Home > Blogs > 3 Things You Should Do Before You Pursue a Course in Data Science
Data Scientists are in demand across a multitude of industries. A role in the field of Data Science is considered the most ‘Sexiest Job of the 21st Century’, according to Harvard Business Review. Data Science has become pervasive across a range of industries. From Analytics to Machine Learning, the necessity for Data science only continues to grow. As the prospects for Data Science, and with it Data Scientists, grows, there is no better time than now to act upon your passion for it.
The first step towards a well grounded data science career is a good educational founding in the subject. Whether it’s pursuing a full time degree or enrolling in an online course, it is necessary that the decision is based on thorough research and informed choices. There is a lot of information out there that can help you prepare for a course in Data Science.
Here are three things you should do that will ensure that you make the most out of your course and stay well ahead.
1) Speculate and Specialize:
The field of Data Science is so vast that learning everything about it is close to impossible. The learning path is a continuum in this field – one can never fully know everything about Data Science at any given point in time. Thus, the first step you should take before choosing to pursue Data Science is to figure your area of interest. This is not to say that you have to narrow down your options one single stream, rather, figure a preference in the vast field. For instance, if you believe that Machine Learning can be instrumental in solving big issues, then focus on that as your specialization and learn other sections to support your choice. Choose a path that interests and motivates you to keep learning based on your strengths and knowledge. If you can’t seem to decide, keep asking questions, experimenting and finding answers to them and remember to notice what leads you there. Constantly seeking solutions to issues you want to see fixed could lead you to figure the specialization you need. The more curious you get with Data, the more you learn. Once you’ve figured out your entry point into data science, things will begin to fall into place.
2) Basic skill sets add value:
A good Data Science course begins from building a base skill set that will work its way into making you a good Data Scientist. But, possessing a few basic skills or even an interest in them can propel your learning and growth. For instance, with basic statistical and programming skills, performing Data Science operations become much easier. If you have identified your area of passion in Data Science, further research will clearly dictate the basic skills that can go a long way in achieving your aspirations as a Data Scientist. For instance, if Data Engineering is your dream, then a foundation in programming can help widen the scope of your dreams when you begin your course in Data science. A strong founding will not only help you understand the course better, it will help you set higher bars for yourself.
3) Ensure that your course will take you to the next level:
Once you have made the decision to pursue Data Science and have performed your initial research about the field, the next big step is to take your goals to the next level. There are wide variety of Data Science courses available out there – online, offline, full-time and part-time and so on. You need to make sure that what you expect to achieve through your part in the field of Data Science aligns with what the course can offer you to get there. While there are courses that offer specialization in varied aspects of Data Science, very few start from the beginning and prepare you for a wholesome career and personal development, such as the course in Data Science from Manipal’s ProLearn. Ensure that you do thorough groundwork and choose the Data Science education that befits your goals and aspirations.
A flair for coding, basic knowledge of some programming language and a good understanding of databases can take you a long way in your Data Science training. The best way to prepare for a career in Data Science is to develop the passion that would be much larger than getting a job; a passion to solve a bigger world issue. Make the right choice with your course and that will be the first step to realizing your passion.
What are some of the other things that one needs to do/ know before pursuing a Data Science course? Tell us in the comments!
Home > Blogs > Did Big Data Fail Nokia?
Data science has made its way into many aspects of our lives and businesses. Organizations rely on data for every major business decision. A large section of the world believes that Big Data has the answers to every problem that the mankind faces and might face in the future. The success stories of organizations and businesses that use Big Data analytics are filling up media content every day.
With the impact of data science being undeniable, companies and industries, irrespective of their size, are investing millions in Big Data for huge profit margins in return. But, how far can Big Data alone take them?
