to traditional marketing terms like community radio or a type of local newspaper.
It is quite unlikely, though, considering that the whole digital network is growing day by day. If we take the case of developing countries like India, a lot of extensions is happening and there are now a lot of ways to reach the local population, via digital.
Factor #3 — Control
For every marketing campaign to succeed, there should be proper control over the process. It begins with a clear-cut distinction of the audience being targeted.
Traditional marketing allows a limited amount of control over the marketing process. For instance, when we consider the case of local TV and radio channels, you have an opportunity to focus on a targeted population. In addition to this, you can do individualized marketing as well.
Apart from the preliminary marketing techniques, however, there is nothing new to talk about. When you run an advertisement on TV, you don’t know if your targeted customers are watching your ad. You are not even sure if they are skipping the ad for something else.
There is not much scope for control here.
It does not matter whether you are into Social Media Marketing or Search Engine Marketing, there is a lot of scope for control. Ultimately, you get to decide who sees your advertisement campaign and who do not. For instance, in Search Engine Marketing, your ads are shown when a person searches for a particular thing.
There are also options for additional customization. For instance, you can make sure that your ads reach an extremely specific group that has intended tastes and regional affiliations. It means that local businesses to have a way-out in the digital marketing sphere.
You are the one who controls things when you market digitally.
Factor #4 — Results & Analytics
At the end of the day, what people need is results. Or, to use the new marketing vocabulary, everyone needs what we call conversions.
There was a time when traditional marketing was highly effective, but that’s now just a story. Of course, you may have better results while focusing on a particular community. Nevertheless, when we consider that an actual TV commercial cannot make much impression on the user, the numbers fall down.
There is also a problem caused by the lack of data. The last time we checked, TV commercials or radio ads don’t show you the specific data-driven statistics. You never know how many people saw your ad you never know whether the campaign was really effective.
Add to this the expensive nature of every campaign ever.
We have already seen that digital marketing is more cost-effective than traditional means. It also happens to be the case that you can reach more people and create more impressions when you are marketing digital. With the same amount spent on both, DM offers better results on any day.
There is also the added benefit of data. Every activity is done in DM — from the user and the marketer — will be recorded. At the end of the day, you will have a huge pile of data to analyze and re-optimize your strategy. This is surely one of the reasons why the sustaining promotion that digital marketing offers.
We would have to consider data at least when we’re in 2019.
Factor #5 — The Miscellaneous
Here, we talk about a few other differences between digital marketing and traditional marketing that you’ve to consider.
One of the bright sides of traditional marketing is that people get to keep their marketing material or feel it experientially. It might be a brochure, a flyer or something else. But, the point is that they have some material that they can look, feel or make use of it again and again. This is often considered an added advantage.
If we go by examples, there are many brands to speak of. Almost every brand used to shell out thousands of dollars in creating brochures to be given out to the customers before, during and after the purchase. Simply by making sure that they have a marketing material at home, these brands were able to enhance the trust, reliability and many others. Of course, they will have to thank ‘mere exposure effect’ for this achievement.
The best examples of traditional marketing is experienced during a conclave, festivals, concerts, seminars and events. This is because all these events are filled of prints full of different hues - from ID Cards to posters to notepads. The variegated types of prints used in such events appeals the visitor and leaves a lasting impact by its design aesthetics.
Unlike Digital Marketing, the Traditional Marketing does not give importance to customization instead it aims to cater to a mass audience with common design printables. Is it mediocre? No. Traditional marketing has its own advantages. It is an experiential gateway to touch lives profoundly with words, textures, colors and variegated shapes.
There are algorithm and platform rules to be considered whilst running a digital Ad. But unlike digital marketing, traditional marketing is borderless. With traditional marketing - you can color streets (via graffiti art), giveaway cards & gift boxes to influencers, print an Ad in the shape of a cloud or flower, print an Ad or do company branding on infinite objects. Besides the common billboard approach, you can leverage print anywhere - on any spot of the cosmos (with legal permission of course).
Probably the best thing about digital marketing is that it gives the user a choice. There are multiple channels through which you can market the content you know — YouTube, Google Ads, and Facebook being some of the obvious choices. This will help you attract a variety of consumers.
The impact of Digital Marketing was so effective that established brands such as Domino’s, Renault among countless others have a few success stories. Talking about Renault, it was able to sell out 60,000 more cars in the period between 2014 and 2017 by making use of Facebook Ads optimization. In another story, Adidas — the renowned apparel firm — utilized user-generated content from 30,000 Boston Marathon runners to market their sport-oriented products. There are hundreds of other stories where established brands took a digital turn to enhance their sales, re-vitalize their reputation and build a better online image.
In another success story, GoPro made a big-time success by launching the GoPro Movement on Instagram. To everyone’s surprise, despite having a reduced budget of $41,000 in 2013, the brand was able to double its sales. In this particular case, GoPro was utilizing the potential of Instagram, which now records a top-notch conversion rate of 72%, according to Ogilvy.
We should also look at the multi-platform nature here. Your single marketing campaign can reach a variety of people who may not have the same device. Thanks to mobile-optimized Email Newsletters and websites, you can keep converting people even when they are traveling.
And yes, we should not forget the easiness of following up.
The Bottom Line
So, above, we have attempted to draw the line between digital marketing and traditional marketing. We had chosen five factors to judge both these strands, and we can finally conclude.
As you have seen, in most of the sections, digital marketing outweighs traditional marketing. For instance, it’s clear that digital marketing is cost-effective, offers more control over the process and offers better results. Unlike the old days, even if you want to focus on a local population, there are ways to do that digitally. It all comes down to the idea that digital marketing is way better.
Well, we don’t mean to say that you can completely ignore traditional marketing. There is an impact on a flyer or a big billboard can make in a person’s mind. But, you should not spend thousands of dollars there either. Here’s some practical advice to begin with: if you are short on budget, start with digital marketing. We can build further when you keep growing.
And that’s a wrap! We hope you leverage and test both - traditional marketing and digital marketing and analyze what works for your brand. With consistent efforts you’ll definitely know that both these mediums matter and are needed for a successful 360 degree marketing campaign for brands of every industry.
Home > Blogs > Is Data Science programming different as compared to programming in AI? Here is an analysis.
In recent years, the industry demand for data science professionals like data scientists or data analysts has been only matched by the equally rising demand for professionals in the field of Artificial intelligence (AI) or machine learning. According to the LinkedIn social media company, the U.S. industry is facing a shortage of over 150,000 specialists with data science skills while in India, the industry demand for data scientists has grown by over 400% in the year 2018. According to IIHT, there is a 60% rise in the demand for AI professionals, while the AI industry is set to create over 2.3 million jobs by the year 2020.
Despite the distinct role played by data science specialists as against professionals skilled in Artificial intelligence and machine learning (ML), there still exists a great deal of confusion with regards to their required programming skills for these 2 disciplines. While there are similarities in the roles performed by AI engineers and data science specialists, this article aims to highlight both similarities and differences between programming for AI and data science.
Introducing Artificial Intelligence and Data Science
Let’s start by introducing both AI and Data science and entailing what each of these technologies are equipped to perform.
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What is data science? Data science is the study and analysis of large volumes of both structured and unstructured data aimed to derive predictive and causal inference that can enable better decision-making. Data sciences use a variety of disciplines like mathematics and statistics along with techniques like data mining, predictive modelling, data visualization, and even machine learning to achieve desirable outcomes.
