Home > Blogs > The Power of Google Analytics
With the rapid advancement of digital media and ever increasing marketing tools, it can be a challenge for a website to stand out. Hosting a website today is a common practice, but knowing how to leverage it is a key part of a digital marketing strategy.Whether it’s for personal or professional use, it would be quite useful to find out how one can assess a site’s performance,and potentially channelize that knowledge for growth. The solution to convert a website or blog frommediocreto outstanding is the use ofGoogle Analytics.
Google Analytics, in simple terms,analyses and reports every piece of information your site receives, right from the moment a user clicks on your site till they leave it.This web analytics tool is free and if you aren’t utilizing it yet, you’re missing out!
Setting up your Google Analytics account
This is pretty straightforward and easy to comprehend. After entering your Gmail account, sign up on the Google Analytics webpage which gives you 3 clear steps on how to proceed. After entering your account information and domain name, you get a tracking codewhich needs to be added to every page you want to monitor.
Gaining from Google Analytics
Noticing what doesn’t work for your site is one aspect, but interpreting the data and taking action is how one can effectively benefit from it.
Google Analytics(GA) gives you an accurate description of your audience. One can find out every detail of a user from their demographics, interests, behavior patterns to which pages they liked the most. You can also track what drove them to click on the site, which would be the source, or checking if they left your site without visiting another page which is known as the bounce rate, and evenwhich keywords are frequently typed on the search engines, and so on.
Determining popular pages will reshape the value of your content, directing it to the right audience and doubling your site traffic. Segmenting users according to their personalities and comparing new users with returning visitors is another plus.Focusing on the kind of users your site receives is critical whenbuilding and enhancingthe user experience in the long run.
Google Analyticslets you set up goals, making it easier to figure out what works for your site or how well a campaign has been performing. It also allows you to track conversion which identifies not only when users have taken action on your site, but also know how, why and on what device the conversion took place.Through tagged URLs, you can monitor your campaigns across various channels to determine your Return on Investment.The Google Analytics Integration feature lets you view yourmarketing analysis and web data all in one place.The Google Analytics Custom Report helps fine tune your site and can be sent to others for performance updates.
Overall, there are a number of features on Google Analytics, and if you want see results or make some money online, you need to experiment and understand how visitors interact with your site. More practice, better the results.
Whether it is enrolling into digital marketing training or a digital marketing certification course, or even learning more about Google Analytics via social media, it is crucial for every website to have some tool of analysis.If data is effectively tracked and marketing strategies are creatively deployed,you can develop and maintain a strong online identity.
Home > Blogs > 5 Common Data Science Mistakes and How to Avoid Them
Many a times, mistakes have led to some fantastic discoveries – penicillin, an antibiotic that saves millions of lives, is one such example. This holds true in case of data science as well. Data scientists sometimes end up finding new patterns and trends when their calculations and data arrangements refuse to run smoothly or don’t exactly add up. But not all mistakes lead to new discoveries; in fact, most of them lead to a dead end – which translates into an insight-less or misleading study for a data scientist. The number of variables is high and the final insight expected out of the study can have huge long-lasting implications; hence, a data scientist needs to try and minimise errors in order to maximise the odds of a fruitful study/analysis.
This famous quote by Sherlock Holmes fits perfectly for the role of a data scientist as well
“My name is Sherlock Holmes. It is my business to know what other people don’t know.”
The margin of error for a data scientist is minuscule. Organizations around the globe invest hefty amounts on data scientists and their work and therefore, data scientists can’t possibly afford to make mistakes.
Here are five common data science mistakes and ways to avoid them.
1. Choosing the wrong tool to visualize: The experts of data science focus more on learning technical aspects of the analysis rather than using different visualization tools that can deliver faster results.
As the popular saying goes “A picture is worth a 1000 words.” So it is crucial to choose the right visualization tool in order to monitor exploratory data analysis or to represent the result. Hence, it is advised to get an insight of what the data is all about. Representing data with rich visuals makes it easier to study it and even spot trends.