Tricia Wang, a global technology ethnographer and co-founder of Constellate Data, shared her insights about relying solely on quantifiable Big Data in a TEDxCambridge Talk. Using Nokia’s downfall for not getting on the smartphone trend because they didn’t see quantifiable proof that it was a trend which was here to stay, Ms. Wang emphasized on the need for human insights to help Big Data analytics. Her Ted Talk is an expansive insight into dealing with the constantly changing and intensifying world of data and innovation.
What’s the challenge with Big Data?
The Gartner Survey result shows that companies that invest in Big Data are on the rise, but most of them are not getting any potential benefits from using it. In her speech, Tricia Wang points out that73% of the Big Data projects that companies invest in, do not turn out to be profitable. When you have a vast amount of data, it may seem difficult to quantify the information in a way that enables the management to take better decisions. When statistical analysis of data is considered as an isolated project not connected with the company’s concerns, there might be difficulties in interpreting the real challenge.
When Nokia was the ‘super star’ of mobile phone industry with their Symbian models, the company failed to see the obvious shift of interest in customers towards smartphones. Nokia banked heavily on what quantifiable data showed them at that point in time, ignoring a more wholesome perspective and strong insights as to how the consumer base could trend.
When organizations begin to ignore the more complex features such as human intelligence to focus only on what is quantifiable, they begin to lose what could potentially make them unique and successful. The tendency to trust the quantified numbers more than anything else is natural, but dangerous.
Mere numbers can’t tell everything:
The need to keep up with market trends constantly forces transformation upon organizations. And these transformations are largely predicted and determined by Big Data. To reap quick and compelling development, Nokia too, invested in Big Data and focused on it from a single perspective. But how far can data alone take the company? What can it do for the business without the human expertise that is required in modeling information for decision making? Not much.
The most brilliant and unfathomable visions unfolded by Big Data have only done so using the human insights that brought out the data, in the first place. This is where ‘Thick Data’ comes into play.
Thick Data and why it will help:
Thick data is formed by using ethnographic user research methods that observe and analyze human emotions and their perceptions. This information is crucial to the very existence of businesses. The stories and lives of prospective consumers; of the products/services manufactured by the business are valuable. Such information cannot be gathered and analyzed by machines with the click of a button. It is achieved through constant research and by understanding ‘WHY’ exactly something is happening and ‘WHAT’ exactly needs to be done.
If Nokia had attempted to incorporate Ms. Wang’s insight even in a small way, instead of merely looking at the margin of difference between their Big Data sample and hers, they might have survived to fight bigger battles. This is proof that 100 people could be a strong suggestion as to where the market is moving next, if the thick data is substantial.
One of the most successful integrations of Thick Data and Big Data was when Netflix upgraded its algorithmic feature to incorporate users’ tendency to binge-watch shows. It not only made suggestions as to what the user would like, it went one step ahead, understood its consumers beyond just the numerical data and made suggestions that kept them binge-watching. A hop, skip and a jump away, Netflix is now more of a verb and an emotion than just a means of entertainment.
Thick Data + Big Data = Best Solution
It is suicidal to refuse change by not implementing Big Data for the betterment of your business. But if data is not used in a way that will address issues beyond what meets the eye, the destiny of your business will be the same as that of Nokia. The ideal approach towards this issue is integrating Big Data with Thick Data.
Thick Data can be used to fill the gaps left by mere numbers generated from statistical methods. The role of an ethnographer in such an industry is absolutely essential. The truth is that ethnographers with impressive analysis skills are hard to find.
Big Data scientists need to understand the importance of ethnographers and user researchers for synthesizing their numbers into actual business goals based on the company’s vision. It is also important to ask the right questions when the data is fed to the system. It must be very clear to the management why they are using Big Data analytics for their business. When Thick Data is clubbed with social media analytics and customer analytics, companies can make near-accurate predictions by simply understanding the context.
What you can learn from Nokia:
It is time businesses realize that merely investing in Big Data will not guarantee the change and upgrade they want to see. They must understand that without the touch of the human interpretation and information to set context; without the stories that give them issues to solve, Big Data will merely be data sets that give out ambiguous solutions.