As a data scientist, you are expected to collect and analyse a variety of Big data to extract valuable business insights. This can include job functions such as:
Understand business needs or problems and formulate possible solutions.
1. Develop statistical models for data analysis.
2. Develop customized data models and algorithms.
3. Construct predictive models to enhance customer experience and business revenues.
4. Collaborate with product engineering teams and communicate results with business executives.
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In simple terms, AI is a field of computer science that equip computer systems to perform tasks typically performed by human beings. AI capability can include language processing, speech recognition, and visual perception. As a branch of AI technology, machine learning makes use of data algorithms designed for computer applications to predict accurate outcomes with minimum levels of programming.
The typical job functions performed by an ML specialist (or engineer) include:
1. Collaborate and develop data pipelines with data engineers.
2. Develop and improve effective machine learning models.
3. Write and review production-line code.
4. Analyse complex data sets and derive useful insights.
5. Develop ML-based algorithms and code libraries.
In the next section, we shall review the similarities and differences in software programming for these 2 disciplines and debunk some of the common myths associated with data science, machine learning, and Python programming.
Programming in Data Science versus AI/ML
Typically, the required programming skills for a data scientist includes experience in the use of Python, R, Java, SAS, and SQL database coding. Similarly, an AI/ML engineer needs to be well-versed with programming in Java, Python, and R.
Here are a few of the similarities:
Machine Learning Engineer
Performs the statistical analysis following by predictive modelling and prototyping of the algorithm.
Use the prototyped models and make them suitable for production by running them through software tools.
Translates a business problem into a technical model.
Integrate the technical model by building an API model that can make accurate predictions.
Determine the product features that must go into a data model.
Write the actual code to implement the features.
Here are a few of the basic differences between these 2 disciplines:
Retrieval, collection, and transformation of Big data
Apply a structured approach towards Big data through searching data patterns that can be useful for business decisions.
Comprises of multiple disciplines including software engineering, predictive analytics, and even machine learning.
One of the many subsets of artificial intelligence and comprises of 2 algorithms for supervised and unsupervised learning.
Includes techniques like anomaly detection, clustering analysis, regression analysis, and classification analysis
Includes techniques like supervised clustering, anomaly detection, classification, and regression.
1. Programming skills in Python, Scala, and SQL
2. Ability to work with unstructured data from social media and online sources
3. Understanding of other analytical skills including machine learning
4. Mathematical statistics for data analysis
1. Programming skills in Python and R
2. Probability and statistics
3. Data modelling skills
4.Fluent in computer fundamentals
Debunking common myths
Myth 1 about technical skills: Among the common myths about data science, programming is considered as the only skill required to become a professional data scientist. A good data scientist must be able to apply programming techniques in Python or R and write suitable library code (example, caret and numpy) to become an expert in data sciences. This is not true as data scientists require a mix of both technical and soft skills to be successful at their function. Some of the required soft skills include effective communication skills, problem-defining and solving skills, and a structured approach towards an effective solution.
Myth 2 about Deep learning: Another common myth is that deep learning is the methodology towards any data science or ML-based solution. While deep learning has been very effective in areas like computer vision and natural speech recognition, learning about deep learning frameworks like Tensorflow and Keras is not equal to gaining expertise in ML.
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As illustrated, deep learning is just one subset of ML that derives its concepts from various other fields like neural networks, information retrieval, and statistics. Deep learning is an ML algorithm that uses artificial neural networks, which are interconnected to each other and can process large data volumes.
Myth 3 about Python programming language: Thanks to its requirement in both data science and ML-related projects, Python programming is one skill that is increasingly prevalent among today’s developers. As a core language, Python is powering multiple projects including products like RedLaser barcode scanning app, the OpenStack cloud infrastructure project, and many more.
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Among the common myths about Python programming language is that it is a new programming language that only came into existence in the 21st century. The fact is that Python is over 25 years old with its first release made way back in 1991 (that is 4 years before Java). An additional myth is that Python language cannot be compiled like other programming languages like Java. Python code can be compiled using standard interpreters and compiles like PyPy and CPython.
Thanks to its flexibility, Python is being used in a variety of industry applications including online payments (example, PayPal and balanced payments), natural language processing (example, NLTK), Big data applications (example, Disco and Hadoop support), and machine learning (example, Orange and the SimpleCV computer vision platform).
In summary, data science is a multidisciplinary field that require both technical programming skills and knowledge of how other disciplines like statistics and machine learning operate. While data science is a generalized term that can be applied to any process that can analyse and manipulate data, a combination of AI and machine learning is instrumental in finding relevant information and insights from a large volume of Big data.
Though there is some degree of similarities and differences between data science and machine learning (as outlined in this article), both these disciplines are unique and important for today’s business enterprises. This article attempts to dispel some of the common myths related to programming in both of these disciplines.
That’s all for now, readers! We hope that this blog has provided answers you were seeking. We would love to hear your thoughts too. Don’t hesitate to leave your comments in the section below. You can also check out our Data Science course or our Artificial Intelligence course here.
Home > Blogs > ''Data Engineering'', The Best Thing Happening To Social Media Platforms?
Is there any one thing that is common among social media platforms like Twitter and Facebook? Yes, the enormous amount of data that they generate on a daily basis. According to this 2018 Forbes article, Facebook alone has 1.5 billion active users on a daily basis with 300 million photographs getting uploaded each day. Similarly, Instagram has a total number of 400 million active users each day posting 95 million photographs and videos.
Social media companies are not just generating large volumes of big data through content marketing and advertising but are also using a variety of business analytics tools to pull insights from the user such as age, place of location, gender, and their buying patterns. Additionally, small retail companies are using social media data to shape their online marketing campaigns. A prime example of this is the phone case manufacturer, Peel, which used the Facebook platform to register a 16x growth in their revenue.
Data Science vs Data Engineering
Whether it is for achieving business goals or foreseeing business problems, big data analytics is enabling organizations to move forward more decisively and respond better to market challenges. However, due to the high complexity of big data, companies need to recruit professionals with a high level of data expertise to implement big data-based solutions.
The growing demand (and shortage) of data specialists in fields like data science and data analysis is a reflection of the rising industry importance of the data science field.
At its core, data science is the process of efficient collection, processing, and visualization of big data. A data scientist typically needs to perform the following functions:
1. Define the business problem and ask the right questions on a given dataset.
3. Communicate the results of the data analysis through data visualization tools.
Even with all the potential shown by big data projects and the role of data scientists, Gartner reported that only arospecialized big data projects actually end up in actual production.
Data engineering is a specialised field that is critical for moving data science projects into production. Tomer Shiran, CEO of the big data middleware company, Dremio states that for the successful implementation of a data science project, companies will “typically take a ratio of one data engineer for every two data scientists.”
So, what is data engineering and how do data engineers differ from data scientists? In simple terms, a data engineer enables the data scientist to perform their job more efficiently by:
1. Building a data pipeline infrastructure for better data handling.
2. Improving the productivity of the data team by foreseeing production needs and removing any system bottlenecks.
3. Performing data collection and storage using various scripting languages.
4. Constructing data stores on database systems.
As compared to the intellectual knowledge of the data scientist, data engineers have more hands-on data skills that can be used to provide clean and structured data to the business. Data engineers are also more conversant with the best practices in software engineering, computer science, and database systems, along with data engineering technologies such as Hadoop and Kafka.
