Reskilling to become data scientists

It is estimated that the digital data growth will reach up to 40 zettabytes/45 trillion gigabytes by the end of this decade. That is a massive 50-fold growth in data. So, what is being done with all this data? This is where Data Science comes into play. Data Science is being acclaimed as the sexiest job of the 21st century. Needless to say, this has led to a huge demand for data scientists in varied industries. It has also become a dream job for many IT and non-IT professionals considering to delve into data science career path. So how can professionals who are well-settled in their career transition to data science? Can they become data scientists, analysts or data wranglers? This post is all about this question. Switching careers is a big move, especially if you are in a stable and well-paying job. In this circumstance, changing careers should be a well-thought-out move that ends in moving to a stable and lucrative career. Let’s face it, before making the big move, you have at least million questions that need to be honestly and clearly explained. This post’s goal is to do just that. So, assuming that you do know what data analytics is and what the job role entails, let’s look at who can go on to become data scientists and how to do it. Let’s start with a bunch of questions asked on Quora regarding this: “Has anyone switched their career in Product Management to that of a Data Scientist?” “I have 6+ years of experience in IT consulting, none of them is related to Analytics. But I want to move into analytics. Is there a way to do that? Please advise.” “I am from a finance background with 10 years experience without any background in IT. I have done my MBA in finance. How easy/difficult is to pursue a fresh career in Analytics and Big data? Will there be opportunities for such background on a long term basis?” These questions might sound familiar to you as you too have ample experience in a diverse background and looking to venture into Big Data and Analytics related career path. Demand and supply of data science skills: Data visualization, Data Science and Machine Learning skills are among the most valued technologies according to recruiters. According to Gartner, poor data analysis costs the company an average of $13 million every year. This has led to organizations, both big and small, to scamper to find candidates with the necessary data analytics skills. Consequently, a great time for ambitious data scientists. In another study by MGI and McKinsey’s Business Technology Office, by 2018, the United States could face a shortage of 140,000 to 190,000 people with in-depth analytical skills, as well as 1.5 million managers and analysts with the expertise to analyze Big Data. Now, that we know that there is a demand for data science skill and that there is a lack of supply of said skills, how can we use this situation to our advantage? Upskilling or Re-skilling to meet the demand Data Science skills are one of the most valuable assets in the IT workplace. They not only make an organization more profitable and industrious but also provide greater career advancement opportunities for employees. Here, let’s look at what the employees can do to upskill in a corporate environment. Focus on job-specific skills It’s prudent to focus on skills that employees will be using on a regular basis. To do this, they must be aware of the tasks and process that they will be doing on a daily basis and develop their job-specific upskilling plan using the list as a guide. Learn one skill set at a time One of the most effective ways to deal with upskilling is to learn one skill or skillset at a time, instead of trying to pick up several skills at the same time. First, figure out how you can fill the skill gap by analyzing the organization’s or team’s needs. Once the employee has developed the skill, they can then move onto the next skillset and then another. Learn as a team Upskilling in a corporate environment doesn’t have to be a lonely journey. You can involve your entire team and learn the necessary skills. This would be successful as the entire team is ready to move up to the advanced requirements of the organization. Pursue online courses Online courses offer a lot of flexibility for busy professionals. Taking online classes allows you to work toward your goals at your own pace. You can take online courses to learn a specific skill set in data analytics like business analytics. Make sure the course instructors have hands on experience in the skill you are going to learn. Data Scientist job roles  Data science roles and responsibilities are not restricted to data scientists. In fact, the job roles and tasks are diverse in nature and the skills required for each job title vary significantly. Here’s a list of some of the prominent Data Scientist job titles:

• Data Engineer
• Data Architect
• Data Administrator
• Data Analyst
• Business Analyst
• Data Scientist
• Data/Analytics Manager
• Business Intelligence Manager

Skills required to transition to data science career You still can create a path for the career switch by upskilling with the following skillsets: Technical Skills:

• Math – Linear algebra and probability
• Statistics
• R/SAS
• Python (most common), C/C++ Java, Perl
• Data cleaning and munging
• Data mining
• Data visualization and reporting techniques
• SQL databases
• Machine learning tools and techniques like k-nearest neighbors, random forests, ensemble methods, etc.
• Software skills like distributed computing, algorithms and data structures
• Unstructured data techniques
• Big Data platforms like Hadoop, Hive & Pig
• Cloud tools like Amazon S3

According to a survey by Stack Overflow, skills such as Python, Java, R, Hadoop, Spark, MongoDB and AWS are in short supply. So make sure you are armed with these skills to have an edge over your peers and competitors. Build a good GitHub repo   It is very important for a Data Scientist to have a GitHub profile to host all the codes of the project he/she has undertaken. Potential employers not only see what you have done, how you have coded and how frequently / how long you have been practicing data science. Apply for jobs & internships Now, you need to apply for jobs and internships in data science. Here are some data science/ machine learning job portals:

• Analytics Vidhya job portal
• Datajobs
• Kaggle Job portal
• Internshala

Where are data scientists employed?  The use of data science and thereby data scientists is not restricted to the IT sector. Data scientist are being used even in the world of business management; even though it is still an evolving concept, there are substantial opportunities for data science professionals in this sector. Marketing Data Science is the latest in-demand skill for Marketing. In fact, data scientists and marketing technologists are the hottest jobs on the market. Here, the data scientist’s role is to focus exclusively on improving organizational marketing efficiency. Marketing data scientists analyze internal and external datasets and use the insights on customer behavior to make improvements to marketing tactics. Retail Retailers built reports summarizing customer behavior to have an insight into the behavioral tendencies of customers. More than these summarized reports, we require customer intelligence and predictive analytics to make big changes in the retail. This is where data scientists come into picture. Finance Banks and credit card companies analyze account balances, spending patterns, credit history, employment details, location and other data to determine whether transactions are overboard. Data science is employed to determine the above as well risk management like credit risk stress testing, market exposure analysis, fraud and security, cost reduction and many other aspects of banking and finance. Healthcare Big Data has the potential to help doctors make better decisions, from tailored treatments to preventive healthcare. With numerous researches going on and immense data available through them, now you can merge and analyze data sets from clinical trials, direct observations of other doctors, digital medical records, online patient networks, genomics research etc. Low-cost DNA sequencing and futuristic genomic technologies are changing the usual manner of doing business. Through data science, we now have the ability to map entire DNA sequences and quantify huge volumes of blood components to evaluate one’s health. Conclusion There is no doubt that data scientists and other data science professionals are in high demand and will most likely to continue to be this way. If you are looking to switch career and become a data scientist, then this would be a great time. With the right skill, you can transform your career and give yourself an advantage. Share this on


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