It is necessary to take precaution and not to fall prey to the quantification bias, because Data has pervaded a multitude of industries. Forget commercial businesses, it is frightening to see that quantification bias could happen in the fields of Healthcare and National Security, where one unwholesome interpretation could turn things for the absolute, irreparable worst. It is distinctly possible for our world to slip into another world war just because some data was not analyzed correctly. So it is important to remember, Big Data without Thick Data is just a solution to an unasked or wrong question.
What is your take on Thick Data + Big Data integration? Do you think Nokia would have continued being the undisputed king of the market had the company banked on this integration? Tell us via comments!
Home > Blogs > The Data Behind Amazon’s Prime Day Sale
By now a lot of us know that E-commerves thrives because of data science. From storing purchase data to generating predictive data customer loyalty and engagement for E-commerce largely depends on data science. The growing trends in machine learning mixed with the right amount of analytics is the secret behind their recipe for harvesting the large profits. The world’s largest online retailer Amazon was one of the pioneers in using data to their benefit.
Amazon has the ideal route to maximize its sales using data science. One of the E-commerce giant’s key features is its premium experience called Amazon Prime and it is marketed as the best online experience any retailer could provide. As of 2017, Amazon has a prime membership of 80 million and it constantly strives to drive this number up. Since 2015, it has been spiking the Prime subscription and their sales exponentially with a 24-hour event called ‘Prime Day Sale’. Similar in concept to the American Black Friday sale, Prime Day offers unbelievable deals on a wide range of their products and services and these deals are available till products last.
How does data drive sales on Prime day?
So how does Amazon use data science as a tool to get more consumers into buying things from their website on Prime day? The statistical analysis of the data squeezed from the previous sales patterns, which depends on numerous factors like location, category and products is the key to recreating this magic every year, and it keeps getting better each time.
Data that blew the roof:
After the 2015 Prime Day sale retailers around the world were mind blown; it was the largest Amazon device sale worldwide and had surpassed the record for user traffic set by Cyber Monday and Black Friday by 19% and 23% respectively. The amazement at the figures was only until the 2016 edition arrived. It sold twice the number of Television sets, headphones and laptops. Sales climbed rapidly across the US and European market. If this trend continues, Data predicts that sales could hit between $800 million and $960 million for the 2017 edition, with it being launched in thirteen new countries.
Insights that improved the sales:
The very first Prime Day sale was noted as being bigger than ‘Black Friday 2014’ which had created a record for the amount of sales and traffic. Amazon sold 398 items per second and its total revenue increase rocketed by 90% in the USA, despite the enormous deals and discounts. Television show box sets, Electronic gadgets such as laptops and headphones sold the most. Using this data, the 2016 Prime day drove its sale up by 60% than the previous year, selling 636 items per second. Deals on televisions, groceries, clothing and other fashion products were promoted better. Amazon used a special algorithm that let it determine which deals should be released as teasers and promotions to keep the users engaged. Data also reflected technical issues such as page rendering delay and the loss that resulted in; 1% loss was incurred for every 1 second delay in page load and in the 2016 Prime Day sale, Amazon fixed the glitch, hastening its page load by 5 seconds.
Prime Day in India:
In 2017, Amazon is launching their flagship event, Prime day in India too, aiming at acquiring a firm grip on sales in the country against the domestic competitor Flipkart. The fact that Prime loyalty service gained one of every three orders for itself in the Great Indian Sale platform gives solid expectations for Amazon for the Prime day. The Indian consumers are presented with overwhelming deals on areas like gardening, electronics, beauty, interior decoration and books for this 30 hours sale. Their data on sales shows that by signing up to the prime services, customers tend to spend more than twice the amount as non-Prime members. With these data, Amazon can construct the perfect marketing plan to tackle the Indian market by predicting the client behavioral patterns.
