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How To Start A Career In Data Science And Not Get Stuck In Dead-End Tech Jobs

Preparation for any career requires a lot of sweat and toil and in the case of data science, it is not restricted to performing well on the interview day alone. An aspiring data scientist is expected to prepare across multiple fronts. In today’s world, whatever your field of work, having skills and knowledge in Data Science will play a great role in your career development.

The McKinsey Global Institute estimates that the US could have as many as 250,000 open data science jobs by 2024. Closer home in India, a study by Edvancer and Analytics India Magazine found that the number of new analytics jobs advertised per month increased by almost 76 percent from April 2017 to April 2018. Even our own research confirms this. When we harvested and processed data from millions of job descriptions across the market, we found that the demands in data science are increasing at a rapid rate. If we were to look at a location-wise trend, Bangalore stands on top as the hub of machine learning and AI, covering 34% of total jobs created.

By all accounts, data is poised to play a massive role in shaping the future of the industry, and possibly the future of humanity itself. Therefore, a career in data science certainly makes for an attractive proposition for any aspiring engineer. The glut of courses in data analytics, AI, machine learning etc. points to the growing popularity of data as a career option.

While the interest in data science is certainly welcome, I have found that there isn’t enough clarity among students and young IT professionals on what it takes to prepare for a career in data science. A common mistake people make is to enrol in online courses that are either too basic or vague to be useful in practice. The danger here is that these courses are likely to get you stuck in dead-end data analytics or AI jobs that are repetitive and provide no scope for learning.

Professional Qualifications And Skills

Spirit Of Enquiry: In the industry, it’s quite rare to be presented with a fleshed-out problem statement that needs to be solved using data. Rather, you will likely get your best insights by studying patterns and trends in the reams of data. The ability to find these patterns or anomalies needs a certain basic curiosity and knack for looking beyond the obvious.

Analysing: When dealing with multiple parameters and data sources, trying to find the exact correlation and causation for any parameter can be confusing and challenging. The ability to use logical reasoning to draw inferences and arrive on a conclusion is invaluable as a data scientist.

Big Picture Thinking: When you are analysing large amounts of data, it is easy to get overwhelmed by the findings and insights. But the real value of those insights lies in them being rolled up to the business level so that they have a real impact on decision making. It needs imagination and ability to look at the big picture and draw insights that truly matter.

The potential impact of data science is limited only by imagination. Whether it is bringing down crime rates or reducing traffic woes or saving the planet from climate change, no problem is too big to be solved through data, provided you have the right vision.

Perseverance: Despite all the great things data and AI can do, the ground truth remains that the success rate isn’t always high for data projects. Patience is a key virtue while working with data. You can safely assume that 90 percent of the things that you do will fail to meet their logical conclusion. It’s difficult to accept failure, especially when you put your heart and soul into something, but perseverance is what will get through it.

Mathematics Lover: Needless to say, a lot of data science boils down to mathematical and statistical analysis. While you don’t need to be an expert in advanced mathematics, a basic love for mathematics and a math-oriented approach are crucial. If you love tinkering with datasets etc. it will serve you well in the field of data.

If you think you possess these traits and would like to explore a career in data science, we can move to the next step, which is to understand the skills and knowledge that you need.

Courses and Training
Data scientists need well-rounded knowledge of mathematics, statistics, deep learning, machine learning and NLP. Here are a few recommended courses and training institutes that you can explore.

Institutes:
1. https://www.insightdatascience.com/
2. https://www.thisismetis.com/
3. https://www.thedataincubator.com/

Prerequisite In Mathematics:

(i) Linear Algebra, Probability, Statistics:

1. https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
2. https://ocw.mit.edu/resources/res-6-012-introduction-to-probability-spring-2018/
3. https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x
4. https://ocw.mit.edu/courses/sloan-school-of-management/15-075j-statistical-thinking-and-data-analysis-fall-2011/index.htm
5. https://www.stat.berkeley.edu/~aldous/134/gravner.pdf
6. https://www.stat.berkeley.edu/~aldous/134/grinstead.pdf

(ii) Machine learning:

1. http://www.greenteapress.com/thinkstats/
2. http://www.greenteapress.com/thinkbayes/thinkbayes.pdf
3. https://www.coursera.org/learn/machine-learning

(iii) Natural Language Processing:

1. https://web.stanford.edu/~jurafsky/slp3/

(iv) Deep learning and AI:

1. http://web.stanford.edu/class/cs224n/
2. http://cs231n.stanford.edu/
3. https://github.com/oxford-cs-deepnlp-2017/lectures

Books:

Books are always a great way to shore up knowledge on any subject. Here’s a list of some of the best books on data sciences that I highly recommend reading.

1. https://www.cs.ubc.ca/~murphyk/MLbook/
2. http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf
3. https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf
4. https://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf

Competitions

Competitions are a great way to brush up your skills and develop a problem-solving approach. Remember that participation and learning should be the focus. It isn’t about winning and losing; it’s the learning that matters.

1. https://www.kaggle.com
2. https://www.drivendata.org/competitions/
3. https://machinehack.com

Influencers:

As they say, we need to stand on the shoulders of giants to be able to look further. Following some of the greatest minds in data science and understanding their thought process is a great way to keep your knowledge up to date. Here’s an indicative list of some data science experts that every professional must follow.

https://twitter.com/fchollet
https://twitter.com/goodfellow_ian
https://twitter.com/chrmanning
https://twitter.com/rsalakhu
https://twitter.com/RichardSocher
https://twitter.com/karpathy

For people that want to make themselves more indispensable to their employers while gaining an innovative, creative, and sustainable career path data scientist undoubtedly seems like the dream career option. With the right preparation, only sky is the limit.

Rahul Kulhari
He is the head  of Data Science at EdGE Networks. His areas of focus are deep learning and NLP. Currently, he’s working on reinforcement learning, meta-learning, explainable AI and Enterprise AI. Rahul has a Bachelor’s of Technology in Computer Science from BKBIET Pilani and a Post Graduation Diploma in Advanced Computing(PG-DAC) from CDAC Hyderabad.

Source: https://www.analyticsindiamag.com/how-to-start-a-career-in-data-science-and-not-get-stuck-in-dead-end-tech-jobs/

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