2. Analysis without a Query: If there is no query, question, concern or objective in mind before the analysis of the data, it's equivalent to running around like a headless chicken and the whole purpose of data science invalidates. Therefore, it is essential for any data scientist to have the project goal before collecting data and a perfect model goal during its analysis.
3. Sampling Bias: Sampling Bias is perhaps the biggest data science mistake that could lead to an incorrect result. Most of the data scientists choose to believe that their selected sample is a good representation of the entire population and conduct the analysis on it. In order to avoid such a mistake, it is important to bifurcate the population into clusters and take the sample from every cluster formed. This method eliminates poorly informed decisions and skewed data models.
4. Ignoring the probabilities: It is a fact that numbers don’t lie but in no fashion does it mean that there is only one possible conclusion after analyzing the numbers and data. After analyzing, data scientists often conclude that in order to achieve Y result, Z action has to be taken. Before concluding anything, the data scientist should shoulder the responsibility of scenario planning and probability theory. More than one possibility almost always exists for a particular query. Data scientists have to keep various possibilities in mind and make informed choices accordingly. These aspects help in delivering correct odds.
5. Ignoring the Historical or Secondary Data: Ignore historical data at your own peril. When a data scientist is involved in collecting primary data, he/she sometimes ignores the secondary or other historical data. Also, in organizations where there is no warehousing in place, the data scientists have to rely completely on new data. However, a lot can be learnt from analysing historical data. It is suggested that you refer and understand previous data too before modelling newly collected data.
James Joyce, the renowned Irish novelist, once said, “Mistakes are the portals of discovery.” If some mistakes could make a business, then there are those mistakes, especially related to Data Science, which hold the potential to break the business.
Lastly, don’t be afraid to try new things due to the risk of failure. It’s our mistakes that we learn from and become better. But it’s important not to make obvious and inconsequential one like the ones mentioned above. As a data scientist, it’s imperative to you track your mistakes, learn from them and strive to avoid them in future. Good luck analysing!
Home > Blogs > Mobile Marketing : an Insight into Its Future Growth and Career Opportunities
The allegiance has truly changed when it comes to social media marketing. The small screen is the king now, and considering the statistics, it would be fair to flip the saying ‘Cash is King’ to ‘King is Cash’ ie. King being Mobile Marketing.
Mobile marketing is here to stay, and along with the bounty of viewership it brings to brands who are looking to make a splash, it also brings with it a great number of exciting career opportunities. In short, if you have a digital marketing strategy, you need to invest in mobile marketing.
India is downloading apps and how! A total of 7.7 billion apps were downloaded in the year 2017, and the number is slated to rise to a staggering 20.1 billion downloads in the year 2021.
Also, when you combine this with the fact that our country will have a whopping 600 million internet users by the end of 2017, it could certainly be said that brands would invest further into the marketing power of the small screen.
Furthermore, it is known that access to the internet via a phone is becoming the norm in most parts of the country. 24.33% of the population had access to the Internet via their phones in 2016, and the number will rise to 37.36% by 2021.
If you’re wondering how all these numbers could be interpreted from a financial point of view, then here is an interesting fact: the total expenditure on mobile marketing in India is predicted to add up to a cool $1.32 billion in the year 2021.
Social = Mobile
Social media usage in India is quite a common phenomenon these days. There were 250.8 million users of social media networks in 2017, but what’s interesting is that the number is going to rise to an incredible 358.2 million in 2021. That’s an incredible 42% increase in usage!
Also, it becomes quite clear that the future of marketing has found a home in social media networks, when you consider the fact that 70% of professional marketers are going to increase spending on social media in next 12 months.
Incredible career opportunities.
If you’re looking for a career in mobile marketing, the future looks bright. With most marketers hopping on to the mobile marketing bandwagon, the job market is flooded with great opportunities.
You can look forward to a promising career as a manager in mobile & app marketing,mobile campaigns,user acquisition and mobile advertisement sales. Also, if communications is your forte, you can avail opportunities as a mobile communication specialist.
As far as salaries are concerned, the packages offered are extremely respectable. If you’re a fresher to the field you can look forward to a salary between 2-2.75 LPA. However, here is where it gets good! Once you have attained a certain level of relevant experience, you can expect a bumper pay rise, as the typical salary range for employees with greater than 7 years of experience is between 15 to 42 LPA.