2017 Prime Day predictions:
Data predicts that deals on Amazon’s brand electronic devices such as Fire stick, Kindle and Echo will continue to ace the top-rated and sought after. Prime membership is predicted to skyrocket before and after the sale day. The overall sales could be between $800 million and $960 million. In all, the Prime Day sales this year has a lot of data scientists enthused about what is to come and how it will be.
Data is the driving force behind a lot of fascinating things in the world and sales are no exception to it. It is interesting to see how data has driven one of the globe’s biggest online sales towards progressive success. This is only one example of the fact that all it takes to change the world and keep it moving is a smart algorithm!
What are some of your predictions for the 2017 Prime Day sale? Tell us in the comments.
Home > Blogs > Internet of Things in the Automotive Industry
One of the regularly used and heard jargons in the field of technology, now, is Internet of Things (IOT). It predicts that everyday objects will soon all be connected to the internet - from desktop computers to toasters and fridges. The concept of connectivity has grown from a privilege to a basic necessity and this being reflected across multiple industries.
What started with Bluetooth headsets for cell phones has evolved to air conditioning systems that can be monitored and controlled from smart phones. One of the prominent industries that have kept up with these connectivity requirements is the Automotive Industry. Cars have gone from having cassette players to touch screen media dashboards, from check engine lights to blind spot detection.
We are now only at the brink of the IOT era - with constant connectivity embedded into cars and the data pouring in from multiple sensors, the possibilities are endless. Here’s a look at how the automotive industry plans to incorporate IoT into it:
Vehicle Status and Safety
In the future, the Check Engine light will no longer cut it. With sensors gathering and transmitting data from multiple areas around the car, apps could remind you to refuel and even navigate you to the nearest gas station. IoT could detect and correct human mistakes, provide much more detailed diagnostics about failures and accidents in real time and request help when needed.
It is already possible to connect phones and stream music, making radio and CD players superfluous. With IoT, phones will no longer need to be the tethering point; the car could connect directly to the streaming app of your choice and load what was playing last.
Inbuilt GPS systems are already getting outdated in comparison to Google Maps and such. In the future, your car could connect to multiple sources of data about traffic, the weather, the condition of your car and determine the safest, quickest path to your destination. Ideally by distributing traffic more evenly across available routes, congestion and accident rates could also be lowered.
Most cars today are able to detect when they are in trouble and bring attention to it, but as we all know, these systems can easily be set off by accident or be difficult to reset even when accurately alerting. Cars in the IOT era will be able to act with the level of security and ease of use that most phones have today. It would be possible ensure that no one but specific people can unlock the car. If the car still gets stolen, it would be easy to track it from your phone.
The most exciting prospect of all is that when each of these pieces is perfected and brought together, the need for a driver could be eliminated completely. Given the amount of time we all seem to spend in traffic, freeing up the driver’s attention and eliminating human error from the process of driving, could be the most beneficial results of the IoT era. It will be interesting to see where the industry goes with the constant evolution of Data Science and IoT.
What are other cool features you would like to see incorporated into the cars of the future? Tell us in the comments!
Home > Blogs > Genealogy in Data Science
One of the most fascinating and constantly advancing aspects of Data is the way it is stored. In the last few years, data storage has gone from megabytes to terabytes. The growth of cloud storage has also been unwavering in the field of data storage. Regardless of the choice of storage, be it DropBox or Google Drive, files no longer need to live on just the hardware.
However, one thing that hasn’t changed is how those files are ultimately stored. Whether the source is cloud or hardware, it’s stored electronically. The collection of hard drives is growing, and server farms are becoming ubiquitous. In this era of exploding Big Data, continuing data storage the way we do today, we will run out of space and power. Not to mention the risk of losing data because of hardware degradation over time.
