Tanmoy Ray en Professions, Workers, Careers, IT - Information Technology, Students Career Adviser & Admission Consultant | Blogger & Digital Marketer • Stoodnt Inc 11/1/2018 · 5 min de lectura · 1,3K

How to Build a Career in Data Science and Big Data Analytics - Tips, Resources, and Career Advice

How to Build a Career in Data Science and Big Data Analytics - Tips, Resources, and Career Advice

Top 6 Ways to Make a Career in Data Science and Big Data Analytics

1. Choose the Right Education Background

In order to become a data scientist, you don’t necessarily need to pursue Bachelors in Data Science. In fact, that’s not recommended at all. It’s actually a bad idea to go for such a niche discipline at the undergraduate level. You could certainly go for a Bachelor degree in Computer Science, Engineering, Economics, Mathematics, Statistics, Actuarial Science, Finance, or Natural Sciences (Physics, Chemistry, or Biology). Even Liberal Arts (including Social Sciences) could be very handy as well at the undergraduate level.

“A data scientist is a better statistician and economist than most programmers, a better programmer and economist than most statisticians, and a better statistician and programmer than most economists.” 
Big Data Made Simple

2. Acquire Strong Technical Skills

· Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.

· Experience with common data science toolkits, such as R, Python, Weka, NumPy, MatLab, etc

· Experience with data visualization tools, such as D3.js, GGplot, etc

· Proficiency in using query languages such as SQL, Hive, Pig

· Experience with NoSQL databases, such as MongoDB, Cassandra, HBase

· Excellent applied statistics skills, such as distributions, statistical testing, regression, etc.

3. Gather Real World Experience

18% of the data scientists reached the top of the data science ladder after completing an internship. So, if you have a Master’s, it is a great idea to look for an internship in the field, rather than going for a Ph.D. straightaway.

In the real world, it would be rare to get employed as a data scientist, right after college. Most of the folks start as analysts (data analyst, BI analyst, business analyst included), scholars, interns, IT specialists, software engineers, and consultants. Only 2% of the folks got their first job as a data scientist. You could also work on a project in your free time, and showcase the data visualization on LinkedIn and other social media platforms. That will help your personal branding.

4. Take Online Classes and Prepare Yourself

I have been advocating taking online courses for a long time. I have been doing it myself as well, and there is a clear benefit. In order to get a data scientist job, and even for getting admission for an MS Data Science program, self-preparation is very important.

5. Work on Your Soft Skills

Of course, data science is about Mathematics, Programming, and Technology. But, in today’s data-driven workplace, soft skills like excellent communication skills, intellectual curiosity, creativity, cultural intelligence, emotional intelligence, and strong business acumen are equally important.

6. Spend Time on Interview Prep

Don’t ignore the interview preparation. Irrespective of your qualifications and technical prowess, an interviewer can throw you off with a set of questions that you didn’t expect. For a data science interview, an interviewer will ask questions spanning a wide range of topics, requiring strong technical knowledge, the ability of handle pressure, ability to think out of the box, and communications skills. Your statistics, industry knowledge, programming, and data modeling skills will be put to the test through a variety of questions and question styles – intentionally designed to keep you on your feet and force you to demonstrate how you operate under pressure. Preparation is a major key to success when you are looking for a data scientist job. Here is a curated list of data science interview questions.

Data Science vs Data Analytics

There there is a significant overlap between the two roles. A data scientist always needs to write custom queries and a data analyst may need to build a decision-making module either by simple rules or applying machine learning principles.

Data Scientists work towards estimating the unknown, building statistical models, and conducting casual experiments to figure out the root cause of an observed phenomenon and/or predict the future incidents. In contrast, data analysts (or business analysts) are looking at the known, i.e. historical data, from new perspectives. They will write custom queries to answer complex business questions, incremental new data acquisition and addressing data quality issues, such as data gaps or biases in data acquisition.

In the business or commercial context, a data scientist will identify new products or features that come from unlocking the value of data. A business (or data) analyst will work on conceiving and implementing new metrics on capturing previously poorly understood parts of the business or product. Check out the following video.

Data science and business analytics are very inter-related. But, don’t assume that an MS in Business Analytics program will make you a data scientist. MS in Business Analytics program is NOT aimed to create Data Scientists. The real objective of the Masters in Business/Data Analytics program is to produce graduates who can solve Business Problems by implementing Analytics. 

For readers who are keen on being a data scientist, an MS or Ph.D. in Data Science (or even Computer Science and related fields) will make more sense.

Higher Education Can Boost Your Big Data & Analytics Career

It's not a mandate to go for a specialist degree like Masters in Data Science or Big Data Analytics. However, it could be advantageous if you fast track your career. However, please be advised that most of these new-age specialist programs need to be done from good universities - not necessarily top-ranked ones (rankings could be deceptive). 

Don't Follow the Big Data & Data Science Craze Blindly

All the business organizations, including management consulting firms, banks & financial services, and tech companies, are indeed looking for big data talent. In fact, a recent IDC forecast shows that 2018 will see a six-time growth in the big data & analytics job market. Social media platforms are getting bombarded with blog posts and videos on data science, big data, and analytics. With a lot of hullabaloo going around, students and professionals are going crazy after data science and business analytics programs. 

All the top and elite universities are re-structuring their program-curriculums. But, on the dark side, all these fuelled the mushrooming of the specialized Master’s programs in business analytics and data science all around the world. Don’t forget about online courses that pop up on your screen every time you check websites or Facebook feed.

The businesses might need around one million data scientists by 2018. As per IBM predictions, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 by 2020. But, businesses do have options for training employees on the job. While companies like IBM or Deloitte are extensively collaborating with business schools to design MBA & MS programs, companies like Booz Allen Hamilton and Qlik are focusing on creating data scientists in-house.

Since data science combines analytics with business acumen, much can be gained by targeting employees with domain expertise, in addition to technical prowess. For many organizations, the best use case for data science to add business value remains marketing and technology platforms with high activity levels.

Rather than worry too much about having too few data scientists, we should worry about whether our senior managers are numerate enough and whether we have enough critical thinking skills across the whole workforce.
Clive Holtham, Cass Business School (Source: Financial Times)

So, do take caution and self-evaluate yourself. Don't mix enthusiasm/trends with passion. Trends keep changing. don’t follow the data science (or big data) hype blindly.  Besides, passion is not always good enough. You must possess the talent and need to be good at particular tasks. If you don’t possess a strong aptitude, quantitative background, and programming skills, Data Science & Analytics might not be your cup of tea.

There are several exciting and new-age career options – like digital marketing, designing, psychology, renewable energy, biotechnology, biomedical engineering, regenerative medicine, hospitality & tourism etc. Don’t just follow the crowd. You might also feel amazed to know that there are excellent career options that don’t require Maths & Science. If you need help to decide the right career path, try out the Free Career Aptitude Test to find out your signature strengths and matched career options. 

If you need assistance with identifying the right program or cracking the admission procedure at the top universities, just give me a shout. You could also share your thoughts and/or queries in the comments, and don’t forget to share the blog post  :)

You might also like the following blog posts:

MBA vs MS Business Analytics vs MS Data Science – Tips for Choosing the Right Program

How to Get Data Science and Machine Learning and AI Jobs in 2018

Top Platforms & Resources to Learn Data Science and Machine Learning – Survey Summary of 16,716 Data Professionals by Kaggle

Tanmoy Ray 11/1/2018 · #2

#1 Thanks @Hector Fong

Hector Fong 11/1/2018 · #1

Buen aporte gracias por compartirlo@Tanmoy Ray

+1 +1