In the following section, we shall discuss 5 future trends of data engineering and how it is shaping business enterprises including social media companies.
Investments in Big Data
Companies that are investing in Big Data technologies must also be flexible enough to deal with constant changes in the way big data is handled and managed. Starting from Hadoop as the preferred environment, data engineering is moving towards adopting Spark or even a server-less environment in the near future. As an example, customers engagement company, Freshworks is boosting its investments in data science aimed to speed up its product development.
Improving ROI using Social Media Data
Be it for social media marketing or customer service, digital companies are increasing their presence on social media platforms to improve their ROI. A 2018 survey by Sprout Social concluded that improving the ROI on social media (or social ROI) is important for 55% of social media marketers.
In simple terms, social media data is used to show how online users are engaging with your social media content. Data engineers can use a variety of social media data including shares, hashtag usage, URL clicks, and keywords for performing data mining and analysis.
Depending on your organizational goal and the preferred social media platform for your business, Social ROI can be measured through various KPIs and metrics such as:
1. For Facebook:
a) User engagement including likes, comments, and shares
b) Impressions measuring the number of times users viewed your Facebook page
c) Organic likes generated without any online ad campaign
d) Page likes
e) Paid likes generated from a paid digital ad campaign
f) Type of reactions including Like, Love, Sad, or Angry
g) Number of unlikes
2. For Instagram:
a) Account impressions measuring the number of views for your stories
b) Total reach measuring the number of unique views of your posts
c) Website clicks and Profile visits
d) Number of likes and comments on your posts
3. For Twitter:
a) User engagement including clicks, retweets, and replies
b) Number of Twitter followers
c) Use of hashtags and @username by others
d) Number of posted and viewed tweets
4. For LinkedIn:
a) User engagement including the number of clicks on posts and company name
b) Number of new followers
c) User interactions including likes, comments, and shares
To save cost and time, an increasing number of companies are moving their big data to cloud-based platforms and solutions. However, lack of portability remains a major challenge as each of the cloud vendors use written code that is often incompatible with another vendor offering cloud services. To enable data portability, Google is offering cloud portability solutions (in association with VMWare) that allow software engineers to create and deploy web applications across multiple cloud environments. Data portability across multiple cloud environments is going to be a major enabler of data science-based solutions in the future.
How Data Engineers are using Social Media
Do data engineers use Social media platforms to gather data for their work? The findings of a 2017 survey of nearly 1200 engineers and design professionals by Engineering.com seems to suggest that the answer is “yes.”
41% of the surveyed engineers revealed that they collect their engineering content from social media platforms. Additionally, 39% of the surveyed professionals acquire their information from the 3 largest platforms namely Facebook, Twitter, and LinkedIn.
Another interesting trend is the growing availability of social media datasets that can be used by data specialists including data engineers. These large and free datasets are an integral requirement for data science projects and can be a reliable resource for developing a new data algorithm. An example of this is the Flickr Creative Commons dataset by Yahoo Webscope containing nearly 100 million images and 0.7 million videos. The cloud service provider, Infochimps has the Twitter Census dataset product that provides datasets derived from over 35 million tweets.
Managing a shortage of resources
Software and Big data companies are adopting innovative ways to overcome the market shortage of qualified personnel in data engineering, Retail company, Overstock, monitors its retail customer’s buying behavior through its One-to-One marketing machine that was built using a cloud-powered data analytics solution. Data scientists are taking on some of the functions performed by data engineers, as a result of which companies are looking at performing data integration in place of using data lakes.
Along with the abundance of data generated by popular social media platforms like Facebook, Twitter, and Instagram, social media data is a valuable input for data scientists and engineers in business enterprises to implement Big data projects and solutions.
This article evaluates the important role played by data engineers and how they have a different skill set as compared to that of data scientists. Additionally, this article covers some of the visible future trends of data engineering and social media data and how they are shaping modern business corporations.
Home > Blogs > How Much Control do Data Scientists Have over a Product Change?
From getting up in the morning to reading news, exercising, making breakfast choices, taking a cab or driving to work, working, socializing, driving back and getting some entertainment in the evening – everything we do today involves technology personalized to you. Wondering how those apps on your phone, tablets, or computers know about you and tailor their offerings to your best usage. The answer lies in the data!
The data they collect, process, and utilize to personalize your experience powers these technology products. This utilization of data is not limited to the consumer apps, it’s far widespread and finds its usage in all walks of life, industries, and society at large.
The people who derive value from scattered data and transform it into immensely useful form are ‘data scientists’. Their role takes the center stage in envisioning, building, and shaping of a product. World-leading products, such as Facebook, Google, AirBnB, Twitter and etc. have teams with a solid data science skillset. It’s really the synergy between the cross-disciplined teams that give rise to truly habit-forming products.
In this article, we’ll discuss the role data scientists play in transforming an idea into reality.
Role of Data Scientists in a Product’s Lifecycle
Data scientists play a very important role in various aspects related to product development, which includes:
1.Ensuring product viability
2.Identifying the right target markets and product’s sweet spot
3.Defining and refining user journeys
4.Product building and implementation
5.Product progress tracking on metrics
6.Success tracking of a product
7.Course correction and providing feedback
8.Marketing and sales
9.Future roadmap definition
A typical relationship between data science and product development looks like:
Myths Related to Data Scientists’ Roles in Product Development
There are several myths associated with the role of data scientists in the process of product development, such as:
Data scientists are an external entity to a product team
Data scientists are responsible to perform important functions at every single stage of the product lifecycle. Their deliverables help multiple stakeholders, including product leadership, technical teams, and customers. This is why considering them as external to the core system is one of the worst myths.
Data sciences are purely about predicting outcomes
Data science includes many skills beyond just predictive analytics. They, along with the data architects, are responsible for defining ways to collect data points, process and cleanse the data, store and secure the data, and in the end, perform analytics based on the business needs.
Data Science is an elegant way of producing reports
Reporting is just one of the several deliverables expected from data scientists. In addition to reporting, data scientists provide actionable insights, build data science related features, perform prescriptive, descriptive and predictive analytics and contribute to numerous decisions that involve data.
Accuracy of Analytics Outcomes is only dependent on the quantity of data
A common myth is, you need to provide large volumes of data to get correct results. This is absolutely untrue because of multiple reasons, such as:
1.Incorrect models will not deliver good results regardless of the data volumes
2.Quality of data directly affects the accuracy of outcomes
3.Too many independent variables introduce complexity that may impact results
Success Stories of Companies Using Data Science to Shape Their Products
1) DBS uses Data Sciences to Expand its Reach while Reducing Trade Anomalies
DBS, with its flagship product DigiBank, uses data sciences for multiple purposes, including marketing, business insights, and for transaction monitoring, credit monitoring, risk scoring, and fraud detection. For marketing and business insights, it utilizes data sciences to achieve identification of the right target segments, designing and running campaigns, tracking effectiveness and understanding the next best actions for their targeted segments.
Also, they have developed a trade alerts program that gave the bank a robust platform for detecting trade anomalies. This has boosted their ability to understand their clients better, make accurate judgments about the nature of their transactions, and even detect fraud anomalies based on the transaction trends.
With the use of data science effectively, DBS is now better equipped to comply with AML (Anti Money Laundering) regulations.
2) Google Maps Uses Data Sciences to Guide you Accurately
Do you realize how Google Maps offers you extremely accurate driving directions based on your constantly changing location on the go? The accuracy of their prediction is dependent upon the countless data points they capture and analyze user data sciences as their core weapon.