Therefore, it can be said that the future of mobile marketing is bright indeed, and it would be wise to invest in yourself by enrolling into a form of digital marketing training or digital marketing certification course, and through these you would be able to get a thorough understanding of the dynamics of mobile marketing.
Home > Blogs > The Only Girl in ProLearn’s First Data Science PG Diploma Batch Shares Her Story
From a hesitant individual to a go-getter who fought all the odds, Puja Prakash’s journey to become a data science professional teaches us several inspirational lessons. In an industry brimming with male counterparts, Puja broke all the stereotypes and emerged as a true champion. So what led Puja, an alumna of Manipal ProLearn, choose a PG diploma in data science? Let’s find out.
Puja Prakash accepting her PG Diploma in Data Science degree awarded by Dr. Narayana Sabhahit, Registrar of Manipal University.
Odd woman out!
“During the convocation, I spotted two other girls in the batch only to find out later that they had opted for the part-time Post Graduate Diploma in Data Science course” recalls Puja. However, that did not deter her from continuing on the path that she had chosen for herself. Her passion to emerge as an efficient data science professional kept her determined. In fact, it did not even take her long to break the ice and connect with her fellow batch mates. Puja fondly looks back at the day when they were all strangers and today they are all geared up to be a part of the same bustling industry.
Remember the FRIENDS episode where a chance encounter landed Rachel a job in the fashion industry? Puja experienced a similar situation when she learnt how Machine Learning and Artificial Intelligence study shopper’s behaviour and suggest them products or prompt them to complete their purchase. She then researched about the data science industry and discovered the promising career opportunities in the field.
Puja’s search for a notable corporate trainer in the industry ended when she found about Manipal ProLearn’s Post Graduate Diploma in Data Science. What followed was 11 months of intensive data science training by eminent names in the industry. ProLearn provided her the exposure and the opportunities required to get ahead in the industry and Puja grabbed them with both her hands.
Puja with her classmates as they all look forward to a fruitful career in the data science industry.
Puja left her mark during the campus placements and is presently working with K-Arogia, a data analytics firm, which helps hospitals provide improved healthcare to their patients. Manipal ProLearn is proud of Puja’s achievements and wishes her the best for her future. We are sure her hard-work, dedication and never-say-die attitude will help her achieve several more milestones in her career.
Home > Blogs > How does Data Science Help Predict Cricket Match Outcomes?
Big data and analytics are no longer a stranger to the world of sports. Sports is another field which relies on data science. It is not just the sportspersons sweating it on the field anymore, data science works equally hard. Its goal is to predict match outcomes and it also helps improve game strategies. The German football team and
famous NBA teams are some of the big names, which rely on analytics to improve their game.
Now, the game of cricket not only generates immense thrill, but also a lot of data. Take into account the figures that come from just batting and bowling. Now think about the data that is related to both, the batsman and the bowler. The insights that are derived from big data, provide the players, fans and the broadcasters with enough background information and predictions to make the correct decisions about the team’s performance.
Let us look at how data science predicts cricket matches today.
1. Readily available information
Data science goes a long way in suggesting optimal strategies for a team to win a match. It also provides sufficient information for a franchise to bid on players. Today, there is an influx of cricket statistics-oriented websites and organizations that provide detailed information on cricket.
International Cricket Council (ICC), for example, uses big data to analyse player data and match tournament data. The Board of Control for Cricket in India (BCCI) acquires this service from Sports Mechanics, a strategic consulting, technology and analytics partner for the global sports ecosystem.
2. Keeps cricket fans engaged
The statistical data related to a single batsman and bowler highlights the wickets left, the way the ball was swung, runs scored per deliveries faced, the way each player responded to the delivery, and so on. This data allows fans to understand the game in depth rather than just looking at the match proceedings.
3. Help captains make the right decisions
Data analytics can help solve the uncertainty attached to a bowler or a batsman’s average performance. What’s critical is to know how they will perform in a given circumstance. Collectively, all of this data has the potential to create vast opportunities to analyse and draw meaningful insights, which then help predict or classify future events. This, in turn, helps captains make the right decisions, on and off the field.