The most revolutionary data storage method yet:
Luckily, specialized Big Data Analytics courses and increased interest in Data Science and Data Science training has uncovered a solution - encoding data into DNA. Essentially, DNA is a form of storing information about living things - how they grow, how they look, and what their personalities are like. What if we could use this encoding method to, say, store a movie instead? That’s exactly what scientists have done, opening up a whole new world of opportunities for Data Science and Data Analytics Techniques.
Scientists have translated combinations of the 1s and 0s that encode data electronically into corresponding nucleotide bases, A, G, C, and T. For example 00=A, 01=G, 10=C, and 11=T. Once these translations are complete, they can be densely packed into DNA, with plenty of redundancy to ensure that even if a few pieces are lost or cannot be decoded, there is enough information left to piece together the whole.
Benefits of DNAs being used for data storage:
Better data compression
The sheer density of storage means that we can compress data one thousand times more efficiently than methods used today. The best part is that this density can be achieved without overheating hardware, which is the current limiting factor.
Error-free storage and retrieval
It’s easy to store, maintain, and retrieve multiple copies of information without errors using DNA encoding. The encoding algorithm used today is similar to how videos are streamed. So even if a few pieces of information are missing, they can be easily detected and replaced.
DNA doesn’t degrade easily; at room temperature, DNA can be stored for up to four thousand years, and can last even more millennia if stored in a cold, dark place. Because DNA is so essential for evolution, its relevance will not diminish easily. Humans, thousands of years in the future, can still read DNA the way it’s encoded now.
Currently, the only drawback is that DNA storage is still a few years away from being a viable option. It’s still slow to read from DNA for it to be of everyday use, and is still too expensive to be used in industry. Hopefully, the next generation, through advanced Data Science training and with more Big Data generated, will be able to laugh at our server farms while they decide which movie to watch from their strand of DNA.
What are other fascinating data storage methods that you know of? Tell us in the comments!
Home > Blogs > Artificial Intelligence in Law
Compared to other industries, the legal sector has been slow to change and thus slower to embrace the benefits of Artificial Intelligence (AI). However, today many legal firms have adopted AI to save costs and ensure lawyers focus more on tasks that add value. The days of hiring paralegals to scan documents or sort out tickets are reducing with law firms quickly implementing Artificial Intelligence systems to automate some of these menial jobs.
What is Artificial Intelligence?
AI creates technological systems and software to perform tasks similar to that of human intelligence. In simple words, AI develops ways of making computers think like humans. It is a science and technology based on Computer Science, Mathematics, Linguistics, Biology, Psychology and Engineering. The Father of Artificial Intelligence, John McCarthy quotes it as, “The science and engineering of making intelligent machines, especially intelligent computer programs.”
Artificial Intelligence in Law
Law is more or less a framework of rules which sometimes demands the need for computer programming. So in many ways the applications of Artificial Intelligence perfectly suits the legal industry.
Initially, AI looked for keywords in megabytes of Data. But it has come a long way since then. Through Predictive Coding and new learning abilities, AI can quickly sift through records by context and offer important information. Artificial Intelligence primarily deals with enabling lawyers to focus on creating legal content (For eg. planning a legal argument) rather than manually completing routine work (For eg. document drafting).
The current applications of AI in legal work include preparing contracts, conducting litigation analysis, legal operation analysis, due diligence, legal research along with tremendous other possibilities.
Apart from AI handling mundane work at a faster pace, law firms have gone one step ahead to take full advantage of Artificial Intelligence. Here are two ways to find out how:-
Law firms have started using chatbots to increase user engagement by steering potential clients to useful information about the firm. The chatbot’s simple to use chat interface can learn about the requirements of potential clients, offer legal advice or answer initial queries. Through the bot’s conversation history, lawyers are already aware of client’s background. Law firms need to include robust Natural Language Processing (NLP) for potential clients who are unfamiliar with correct terminology. Chatbots have become increasingly popular to upsurge business for law firms.
Started by a Stanford student, DoNotPay is a chatbot that uses Artificial Intelligence to help people fight parking tickets and legally aid refuges seeking asylum.