Google takes into account data obtained from its users’ movements as well as from its partnerships with the local city authorities to elicit construction updates, road closures, and accident data. For this specific purpose, they acquired a start-up, known as Waze, in 2013 and integrated it into their maps offering.
3) Shell Improves Productivity by Detecting Machine Failures
An Oil & Gas giant, Shell successfully uses data sciences to gain a multi-faceted impact on the quality of its processes, business decisions, maintenance costs, and environmental impact. Shell has transformed its asset management systems to use SAS that helps detect the machine anomalies at various sites, regardless of accessibility by humans. This directly extends the lifespans of their machines, which is a significant saving for the organization.
At Shell, data science is also empowering their business leaders to make decisions based on real data, eliminating the guesswork from decision making. This greatly reduces the operational, financial and reputational risk for them. In addition, usage of data sciences helps them understand and take proactive actions in the scenarios related to human safety and environmental impact of their exploration.
4) Your iPhones Recognizes You By Using Data Sciences
Apple introduced Face ID to enable its users to unlock their iPhones using their facial patterns. A unique combination of powerful hardware and data science algorithms it employs allows you to use Face ID to recognize every user uniquely and accurately. Face ID is able to automatically accommodate the changes in your appearances, such as growing facial hair, wearing cosmetic makeup and even, with accessories, such as hats, glasses, contact lenses, scarves, etc.
The TrueDepth camera used by Face ID records facial data and captures over 30,000 dots to create a depth map of a user’s face. It then employs its neural network engine to transform the captured data into a mathematical representation, which is then used each time authentication is attempted by a user.
Tips for Data Scientists to Contribute Better and Effectively to the Existing Products
Emphasis On ‘Supremacy Of Data As Source Of Truth’
As a data scientist, you have a special skill, that is, to rely solely on data as opposed to opinions or notions. You should establish yourself as a champion promoting the value of data as the most reliable source of truth. This involves creating a culture where claims are supported by evidence & data.
A great starting point can be identifying the metrics that can help product during its envisioning, market research, development process, go-to-market plans, and post-deployment activities. Identification, followed by collecting the relevant data and then, analyzing it to present findings that translate into actionable insights, can be your strategy to effect a wider change.
Sharpen Your Data Skills Beyond Pure Analytics
Data science is much more than pure analytics. It includes other aspects, such as data collection, data processing, data storage, and securing the data as well. In order to contribute more effectively to product development, it is advisable to become the owner of such aspects. Of course, this requires gaining these skills before you can play the role of a trusted advisor to the product team. Taking an online data science course can prove an extremely advantageous decision.
Build Reusable Data Sciences Tools To Help Business
Everyone, including the leadership, business teams, clients, and developers can become a true beneficiary of your data science expertise. In order to accomplish this, you can build a data science toolbox that delivers them information analyzed and synthesized per their needs. This requires understanding the demands of the stakeholders per role and then, collecting and analyzing data such that it really helps them make impactful business or technical decisions. In addition, it is vital to obtain regular feedback to ensure that your solution consistently stays on top of the situation.
Blend Into The Product Development Workstream
One big mistake often made by data scientists is that they tend to work in isolation as long as they have the data to analyze. This reduces their ability to contribute to product development. It is important for the data scientists to act as a core member of the team, following the overall development process, utilizing the skills available in the team, ensuring that data artefacts are tracked, version controlled and deployed in the same way the overall team is following.
And that’s a wrap! Hope this blog post proves to be insightful for you! Feel free to share your thoughts in the comments section! Also, If you seek to upskill your Data Science skills, feel free to check out our Data Science Courses here.
Home > Blogs > Nostradamus or a Creator – Why Data scientists are less of the former
Along with the growing demand for data scientists and data specialists around the globe, there is an increasing number of myths associated with the role of data scientists. Thanks to its predictive modeling capability, a data scientist is considered skilled enough to be able to build predictive models that can predict the future buying behavior of online customers. However, a common myth, particularly among non-technical professionals, is that a working day in the life of a data scientist purely comprises of building predictive models only.
So, is a data scientist simply the Nostradamus of the 21st century or is more of a “Creator” who creates useful business insights and solutions from the available data? In reality, data scientists are not just involved in creating efficient prediction models but can also provide strategic solutions and creative insights to businesses based on the data predictions. As an example, DataRobot enables its retail customer to use an Artificial Intelligence or AI-enabled predictive model resulting in an increase of $400 million (in retail profits) in just 3 months.
Through this article, we aim to burst the myth about how data science is not just a task of predictions but a tool to drive flourishing business solutions. In the following sections, let’s discuss 5 core skills of a data scientist that makes them a valuable asset to any business enterprise, followed by real-life solutions (driven by data science) that is transforming a variety of industries.
Core skills of a data scientist
First, let’s start with 5 core skills that every good data scientist must have:
1) Problem-defining skill
Every data scientist must be able to define and formulate a business problem. Through asking relevant questions to the subject experts, they must be able to simplify a complex business problem into simpler parts that can be easily categorized. This process requires a certain level of curiosity and hypothesis building.
At an intuitive level, data scientists know how to approach a particular business problem and typically follow them up with the following actions:
i) Identify the main features of a particular use case.
ii) Frame the right questions that would provide the desired responses.
iii) Decide on the various approximations to be applied.
iv) Consult with the right subject experts.
2) Technical skills
Once the business problem has been defined, data scientists require technical programming and statistical skills to extract the necessary data. While data scientists use a variety of programming languages, they must be familiar with the following software:
i) R, an object-oriented programming language that is used for data visualization, predictive modeling, and statistical analysis.
ii) Python programming language that is fast, powerful, and an easy-to-learn tool essential for data science.
iii) Structured Query language (or SQL) that is used for managing structured data in database systems.
iv) Hadoop, an open-source framework that facilitates distributed processing of huge volumes of data sets across computer clusters.
3) Analytical skills
After completing the data extractions, data scientists must possess the analytical skills to manipulate the data sets and extract value from it. As a data analytics company, Tableau offers products that work well with data science tools like R and Python. The Tableau tool works great for data exploration and analysis.
For example, Tableau Public can be a tool that can unleash creativity skills with its collection of rich data sets that can be used to create creative and engaging visualizations.
4) Visualization skills
As data visualization is an effective mode of presenting data in the form of visually-appealing dashboard tables, graphs, and even images, data scientists must be able to correctly interpret the data results and connect them back to the original business problem.
A great example of effective data visualization is that of Andy Kriebel’s dashboard that reflects a financial statement using some cool visuals.
5) Communication and influencing skills
Data scientists must be able to build a compelling data-driven story that can influence decision-making by connecting a business problem to actionable insight. Along with story-telling skills, they must have effective communication skills that can convey their data insights to both technical and non-technical users.
How Data science can influence innovative solutions?
So, how is data science enabling innovative problem-solving solutions in the market today rather than just being a prediction tool? Here is a look at its impact on the following industries.
Data insights are driving the empowerment of government agencies thus providing indicators on their mission goals. Operating since 1790, the U.S. Census Bureau is adopting innovative modes of data collection with tools such as the American FactFinder, a database that stores data retrieved from various public surveys. The U.S. government is also praising the easy data access policy (followed by the U.S. National Centre for Health Statistics) that provides easy access to health records without compromising data security standards. The U.S. Department of Veteran Affairs (in association with students of the University of Virginia) have created a data-based hackathon that aims to improve healthcare access for U.S. veterans.
