Kunal Jain — Updated On April 17th, 2015
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Data science has become one of the most dynamic field. Every alternate month I hear about a start up coming up with next gen tools and products. Cloudera, Neo4j, MongoDB, Ayasdi are some of the companies, which are showing us this exciting future.

With this pace of development, your learning strategy needs to change accordingly. You can no longer learn a tool and be good for 2 – 3 years. You need to learn continuously.


I lay out 8 rules of new age analytics learning in this post. If you are serious about your future in analytics, you should keep them in your heart:


Rule 1: Open Source tools are in

Open source tools are growing in their presence by the day, for the right reasons. They are cost effective, have big communities to support and have faster pace of development. They are also the tools available to a scientist (or a freelancer) working on his own.

You can read a more detailed comparison of these tools against SAS here. Except for people just entering analytics industry (with primary concern as jobs), learning R (or Python) is almost mandatory to future-proof yourself.

So, if you want a long term career in analytics, learn one of these now!


Rule 2: Democratization and free trials of tools will become the norm

Free trials of tools in individual capacity will become the norm (if it is not already). What I mean is, that more and more companies will offer a basic version of their tools for free. For example, Qlikview offers personal edition as a free download, but you need to buy licenses if you want to share you dashboards. Big query (from Google) offers querying till a particular data size for free.

How does this matter?

Well, you get access to any tool you want as a starter. You can test out tools before purchasing / deploying them. Also, you can accelerate your learning through downloading and experimenting through these tools.


Rule 3: Deep learning in at least one subject will propel your career 

Especially so, in early part of your career. In order to distinguish yourself, you will need specialization in at least one area. If you are a Business Intelligence professional, you need to understand the entire spectrum of tools available, their pros and cons. Same with Big data experts and data scientists. The pace of change doesn’t allow you to specialize in all these subjects.

You are free to pick what you prefer, as each of the specialization offer good career aspects.


Rule 4: However, for leadership positions, you will need broader perspective

Leaders will be expected to know what is happening across spectrum and how can that benefit the organizations. So, at some point, when you have deep knowledge in one of the subject areas, you need to pay equal attention to other areas (at a higher level though).

Or as Srikanth Velamakkani, CEO, Fractal Analytics puts it:



Rule 5: Outstanding visualizations and storytelling will differentiate best analysts from the rest

With data size increasing every second, you can no longer rely on bar-charts and pie-charts to tell your stories. New creative visualizations help deliver stories effectively and efficiently. Be it infographics, graph representations of networks or geo-spatial heat maps – all of these are far better to make an impact compared to a bar graph / table narrating the same.


Rule 6: Man machine co-ordination will gain in importance

Machine learning is gaining importance due to strong fundamental reasons. Be it Google driverless cars, or your smartphones trying to understand your needs or a sensor on your wrist to monitor your health regularly, all of these require man machine co-ordination. I think the career opportunities here can almost be divided in 2 categories:

  1. Data collection from various sensors and machines
  2. Analyzing stream of data to come up with insights and personalized experiences


Rule 7: Data Science competitions are opportunities to learn and showcase your talent

I love these competitions and hope that you do too. Sadly, I don’t get as much time for these as I would want. But these are ideal platforms to learn along side your peer. Look at the kind of discussions happening on various competitions on Kaggle and you will understand what I am talking about, You learn immensely through these competitions.

As a side benefit, they can become your hiring platforms.


Rule 8: Last but not the least, learn continuously

This goes without saying! The more you learn, the better it is. What is important is to learn regularly and continuously. Be it courses on Coursera, Youtube tutorials, blogs or iPython notebooks on github, just spend time to learn on a regular basis.

Each of these rules is something I believe in. Internalizing them will help you become clearer on your learning journey. What are your thoughts on these? Do you have any other rules / perspective to add? Kindly do so, through the comments below.

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photo credit: giulia.forsythe via photopin cc

About the Author

Kunal Jain
Kunal Jain

Kunal is a post graduate from IIT Bombay in Aerospace Engineering. He has spent more than 10 years in field of Data Science. His work experience ranges from mature markets like UK to a developing market like India. During this period he has lead teams of various sizes and has worked on various tools like SAS, SPSS, Qlikview, R, Python and Matlab.