2) Machine Learning
Law firms with Big Data are resorting to Machine Learning to review data quickly and as effectively as possible without any initial training. This prevents firms from spending heavily on research. Machine learning ensures impartiality if Judges make all their final decisions in writing. For instance in US, law firms that adopt Machine Learning quickly scan tons of data to detect any wrong judgment.
The future of AI in Law
Artificial Intelligence is changing the entire dynamic of the legal department for thousands of companies globally by ensuring more cost effective and efficient lawyering. In the wake of ROSS, the world’s first AI lawyer hired by a US firm, there are theories stating lawyers will be replaced by robots in the years to come. While AI technologies will enhance the work of a lawyer, experts believe that it is still a long way from replicating the analytical/problem solving skills and intuition required to be a good lawyer.
Law firms that continue to use AI will bring greater savings than ever before and those that fail to embrace AI will get left behind.
Home > Blogs > 5 Hadoop Mistakes and How to Avoid Them
In the past, the collection and storage of all the big data by business organizations came with a lot of challenges but new technologies such as Hadoop have clearly eased many of these problems. Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It is easy-to-use, inexpensive, flexible, and powerful enough to help process large volumes of data.
Hadoop, for all its strengths, is not without its difficulties. Business needs specialized skills, data integration, and budget to factor into planning and implementation. Even when this happens, a large percentage of Hadoop implementations fail. This is because of the common big Hadoop mistakes made by data scientists.
1. Security breaches: High-profile data breaches have motivated most enterprises’ IT teams to prioritize protecting sensitive data. The user accidentally shares the card and bank details, and personally identifiable information about the clients, customers or the employees. If the user considers using big data, it’s important to keep in mind the security structure while processing sensitive data about the customers and partners.
How to avoid:
- Addressing each of the security solutions before deploying a big data project.
- Planning ahead to decide who will be benefited from the investment and how it is going to impact the infrastructure.
2. Migrating everything before a plan: Migrating everything into Hadoop without a clear strategy always results in long-term issues and expensive ongoing maintenance. With first-time Hadoop implementations, you can expect a lot of error messages and a steep learning curve.
How to avoid:
- By considering every phase of the process (from data ingestion to transformation) beforehand.
- Following a holistic approach and starting with smaller test cases.
3. Treating Hadoop as a regular database: One of the worst mistakes anyone can make is to treat the data lake on Hadoop as a regular database like in Oracle, HP Vertica, or a Teradata database. The structure of Hadoop is totally different and wasn’t designed to store anything you’d normally put on Dropbox or Google Drive.
How to avoid:
- By following a simple rule of thumb: if it can fit on your desktop or laptop, it probably doesn’t belong on Hadoop.
- Taking proper steps up front, in order to ingest data to get a working data lake, thus, avoiding data swamp.
4. Buying cheap server hardware: Because Hadoop is often talked about as being low cost due to the free open-source framework; some businesses think that buying inexpensive hardware will do the trick. This is far from the truth as it always results in frequent node failures and other time-related losses.
How to avoid:
- By buying quality server hardware, even if it is a bit expensive
- Consistently monitoring and maintaining the quality of the server hardware.
5. To assume that rational database skill sets are transferable to Hadoop: Hadoop is a distributed file system, not a traditional relational database (RDBMS). Thinking that you can migrate all your relational data and manage it in Hadoop the same way will result in nothing but problems. If your current team lacks Hadoop skills, it would be best to train existing people, by the means of big data Hadoop training, rather than hiring new talent.
How to avoid:
- By using new software, along with the right combination of people, agility, and functionality to make big data Hadoop successful.
- By automating some of the more routine and repetitive aspects of data ingestion and preparation using tools available in the market.
Hadoop is one of data science’s big revolutions and effective use of it has proven beneficial to a number of different industries. A number of premium institutions are offering big data hadoop training opportunities. A big data hadoop certification is not only a skill set boost, but a career boost too.