Be it a start-up or a large retailer, adoption of the latest technology remains the leading trend in this industry. Retailers continue to use AI and machine learning in data analysis to improve their marketing strategies and customer support. Business insider estimates that AI adoption will increase profit levels in retail by 60% by the year 2035.
Through its physical “Amazon Go” stores in the U.S. and Britain, online retail giant, Amazon is enabling its customers to shop for products without having to queue at the checkout line. With its AI-powered Amazon Go mobile app, the technology can automatically scan and detect the products being taken by the customers and charge the amount directly to the customer’s Amazon account.
Apart from the success of AI technologies in online retail, even brick-and-mortar retailers like Sephora and Stitching Fix are using AI to personalize their in-store customer experience. For example, the AI-enabled Color IQ beauty product (launched by Sephora) can recommend beauty products based on the skin tone of the shopper.
Besides personalization, data science-enabled AI and machine learning are being used to optimize the retailer’s bottom-line revenue through accurate inventory management and pricing.
Climate Change Mitigation
Data science is enabling the collection and analysis of climate-related data that is required to protect major cities and people from the devastating impact of climate change. For example, “Neighborhoods at Risk” is an interactive data tool that provides city planners the latest updates about climate-related risk factors such as heat and flooding to the local demography.
Big data and predictive analytics are playing a crucial role in providing real-time analytics about climate change. An example of this is the Global Forest Watch tool that analyses over 100 local and global data sets to collect data about forest conservation, land use, and deforestation.
Opioid addiction and abuse are among the leading causes of overdose deaths in the Western nations particularly in the U.S. In the year 2017 itself, there were over 72,000 deaths in the U.S. caused due to opioid-related overdoses.
The U.S. Food and Drug Administration (FDA) plans to reduce opioid abuse through the use of data analytics. Through a large-scale data warehouse, FDA plans to use machine learning algorithms and predictive analytics to assess vulnerability factors among opioid users and identify trends that contribute to the opioid epidemic.
The above 5 industry case studies are an eye-opener to how data science is playing an effective role in providing real-life solutions rather than just being a predictive tool.
Apart from being used as a predictive tool, data sciences encompass business functions such as business problem definition and creation of data-driven solutions that can improve customer experiences and revive environment and health-related problems. As a data scientist, you can build accurate data-driven predictive models along with tackling business-related problems with your technical and analytic skills.
Data science is not just confined to prediction tasks but envelopes a lot more capabilities and can be leveraged in almost every business - be it a tech giant or a startup.
Home > Blogs > 7 Tips from a Data Scientist to Data Analysts
The recent boom in the data industry has driven the demand for data science professionals at enterprise-level, across all industry verticals. There are job openings for data scientists, data engineers, and data analysts. And there seems to be a lot of confusion and varying opinions among people regarding the roles and skillsets driving this field. Although all these job titles sound similar and are related to data the devil is in the details.
Unfortunately, there are no defined skill-sets that can distinguish between the role of a ‘Data Scientist and Data Analyst. In fact, different companies have different definitions for both these roles, and there is a lot of grey area in between the two job titles.
Broadly analyzing, a Data Scientist is a professional who combines data handling and data visualization with sound business understanding to make smart business decisions. A data scientist is expected to deliver business impact and take insights from the raw, chaotic data thereby uncovering answers to the problems we did not know existed. Data science as a job profile demands skills such as data structuring, data mining, data visualization, analytical skills, programming skills, machine learning skills, and customer insights
The role of a data analyst, on the other hand, is to summarize data and provide futuristic inputs by identifying consistent patterns from the past and the current data. The primary role of a data analyst is to collect, curate, process, and arrange data from different sources. They are responsible for presenting data in the form of charts, graphs, and tables and use this structured data to build relational databases for companies.
The difference between skill-set, scope, and goals of data science and data analytics can be well understood from the image below -
Although there is a difference in the job responsibility of a data scientist and a data analyst, these two fields are exceptionally interconnected. They often work in close coordination to achieve the same goals i.e. of growth and development. For someone who aspires to become a data analyst, it is essential to understand the nuances of data science. And to help you with that, we bring you some solid advice from our star data scientist, Gunjan Narulkar.
Gunjan is currently working as the Chief Data Scientist at Data Semantics Pvt Ltd. As the head of the Hadoop and Predictive Analytics division at Data Semantics, he has a broad range of experience working with both data scientists and data analysts. And here are some word of advice from the expert data scientist for data analysts.
Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” –Stephen Few
Too often data storytelling is understood as effectively presenting data with visually-appealing data charts. However, data storytelling is much more than that. It is the art of weaving a rational story with clear logic that can strike the right chord with the stakeholders and give them enough insights to drive a decision.
More than the data presented, it depends on how the data is presented to a non-technical audience. Data storytelling follows a structured approach that involves a combination of 3 crucial elements, which are data, visuals, and narration.
As a data analysts, it is important that you learn the art of storytelling. The key skills required in a great storyteller are:
i) Knowing the audience and weaving the story to their understanding
ii) Clearly understanding the business problem and the solution derived
iii) Getting the right data at hand
iv) Strong presentation skills
v) Analyzing probable questions and preparing answers for them
Most top-notch data scientists code a lot and are comfortable handling a variety of programming tasks. To be a really successful data science expert, your programming skills should be a combination of computational and statistical abilities. You should be able to handle a large volume of real-time data and apply statistical models like clustering, optimization, regression, etc. to it.
Currently, the preferred language among data scientist is Python with the use of other languages such as R, Scala, Clojure, Java, and Octave.
Try to do a dummy project that highlights your strengths. Code wildly and to the point, you lose your sleep. As a data scientist, this will help you grow, learn something new, and most importantly hone your coding skills. Remember, the more toy problems you solve, the better equipped you will be to handle the real ones.
Data is all about numbers. To become a successful data scientist, the first thing you need to do is to get rid of your ‘fear' for number, i.e. mathematics. You can never succeed in your career as a data professional unless you are proficient at mathematics. Period.
As a data scientist, you will be working with a global organization to develop sophisticated financial models. For these models to be statistically and operationally relevant, large volumes of data will be needed. You will need to use your deep expertise in mathematics to develop these models that can shift key business strategies.
Don't think of mathematics as your enemy or get scared quickly by the complexity of the task at hand. Try to develop an intuition for mathematics as you learn about the different techniques and how these techniques can help you solve difficult problems. You can start with a basic course on statistics and mathematics with an enhanced focus on probability, algebra, set theory, functions, and graphs. Once your basic concept is strong, you can use technology tools to design complex financial models.
Domain expertise is something that makes a Data Scientist an expert! Having domain knowledge is not enough. As a data scientist, it is crucial to stay in front of the curve and understand which technology to apply and when. Unwavering focus on the domain helps us to understand the real problem which empowers us to create solutions that are useful on the ground, and not just "useless innovation".
A data scientist should always work closely with the business to measure and prove the effectiveness of the project on the ground. In addition to having an in-depth understanding of the problem, being aware of the latency, bandwidth, interpretability and other system boundary conditions, will help you understand what technology to apply.
A good data scientist is the one having traits of a good problem solver. Sometimes problem-solving needs assumption as you may not be able to test the solution on ‘real data'. To make such an assumption, you will need to bring critical thinking to the forefront and look at the problem from many perspectives. These perspectives give the data science experts a view of what they are supposed to be doing before pulling all the tools so that they can work to completely solve the problem.