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10 thoughts on "8 rules for new age analytics learning!"

AKveni says: April 11, 2014 at 11:15 am
I have done my big analytics course, but do not have any experience in software field and my background is BA. I have done my higher diploma in software engineering and learnt SAS, but no experience. Now I want to join as a R programmer as a trainee, where could I get a chance? Reply
Kunal Jain
Kunal Jain says: April 15, 2014 at 5:38 am
AKveni, People will be more open to take you as a trainee if they see some efforts you have put in. Start going through courses on Coursera and apply for jobs in parallel. Thanks, Kunal Reply
Nitesh says: April 17, 2014 at 10:33 am
Hi Kunal , Thanks for writing this wonderful Blog .I regularly follow all the blogs @ anayticsvidhya .It really helped me a lot when i was in dilemma that i should enter this industry or not .Now i have decided and learned SAS , going through R and at the same time also learning at cousera (DATA SCIENCE ) .As you mentioned kaggle i am also following all the events kaggle this is a great platform of learning and also every one can interact with global community . I am from engineering background and two plus yrs of experience but not in analytics industry .So,how much all these efforts will help me to lend in analytics industry . what else i should do to enter in this area. Thanks, Nitesh Reply
Kunal Jain
Kunal Jain says: April 20, 2014 at 4:05 pm
Nitesh, Good job on the initiatives you have taken to enter the industry. You are on right track and I would urge you to stick to these. Quality of learning would matter more than quantity of learning. So, if you are participating in a competition on Kaggle, look at what other people are doing and then compare it against what you are doing. Wish you all the best for a bright career! Thanks, Kunal Reply
vaishalee says: April 24, 2014 at 1:08 pm
nice blog. started reading it today n finding it very usefull. I would like to know the difference between statistical programmers required skillset and business analyst skillset. thanks Reply
Kunal Jain
Kunal Jain says: April 27, 2014 at 7:25 pm
Vaishalee, Can you be a bit more specific about these roles? In case you have a job description, please feel free to point at them. These terms are used loosely in the industry and hence I can not provide a standard answer without knowing the context. Thanks, Kunal Reply
Raj says: May 01, 2014 at 8:23 am
Hi Kunal, I am very much interesed in learning analytics, I am working as asset management analyst , want to step into analytics, I have done some ground work about analytics how to approach. but i am lacking in proper gudiance how to make this complete the analytics, can you guide me form where to start the analytics and what all are necessary Reply
Kunal Jain
Kunal Jain says: May 03, 2014 at 4:25 am
Raj, You can start with courses available on Coursera. Data Science track from John Hopkins University might be a good place to start. Let me know if you have any specific queries, once you start these courses. Regards, Kunal Reply
Ankitesh Mathur
Ankitesh Mathur says: May 04, 2014 at 12:32 pm
Hi Kunal, Awesome article, i follow ur blogs regularly, content which is publish here is so knowledge full and inspiring. Ur blogs had inspire me a lot to choose analytics as a future career I have done 3 month certification course from the IIM Ranchi in business analytics. i had an experience of 1 year in E-Business company as an ERP and CRM Admin. Now i want to pursue my career in analytics i m pretty good with R i have an interest in linear regression. could u suggest me from what level and post i can start my career in analytics industry, Because All are demanding experience for all opening. what kind of post are their in analytics industry for beginners and brief description about the requirement for those post I m looking for designing several Predictive models in analytics with the help of R could u suggest a specific course in Coursera which can help me in understanding basic needs and concept which can help me developing predictive models. In applying Analytics, Domain of industry plays what kind of role? Regards Ankitesh Reply
Yuvaraj M
Yuvaraj M says: September 17, 2016 at 3:45 pm
Hi, Thanks for this nice article, basically I am from ETL background with 5 years of experience mainly on SAP BODS, Right now I am very much interested in Business intelligence landscape. I would like to know your suggestion regarding about my future learning, I have a plan to learn SAP HAHA BI modelling / Data Warehouse architect? Reply

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