What are some common Hadoop mistakes you’ve come across? Tell us in the comments!
Home > Blogs > The Three Pillars of IT Architecture
IT architecture has been the key practice undertaken by an organization for achieving organization goals for decades. Many companies are brilliant at developing strategies but repeatedly see them come to nothing because they can’t cope with the multiple changes necessary to see them through.
When business structures need to change at the same time as new technology is being implemented and information processing is changing, it’s easy to become overwhelmed. IT architecture is designed to address this problem and IT architects are trained experts on finding robust solutions.
IT architecture, to be successful requires support from the organization and stands on the three pillars of IT architecture:
- Organizational policy: Organizational policy is a set of principles, rules, and guidelines formulated or adopted by an organization to reach its long-term goals. It is the main purpose of why the organization has been established. It is a premeditated rule set by a business to guide organizational direction, employees and business decisions, and to regulate, direct and control actions and conduct. It is the direct connection between the organization’s vision and its daily operation.
- IT strategy: For a better understanding of what is necessary to achieve the organizational goals and how and where the resources of the organization should be invested, IT strategy is one of the most important pillars of the IT architecture. It allows the IT architect to plan ahead according to the organizational need.
- Programs: Having a solid technical architecture in an organization is crucial for the success of the IT architecture, especially for a complex system and subsystems. For a solid technical architecture, the organization requires different software programs and technical tools like Telelogic system architect, Microsoft Office visio, casewise modeler, dragon1, powerpoint etc.
IT architecture heavily relies on the organizational policy as, without organizational rules, the IT architects won't know the goals they are trying to achieve. Enterprise Architects concern themselves with the strategic implementation of a company’s business goals. Every project the IT architects undertake is directed towards achieving the goals specified in the organizational policy. This makes the policy as the most important pillar for enterprise architecture management.
IT strategy provides the IT architecture with the opportunity to spread the cost over multiple budgets, rather than having to make the investment with a distressed need. This way the IT architect can carefully plan when and what to spend the IT budget on. This approach also supports suitable business growth, as it provides the organization with IT infrastructure that’s up to the job and can handle growth fluctuations better.
These programs allow an enterprise architect to take efficient and quick decisions as they can create models, viewpoints, views, and visualizations of the enterprise architecture for different stakeholders of an organization and generate various reports on it.
Programs are the actual tools and softwares that get the work done in an organization and allows the IT architect to find solutions by completing different projects and reports to link the current state of the organization to its future state thus, achieving organization goals.
Home > Blogs > Know When to Specialize in IT
With the ever-developing nature of work roles, as well as the increasing skill requirements of the employee, IT professionals are constantly under duress to improve their business training. There is constant pressure to gain qualifications that lead to all–round competency and expertise.
A career in the IT industry entails roles that are perpetually changing. These roles also increase in complexity quite often. So, it is imperative that an employee has the skills required for his/her role, apart from in-depth knowledge of the field.
The time is right!
A specialized course is ideal to gain advanced knowledge and skills. The appropriate time to take this step is after gaining comprehensive knowledge about the inner workings of the organization, and the intricacies of the role. Ideally, the right time is after you have spent a couple of years in the industry.
While entering an organizational role in the IT industry, it is pertinent to evaluate your skills and analyze the areas you may be lacking in proficiency. Once this is done, you must draft a clear plan entailing the areas that need improvement. It should also involve a deep analysis of the advancement of your role in the organization in the next five years.
The third year of your professional life would be ideal to take up a specialized course. By this time, there is a definite awareness about the inner workings of organizational roles, the discrepancies and the areas that need all-round improvement.
Response to Industry Demand
With a job market that is continually evolving, new opportunities arise everyday due to advanced technologies.