Be creative and accepting of "out of the box" solutions because there are way more examples of success than failure using this method.
Many people entering the field of data science have this pre-conceived notion that data science is all about mathematics and statistics and they hone their ability to think that way. While learning new skills are essential, it is also vital that you work on sustaining your current skills as well.
In current times, the use of data science has found a broader horizon. And a broader horizon needs a wider knowledge in its ability to execute, and that is why the more things you know, the better it is for you. Remember your experience and contribution as an individual is what will help you climb up the corporate ladder.
One of the best approach to have a full-fledged career in Data Science is to pursue a certificate program/course that provides you a 360-degree knowledge, resources of portfolio preparation (capstone projects) and curriculum that covers the A-Z of Data Science. For example, courses like Manipal ProLearn’s Data Science course covers all those useful resources with its in-depth curriculum and practical learning methodology and helps you build a solid portfolio required for a career in Data Science. From beginner’s data science courses to PG diploma in data science, Big Data, Data Analytics, Machine Learning, etc., the choices are many. These courses can be done remotely and in addition to any degree, you are pursuing currently.
Also, once you’ve pursued an awesome course like the one listed above, what next? It’s essential for you to stay connected with Data Science resources - whether it be Popular Blogs, Podcasts, Useful Textbooks, Tutorials, or Video Channels.
Remember: Books are classic, but when it comes to fields like Data Science, AI/ML and Coding, it is the practical approach training that helps you uplift your skills!
A great data scientist is someone with the intelligence to handle data processing and an intuitive understanding of the business problem. While people with good maths skill can easily do the first part, the difficult part is to delve deeper into what you are doing. Someone with a deeper understanding and intuition of the model they are working on is likely to have a successful career in this field.”
And that’s a wrap! Hope this blog post proves to be insightful for you! Feel free to share your thoughts in the comments section! Also, If you seek to upskill your Data Science skills, feel free to check out our Data Science Courses here.
Home > Blogs > 5 Data Science Portfolios to Aspire for
For three consecutive years, data science has been ranked as the number one job in the U.S. In fact, it has been reported that over the period of next five years, there will be a significant increase in the global demand of data scientists which will create 11.5 million job openings by 2026.
Indian companies are also largely adopting data analytics and data-based decision making, and as a result, there has been a steady increase in the demand of data scientists over the last year with India contributing to 6% of data science job openings worldwide. The number of available data science jobs in India increased by 42% in 2015, 52% in 2016, and almost doubled in 2017. Currently, close to 97,000 positions related to analytics and data science are vacant, out of which 97% of openings are full-time while 3% are contractual/part-time roles.
The Dearth of ‘Qualified’ Data Science Professionals
From healthcare to banking, retail, fashion, and pharma, not just in the technology sector but data science experts are required in every sector. Data science has important applications across most industries. For example, your purchase data is used by Amazon to customize your Amazon home page and suggest you relevant products. Similarly, agriculture production data is used by farming companies to define ways for most efficient growth and delivery.
With so much happening in the field of data science, pursuing a career in it is clearly a smart move. In addition to the fact that it is one of the highest paying jobs in India, data is also the pivot point where the entire economy is expected to turn in the coming years.
However, as many as 97000 positions in data science related field are vacant in the dearth of qualified talent. In order to break into these high paying, in-demand job roles, you need to have an advanced degree in a related field. While the most common field of study for data scientists includes Mathematics and Statistics (32%) followed by Computer Science (19%) and Engineering (16%), people with Bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics are also preferred for the role.
It is important to note that a majority of data scientists are highly educated with 88% having at least a Master’s degree and 46% have PhDs as per the reports in KDnuggets, a leading site on Big Data.
While a Ph.D. or masters degree may not be the norm, but truth is that most aspiring data scientists undertake at least one or more online courses in a related field. A degree in any data science courses will give you the skills you need to process and analyze big data. Therefore, it is a good idea to enroll yourself in an online course in the field of Hadoop, Big Data, Machine Learning, Data Science, Mathematics, Astrophysics or any other related field. The skills you have learned during your degree program will enable you to easily transition to data science.
People Leading the Charge
Worldwide, the data science wave has been massive. And like any other technical trend, experts were quick to adopt as well as to explore and innovate in this technology. There are many celebrated and skillful data science experts who have not only made a difference to the organization they work with but have also positively impacted the data science arena across the globe. We pulled together the list of 5 amazing and influential data scientists worth following:
1) Bernard Marr
Twitter Profile - @BernardMarr
Recognized by LinkedIn as one of the world's top 5 business influencers and the No 1 influencer in the UK for 2018/2019, Bernard Marr is one of the world’s best technology expert pertaining to the intelligent use of data in business. Having authored more than 15 books and hundreds of high profile reports and articles, including the international best-sellers ‘ ‘Big Data in Practice’, 'Big Data', 'Key Business Analytics’, 'The Intelligent Company’, ‘Data Strategy’, and ‘Strategic Performance Management’, he is one of the world’s top business and technology influencers.
With over 1.3m followers on LinkedIn, over 160K fans on Facebook, and over 100k Twitter followers, Bernard is a major Social Media Influencer and actively engage with his follower on a regular basis. In addition, he is a frequent contributor to the World Economic Forum and high-profile publications such as The Times, The Guardian, The Financial Times, the CFO Magazine and the Wall Street Journal.
Currently, Bernard is working as a visiting professor for the Irish Management Institute, Oxford University, BPP, and ICAEW. He also has a seat on the dean's council for Lancaster University Management School.
2) John Elder
Twitter Profile - @johnelder4
Author of several books, such as the Handbook of Statistical Analysis and Data Mining Applications, Ensemble Methods in Data Mining, and Practical Text Mining, John Elder is an entrepreneur, an adjunct professor, a YouTuber, and a frequent keynote speaker.
He is the founder of Elder Research, Inc., America’s largest and the most experienced data mining consultancy firm. Ph.D. with degrees from Rice and UVA, he is an Adjunct Professor who takes classes on the optimization of data mining at the University of Virginia. He through his consultancy firm has solved hundreds of challenges for commercial and government clients and has been nominated by President Bush to serve a panel that guided technology for national security.
To know more about John’s work, get free data science tutorials and to follow his presentations, visit his YouTube page here.
3) Kira Radinsky
Twitter Profile @KiraRadinsky
Working as the director of data science at eBay, Kira Radinsky is also the founder and CTO at SalesPredict, where she leads the research and development of predictive data mining algorithms and external dynamics to revolutionalize the way companies do business. She is a regular writer about the application of Predictive Analytics at her blog as well as for Harvard Business Review. She is also a renowned speaker at the world's leading data mining conferences, WWW, SIGIR, AAAI and WSDM and many of her talks can be found on YouTube.
She has won many awards for predictive analysis as well as for her contribution to the field of data science. Kira is recognized by Forbes 30 Under 30 as a rising star of enterprise technology. To know more about her and her research work, follow her on LinkedIn.
4)John Myles White
Twitter Profile @Johnmyleswhite
John Myles White is a data engineer at Facebook. He is one of the youngest achievers in the field of data science with a high level of expertise in machine learning, statistics, data science, and the R programming language for statistics. He has authored several books on machine learning, such as Bandit Algorithms for Website Optimization, Machine Learning for Hackers, and Machine Learning for Email. All his books are dedicated to making machine learning easier for developers and hackers alike. Currently, he is focusing on his research on decision theory where he is trying to understand both how people make decisions and how they should make decisions.