4. Machine learning technique WASP predicts final score
A machine learning technique called Winning and Scoring Prediction (WASP) predicts the final score in the first innings and estimates the chasing team’s probability of winning in the second innings. And it works as a scoring predictor in the first innings of a match. For example, WASP may predict based on its calculations that the team will score 278 at the end of the innings.
In the second innings, it works as a winning predictor. For example, if WASP says 67% during a match's second innings, it means that the chasing team has a 67% chance of winning the match.
5. Deeper analysis of match predictions, performances, and patterns
Researchers have used Google trends to refer to data science for a deeper cricket match analysis. Certain Indian analytics companies like Cricket-21, play a huge role
in data analysis for most global teams.
Opportunities in big data and sports analytics are now even greater than usual. The future of machine learning is bright in the world of cricket. Big data has a vital role to play in decision-making for cricket, based on the available data. People don’t chase cricket anymore. In fact, it is the sport that is running behind fans with data.
Home > Blogs > If Superheroes were Data Scientists
The universes of Marvel and DC have given us countless superhero adventures that keep the believers’ faith hanging in the world being saved by the big bad villains. In the real world though, people rely on normal people who do superhero-like work to keep the world moving; data scientists are one such group of ordinary yet extraordinary people. But, we imagined what it would be like if these worlds merged and superheroes used saved the world by powering data science.
Here’s a look at what superheroes would be like in this world:
Batman, Data Scientist:
Batman would make a good data scientist whose role thrives on proper comprehension of patterns and shifts in trend. Given his analytical abilities and his knowledge of patterns and trends in crime, he would make one of the best data scientists out there. His deductive abilities help him predict future calamities and how to prevent them; his resources also allow him to stop the ones that have been set in motion before it reaches a point of no return. He also has access to some of the best gadgets and technologies backed by Artificial Intelligence and Machine Learning. Batman also takes the consequences of his plans into account and works to execute them in a way that’s best for the people of Gotham. His training, technology and his good instincts are what make him an amazing superhero and these are the qualities that make him perfect for the role of a data scientist.
Iron Man, Data Architect:
With his outstanding talent to build standalone systems, Iron Man works closely with other members of the team to centralize and integrate technology effectively into their superpowers. He conducts requirement analysis by understanding the organization’s vulnerabilities, forms a technical architecture followed by the testing and deployment of his weapons, backed majorly by artificial intelligence. He is indeed responsible for the full cycle of a data architecture solution that helps his team achieve their goals faster and safer in any unpredictable situation.
Superman, Data Mining Engineer:
Superman’s knack for noticing the tiny details and to assess their effect on the whole scheme of things is one of the primary reasons that could get him hired as a data mining engineer. He would be able to identify and analyse the minute details in a given data set and can easily apply algorithms that will deny fallacies. His instinct to tell the good from the will come in handy while classifying and grouping data sets. He can separate irrelevant data and channel his intelligence towards the numbers that actually matter in finding the patterns and rescuing the organization from doomsday. Legend has it that he uses his powerful vision and hearing ability to find patterns in data that nobody else could possibly find!
Wonder Woman, Data Engineer:
Wonder Woman, with her strong talent to converse in different languages and multitask would make the perfect data engineer; one who is in charge of creating complex queries and programs for engineering the actual system. Her ability to tackle anything thrown at her can help deal with the pounding demands that data engineers usually face. She can design and code applications in accordance with those demands. Wonder Woman's multilingual capabilities and her enigmatic social presence come into effect when she has to understand and cater to different target groups. Her strong concern and instinct for security will play a great role in securely coding and developing applications and handling secure data. Her superpowers will definitely have her working wonders as a data engineer.