Years ago, many digitally inclined job positions did not exist. Today, employers looking out for marketing professionals place an emphasis on digital expertise. This creates the need for specialized courses, whether it is to advance in a particular industry or in the case of a career change.
To Concentrate on Career Advancement
It is always a possibility with any career change that there will be a requirement of skills that weren’t needed in your old job. This is where a specialized degree can help fill in the gaps.
A product manager for example, can improve his knowledge and grasp of the organizational business process by either delving into a specialized product management course or a business process management course. Each of these would help gain business acumen and offer additional skills which invariably boost morale; providing the candidate with the confidence to advance in his career.
Garnering new skills through a specialized course can largely increase and improve feelings of productivity leading to valuable and improved contribution in the organization. Therefore to add to your skillset, you can take up Bootcamp courses or it certification training programs
For example, in the field of computer science, taking up specialized courses such as language programming, cryptography and image processing can not only aid in honing skills required for the present role, but also catapult your career substantially. Specifically, an Android app development course will provide an employee with all the skills necessary to be adept at the position of an android app developer. With added knowledge and proficiency, comes confidence and accuracy. This can be achieved through a specialized course during your career. Therefore, in the IT sector, proficiency of skills coupled with a great work ethic is the ideal formula for success, and IT oriented certification courses can surely help you out in this field.
Home > Blogs > Why a Product Manager Should Think Like a CEO?
Operations and mode of operations of a functional organisation have diversified because of a series of changes in industry and market environment. The roles and scope of contribution that an employee can make in an organisation has also undergone sea-change. It is not just the leadership of a single person that brings success to the company but a perfect business analysis and allocation of optimum human resource to projects. So let’s take a look at the factors on which depends the fate of a company.
Technology is the oxygen in a business environment. With greater IT Education and Training coupled with increasing research on architectural technology, systems have started influencing processes and the core product is a technology itself.
Importance of a Product Manager
As the core of the matter, the product is heavy-weight element which can decide the fate of an organisation. Ensuring that their design and application is consumer-friendly ensures that your organisation gains and there is no limit to this success. Hence Product Managers have come to play the key role in earning revenue for a company.
Correspondingly, it can be said that product managers never get bored with their job. This is so because it’s never a typical day in the office for them. They always come across new challenges by working across different vertices of an organisation, and also get the chance to use their diverse skillset to solve various problems.
Moreover, they are mini-CEOs, since the product responsibility lies completely with them. So whether it is product strategy, innovation or delivery, all the communication and ideas run through them.
Product Managers bear the core responsibility for earning profit for the organisation by chalking out an effective strategy for the concerned firm, its roadmap and features definition for the concerned product or product line. Others, in the organisation, follow the laid path. Thus, the onus lies on Product Manager(s). He or she is practically the ultimate authority for the products. Hence, a Product Manager is expected to and should think like the CEO of the company. Let’s discuss the reasons in detail.
- Central Leadership: It is the product managers who decide the core strategy which is followed by others in the organisation. They understand requirements of the market, define and then prioritize products and designs with different teams for reaching the ultimate goal. Many corporate training programs are now structured to groom managers for the authorities and responsibilities of leadership.
- Consumerisation of IT: This has opened a floodgate of opportunity for organisations to earn the most. Product managers have an in-depth understanding of which technology or group of technologies to apply for relevant products and services. Their knowledge and expertise helps in making the correct decisions and boosting efficiency.
- Complexity of business ecosystem: The business ecosystem has grown more complex with time by the inclusion of elements like technology, evolution of economies around the world, localisation and globalisation, industries mergers for certain products and services which brings in more elements and further increase the complexity of the prevailing ecosystem. It is where a Product Manager steps in to simplify the work with categorisation, simplification and effective leadership. These are far more than the prescribed roles for the job.
A Product Manager offers central leadership and his role is directly linked to the operations of an organisation and profits. Hence, it is imperative that a Product Manager thinks like the CEO. It is time that more faculties look into Product Management Training for freshers.