You can find his work and other related presentations on YouTube.
5) Hilary Mason
Twitter Profile @hmason
Hilary Mason is a renowned data scientist and the founder of Fast Forward Labs, which was acquired by Cloudera in 2017. Hilary is the new vice president of research at Cloudera. She has also served as chief scientist at Bitly, Inc. and was a consultant data scientist at Accel. In 2010, she co-founded HackNY and is a prominent member of NYCResistor.
Hilary has received numerous accolades during her career including the TechFellows Engineering Leadership award in 2012 worth $100,000 as well as a citation by Fortune in its 40 Under 40 list. She was interviewed by TechRepublic, Forbes, Wall Street Journal, Programmable Web, and many others. She is also a popular social media influencer and has a large follower base on LinkedIn. She enjoys speaking on data science-related topics and you can find many of her presentations on YouTube.
What made these people extraordinary is their immense passion and dedication towards learning and exploring data science. Their continuous effort and quest to do work that can make a difference in other people’s lives. For someone who is passionate about data science and wants to make a mark in the field, opting for online courses in data science and the related field is the best way to begin.
Home > Blogs > 4 Tips For Addressing And Embracing Data Security Issues
There is a lot of data out there. In fact, over the last decade, there has been an explosion in both the data generated and the data retained by the companies. Striding through tonnes of data and coming up with a real-world solution is considered as a superpower. No wonder, data science is considered as the most enticing job title of the 21st century.
However, not all is a cake walk. There are many challenges that hinder the day to day operation of a data scientist dealing with which needs a lot of smart thinking, informed decision making, and analytical skills.
Future data scientists, here are some challenges you might have to deal with:
Working without Concrete Objectives
Often data scientists are expected to find solutions for a problem they are unaware of. Instead of having the liberty to work on a solution, data scientists have to first figure out the business problem and define various aspects of it. As chief scientist at SnapLogic, Greg Benson says, “Data scientists often run into the issue of trying to add artificial intelligence or machine learning capabilities without concrete objectives”
Dealing with Raw, Fragmented Data
A typical data scientist have to put forth an overwhelming amount of effort to create a clean data set before any machine learning or artificial intelligence algorithms can be applied. However, quite often the data is presented in such a scattered format that accessing them and formatting them can be quite difficult as well as time-consuming. Data scientists have to deal with poor data quality ranging from insufficient data to scattered data, hidden data, repetitive data, and so on. The primary challenge may be how to use the (enormous amount of) data, how to clean it, how to analyze it and how to build working models from it. All this take tremendous effort that often goes unnoticed.
Explain Technical Concepts to Non-Technical Audiences
A data scientist is usually working around technical terminologies. So, it doesn’t come as a surprise that most of their findings and conclusions are in technical terms as well. While the data scientist might be excited to share all the technical complexities and the long-drawn process they took to come to the conclusion, the stakeholders are only interested in the key findings and action items. Communicating effectively with a non-technical audience of other departments and making them understand why your model is of value to business stakeholders can be a source of frustration for data scientists.
Data security is one of the major challenges faced by data scientists today. Given that data is extracted through a lot of interconnected channels, there are multiple doors for a hacker to attack. It is difficult to implement security tools at all these ‘doors’ because these tools at times cannot distinguish between a genuine user and a hacker thereby hampering the data extraction process. Also, owing to the confidentiality element of the data obtained, data scientists face challenges in data extraction, data usage as well as in building algorithms.
A Deep Dive into the Issue of Data Security
The following characteristics of data security challenges stand out in the context of data science:
1) In most data science solutions, a humongous amount of business data is involved. The team of data scientists might be dealing with millions or billions of records, which can have a tremendous value of sensitivity depending upon the problem domain.
2) Not only is the volume of data is huge but to give an all-round solution, data scientists need to consider all attributes and dimensions of data. This contradicts with the ‘data minimization’ principles of privacy.
3) What makes data science different from traditional engineering is that all point data scientists are dealing with precious real data. There is no ‘dummy data’ at any stage. At all points in AI and ML, various stakeholders and systems are ‘learning from real data’. The real data gets into the system and remains there as it never did in the past. So many iterations for multiple varieties of data can disrupt even the most secure data governance system.
4) From a security standpoint, data science is still evolving. We are looking at a lot of new tools, frameworks, systems and system combinations. There are still a lot of unknown security threats because new tools often take time to become ‘secure’. Add to that the new stakeholders may not always have an even aware of the security
5) Given that data science has made a way into all walks of our lives, we are dealing with countless obscure data and record formats. Solutions involving so many systems and interfaces have to account for multiplier effect and are vulnerable to security bugs and failures.
4 Tips for Addressing Data Security Issues
While this may seem intimidating, security practitioners have a history of dealing with these challenges. The basic principles of good security still remain the same. Data security depends on how you mold the traditional practices to address the nuances and unique requirement of the new ecosystem.
1) Have a Good Data Governance Structure in Place
Ensure that all the team members, as well as stakeholders, are well aware of the basic security and privacy such as data authorization, classification, protection techniques as well as the applicable policy and standards. The main goal is to ensure that all stakeholders have a clear understanding and ownership of security and privacy as the data moves through different stages of the workflow. Given that data circulation in data science problems is huge and widespread, it is important that everyone uses the same terminology and follows the same privacy principles
2) Enforce Encryption for Accessing Critical Data
Business data can be classified into two broad categories – Public and Protected. While there is not much fuss about the security of public data, protected data needs to be kept secure and confidential. The first step in doing so is to identify the protected/sensitive set of data.
Once identified, you should further classify the sensitive data into ‘data in motion’ and ‘data at rest’. For data in motion, employ encryption techniques (such as SSL, TLS) to ensure the confidentiality and integrity of data are secured from eavesdropping. For data at rest, encryption techniques, such as SHA256, along with appropriate controls, such as role-based access, strong authentication mechanism are applied.
3) Perform Up-front Threat Modeling
Diligent threat modeling of solutions will ensure that the security is built-in into the design. When threat modeling is done at both component and end-to-end level, it ensures that appropriate security requirements are met at every point of data transmission. Given that production data is involved at every stage, it is important to thoroughly cover all workflows in the threat models while giving special attention to the boundaries and interfaces between different sub-systems.
4) Embrace Secured Programming Practices and Proactive Monitoring
Ensure that the development team follows the secured programming practices. Invest in educating your developers about potential security threats applicable to the solution, ways to mitigate them and tools to monitor threats. The OWASP Top Ten could be a great starting point for ensuring secured coding practices, followed by implementation of the tools for security monitoring.
In addition, ensure that you use the appropriate server and network hardening mechanisms, keep your software components patched and upgraded with the security-related fixes and conduct periodic reviews to assess the overall security situation at your organization. Also, opting for data science security courses will help them understand and embrace secured programming practices.
Finally, it’s vital to have a good incident response plan that describes the methods to deal with any security breaches and security-related disasters gracefully.
For data scientists, data security is one of the biggest challenges faced while extracting data because of the volume and sensitivity element of the data. So, there are no shortcuts there. But through effective analytics system, data science can enhance the cybersecurity industry. With the help of additional security checks, advanced use of machine learning & use of cloud platforms, cybercrimes & fraudulent practices can be resolved. It allows data scientists to come up with more operative and active measures to prevent cyber-attacks.