Deadpool, Business Intelligence Analyst
Deadpool is a realist; he isn’t afraid of getting his hands dirty, if he could get to the bottom of what he’s looking for. This is what will make him a great Business Intelligence Analyst, who will dive deep into the business and find realistic techniques to deal with any given scenario. Though he seems like someone who jumps into things undecided and without a plan, Deadpool is actually more of a calculated risk taker. He knows that taking small risks would not cost him much, given his self-healing ability. He is capable of providing and perceiving new perspectives on the data he is given and this helps to make more informed decisions and devise foolproof strategies. It might seem like his lack of avant-garde gadgets makes him the least technical one in the team, but without his in-built powers and ability to take different stands and risks, winning is impossible!
It would be quite thrilling to watch a DC or Marvel movie built on this premise, wouldn’t it? But, until a time like that rolls around, we will have amazing revolutions that data science bring into this world. Across the globe, various fields and functionalities are upgrading their game, thanks to data science; it is one of the hottest jobs and industry to be in. If data is your passion, there’s never been a better time to pursue it. Choose to realize your passion with Manipal ProLearn.
What would your favourite superhero do in the world of data science? Tell us in the comments!
Home > Blogs > Data Science Diaries - Jacob Minz
Data Evangelist, R&D Manager - Synopsys Inc
Over 10 years of experience in the field of Data Science and 2 years in Advanced AI
Previous Experiences – Georgia Tech
- PhD and MS in Electrical and Computer Engineering - Georgia Institute of Technology
- MS, Electrical and Computer Engineering
- BTech, Computer Science & Engineering - Indian Institute of Technology, Kharagpur
Verilog, Algorithms, Software Engineering, Data structures, EDA, Deep Learning
Excerpts from Jacob’s interview with Manipal ProLearn where he explains about his role as a Data Evangelist:
1. Manipal ProLearn: Tell us about your journey, your endeavour with data science. How did it all start?
Jacob: I would say I took a top-down approach to data science. Artificial Intelligence (AI) has always fascinated me, but we began to see tremendous success and innovations in this area only recently. This was possible due to the availability of huge amount of data and easy access to cost-efficient computing resources.
It would be fair to say that it is the data-driven AI that is experiencing a period of spectacular growth. So I started looking deeper into it and then I realized that the whole of data science contains all the knowledge to move gradually from basic data analytics to advanced forms of AI.
2. Manipal ProLearn: You have been in the industry for a long time now, but is there something that you wish you had done differently in your career?
Jacob: I have been primarily associated with the Electronic Design Automation (EDA) industry for more than a decade now. The EDA industry is also starting to consider data-driven approaches such as Big Data Analytics and Machine Learning algorithms to solve some of the difficult problems in the domain. My experience in this domain has been rewarding. However, there is so much more to be done to provide the highest quality products to our customers. We can always do things differently now and in the future to that end.
3. Manipal ProLearn: What has been your biggest challenge so far?
Jacob: Keeping up with the pace of technology. The data science technology has been evolving at a pace more rapidly than most of us can keep track of. Many high quality open-source tools and platforms are being developed, which aim to solve a multitude of practical problems. It is a challenge to figure out the right tool for the right task. Once the tools are locked in to the architecture it becomes difficult to move out of it to a possibly more efficient set of tools. So future-proofing yourself for upcoming technology is a hard task, I would say.
4. Manipal ProLearn: One book or movie that you think every data scientist should watch/read or could draw some inspiration from?
Jacob: I am quite a movie buff and an avid book reader. I would suggest Transcendence, Ex-Machina, and Her to get charged up for considering a career in data science. I would highly recommend the book, “The Master Algorithm”, by Pedro Domingos.
5. Manipal ProLearn: Three qualities or traits that you think are a must-have for data science professionals.
Jacob: I think the primary trait every data science professional should have is hunger for learning and exploration. They should aim to thrive and excel in challenging work situations. They should be willing to learn about new tools, and new approaches from blogs, articles, and research papers to provide the highest quality solutions to their business problems.
In other words, they should be self-driven, and on the other hand also believe in sharing and helping other data scientists. We will grow by democratizing knowledge in this domain, hence there should be a spirit to collaborate more and compete less.