In addition to all the preventive measures, it is also very important that data scientists upskill themselves and learn about the latest data security measures and practices. Online security courses like HackerU’s cyber security courses, and analytics courses not only will help you tackle the issues like data security but also can help you make a career as a cybersecurity professional in this highly demanding field of data science.
Home > Blogs > Here’s how Companies can reduce the friction between Data Scientists and Product Developers
Alright, picture this: You’re a month deep in building a product, under pressure to hit the deploy & sales. You’re continuously appending & rectifying. Amidst the chaos, you ask yourself: “Why are we even building these features?”. As the team shifts from data to design and development, it’s inevitable that the focus too shifts from practical details to inclusive details.
In this data-driven age, can any digital product or service company establish itself in the market without the optimum use of its data? The answer is a big No. Digital companies like Facebook and LinkedIn are as popular for their service offerings as they are for the innovative ways that they use data to improve their products. For example, Facebook uses product teams comprising of both product developers and data scientists to measure the positive (or negative) impact of a new product feature before releasing it to over 2 billion Facebook users.
With the growing importance of Big data in product development, software engineering teams (or product developers) are required to increasingly collaborate with data specialists including data scientists and data analysts.
Spotify, the audio streaming company, is driving cross-disciplinary collaboration between user researchers and data science teams to drive better product decisions. Similarly, food tech company, Zomato is creating a data-driven culture with a 150-member engineering team that includes both data scientists and product managers.
So, how do product companies ensure a productive collaboration between these 2 teams for the purpose of developing & delivering an ingenious product?
Through this article, we shall understand the critical role played by product developers and data scientists in product development along with useful tips on ensuring a fruitful collaboration between the two.
Data Science vs Product Development
In simple terms, product development (or software engineering) is defined as the structured approach towards the development and maintenance of a successful software product. Using the Software Development Lifecycle (or SDLC), product development teams use a variety of tools in product designing, database management, or web application to develop a robust solution.
On the other hand, data science is a discipline aimed at effective analysis and management of data in order to derive useful business insights and enable high-quality decision making. A data scientist uses a variety of data analytics and visualization tools to extract a deeper business understanding of data from sources such as social media platforms, business apps, and public data.
Build a robust product.
Analyze and convert business data into useful knowledge.
The end-user requirement, desired product features
Big Data sources including Social media data, business transactions, public data, business app data
Development of new products or features with a structured approach
Informed decision-making process based on the analysed data
Product design, Writing code, Testing
Data modeling, Data algorithms, Machine learning, Business Intelligence
Based on project management frameworks like Design Sprints, Agile, lean or Waterfall methodologies.
Based on process-oriented approaches such as data pattern recognition and data algorithms.
Software developers, Product Managers, Product Testers, UI-UX designers.
Data scientists, Data analysts, Big Data specialists, Data engineers
Core programming skills, Testing tools, Product build tools, Prototyping, Running Design Sprints.
Product domain knowledge, Data mining, Machine learning, fundamentals of AI, Unstructured data processing, Data algorithms.
Despite the obvious differences between the 2 disciplines, product companies stand to gain business benefit through their efficient collaboration and teamwork. In the following sections, we will explore the major challenges faced by both product development and data science functions (from each other) along with practical tips to overcome these challenges.
Common Complaints from Product Managers
Here are the common complaints that Product Managers have about Data science specialists:
i) Product features
Executive decision-makers decide on new product features or product lines based on useful data insights. Product development managers have the rationale that these stakeholders do not actually use the product hence cannot be in any position to determine the value of new features. The data-centric approach is a violation of the more dependable customer-centric approach. Product data that does not offer useful answers to common concerns related to product usage, user experience, and competitive product features are simply not relevant enough.
ii) Event Data or Customer Feedback
According to product managers, no event-based data can compare with the insights gained by directly talking to a user and taking their feedback. Observational event data (taken from the product itself) can be an effective starting point but needs to be combined with user feedback data for finding a good product-market fit.
iii) Cost factor
This relates to product management concerns on whether data sciences can justify its high cost and can effectively improve the customer revenue (and not just make for happier customers). For example, is the latest Machine learning project driving growth in ROI, or did implementing product upsells (using Artificial intelligence) increase customer purchases?
Common Complaints from Data Scientists
Among the reasons for the high failure rate of data analytics projects, data scientists complain of being treated as technical resources rather than being valued for their business relevance. Some companies lack the focus of defining business goals and opportunities that need to be addressed by data science specialists.
While skills like Python programming, data mining, and statistical analysis remain among the leading skills for data scientists, it is equally important to involve them in the business process, learn how work is conducted, and also connect with other company functions.
How to enable collaboration between Product Development and Data Science
Listed below are 4 useful tips on how to bridge the divide between these 2 important functions and encouraging a collaborative environment:
i) Building a data culture in the company
Product managers collaborate efficiently with data science function when business data is part of the daily decision-making process at each and every level. Implementing and building a data culture where data-driven decision-making combines data from across organizational functions ensuring the market success of your software product even before it has been released.
Collaborative frameworks that utilize metrics such as the customer journey map, user persona, and lifetime value can build and guide effective collaboration between product development and data science teams.
ii) Implementing a Data Lake storage repository
The question that product managers face is how much access to production data must be given to data scientists. “Too less access” means that data scientists have to wait long for data inputs, while “too much access” typically result in more-than-necessary access to the production database resulting in production delays.
A data lake enables the sharing of raw business data with data scientists that is separate from the production data. While the product development team can provide the application data, data scientists must ensure that this data is stored in raw format in the data lake. This requires minimum levels of involvement from the production team, while the data sciences team takes the call on the type of data format and data schema to be used.
iii) Data interpretation
Though effective, data interpretation can be a problem for most project managers and even for experienced data scientists. Incorrect data interpretation can occur from incomplete data and even from accurate data.
Hiring a data scientist as part of the product development team can be valuable when it comes to data interpretation. Alternatively, a collaborative team comprising of multiple professionals (with varying skill sets) can be the right answer to effective interpretation.
iv) Building a data science toolbox
Building a data science toolbox with high-level abstractions can lead to a deeper understanding of the tools that data scientists require for data exploration and derive business value.
Companies can include a software engineer in their data science team so as to review existing code and append new functionalities into the toolbox. Software engineers can utilize their knowledge of building modular software to define the data science toolbox requirements gradually over time.
v) Data Science and Agile
The use of Agile methodology and design sprints have been widely adopted by software teams for the purpose of designing and prototyping a product (or product feature). The question is whether Agile can work successfully with data science?
The answer is that yes, Agile can be incorporated into the following aspects of data science:
1) Planning and prioritizing data science-related tasks before each design sprint (that typically lasts around 1-2 weeks).
2) Defining the data science task (or problem) along with setting the deadline (or timeline) to mitigate the problem.
3) Conducting a retrospective session (after every sprint) to reflect on the achievements (or failures) of the data science team.
A collaborative environment comprising of product engineering, Agile specialists, and data science specialists can help in seamless project management and delivery.
While data science focuses on deriving deeper business insights from user data that can lead to a more productive and accurate decision-making process, product development focuses their efforts into building more feature-rich and customer-centric product solutions. Multidisciplinary teams comprising of data analysts, software coders, digital marketing professionals, and UI designers are now critical for making really cool product decisions.
This article presents an outline of how these functional domains can collaborate more efficiently to build software products that bring more market success and revenues to the company.