6. Manipal ProLearn: What do you enjoy the most about being a data science professional?
Jacob: I enjoy sharing knowledge through workshops and interaction sessions. I collaborate with other experienced professionals to provide current technology exposure through talks or mini-courses. I also talk to people about their work, and try to learn the best practices from them. Additionally, I mentor young people in this area and help them to sharpen their skills, so they are well equipped to tackle complex problems.
7. Manipal ProLearn: Tell us about any interesting project that you worked on.
Jacob: Most of the work that I have already done or am currently doing is in collaboration with a lot of talented individuals. Currently, I am interested in something, which is loosely being termed as Human Analytics or Cognitive Analytics. Essentially, it is the combination of AI and analytics. We are starting to do some early work on it, and it has been very interesting so far.
8. Manipal ProLearn: The data science industry has grown rapidly over the past few years. What changes have you witnessed closely in this regard?
Jacob: I have closely witnessed the barriers to this technology come down significantly in the last few years. There are many high quality resources and courses on the internet, so anyone with a will and strong determination can learn about this area and start contributing. The push is towards democratization, so there is no significant leverage for companies using closed source tools.
A large number of high quality tools are being developed by brilliant developers in the open source world, which in many cases are performing better than proprietary tools. So there is a significant motivation for big companies as well to support these tools. Overall, the entry barriers have been drastically brought down by the efforts of some fine individuals.
9. Manipal ProLearn: What would you call your most significant accomplishment till date?
Jacob: I see myself as a technology activist and an evangelist. I feel happy when people call back or write to say that they have benefited from our courses and mentoring. If I can help a student pick up relevant problems to solve, and help them tackle those problems, I see the returns as very satisfying.
In fact, my friend and I, trained a student early on, on an area in data science, which became very popular later, helping him get a very prestigious position in a reputed industrial R&D lab.
10. Manipal ProLearn: What are the self-employment opportunities in the data science field?
Jacob: The field of data science is vast and requires a diverse skillset. I think the opportunities to provide knowledge to others through workshops and training sessions is a very viable self-employment channel. There are also opportunities to do data science related projects as a freelancing consultant. Overall, I believe there is a huge knowledge dissemination opportunity in these areas, which also offer good return on investment.
11. Manipal ProLearn: Do you think data science is the next big-thing? If yes, why?
Jacob: Definitely. Andrew Ng, the leading expert of Deep Learning, recently said that AI is the new electricity, and that AI will transform industry after industry. We are already seeing great traction in various application areas such as Healthcare, Retail, Logistics, Manufacturing, Fintech etc. The way to the grand vision of an Artificial Super Intelligence is through data and data sciences.
12. Manipal ProLearn: Name one industry leader who has been an inspiration to you.
Jacob: Jeff Dean, Google Senior Fellow. He is one of the most brilliant engineers of our time who has developed most of the Big Data Analytics related tools within Google. More recently he was involved in the development of Tensorflow, which is currently the most popular open-source tool for Deep Learning. Another person who deserves a mention is Elon Musk, widely regarded as the most astute contemporary technology entrepreneur.
13. Manipal ProLearn: What do you do to ensure your growth as a leader?
Jacob: Listen to people and talk to them, in that order. Having good communication skills is essential for any leader. Leadership is not about control; it is about striving for excellence of the team. It is about earning the respect and trust of your team members and peers. If the members of the team can be motivated to grow individually, and collaboratively, the leader’s success is guaranteed. Also having mentors who guide you helps immensely in this field as it is fast-evolving.
14. Manipal ProLearn: What is your advice to young data scientists?
Jacob: This is a good area to be in right now. There are huge opportunities for career growth. But this is a vast area and there is always something new to learn, so I would advise young data scientists to keep learning and keep sharing their knowledge. It is also essential to be a part of the community of likeminded people to enhance your knowledge and engage in mutually beneficial discussions.
We have started a Facebook community called Indian Deep Learning Initiative to encourage people to start learning advanced data sciences and to connect with experts from Academia and Industry. Also it is imperative that young data scientists realize that this is a very hands-on area. The only way to truly learn is by doing the hard thing and getting your hands dirty with data.
Manipal ProLearn thanks Jacob Minz for taking out his time and giving us valuable insights into the field of data science.