I have been writing and answering queries on career transition into analytics for more than 18 months now. While this experience has been very fulfilling, I still feel that there is a gap between what I tell people and what they implement. Let me tell this through a few examples:
Mr. A wanted to move into analytics badly. He had left his job and wanted to focus on learning analytics. He had been reading on the subject for some time now and was convinced that this was his calling. When he reached out to me, he asked what, where and how should he start learning about Analytics.
I suggested him to start by taking up a course on edX / Coursera and make sure that he completes all the assignments. When I checked back 3 months after our discussion – he had registered for at least 5 courses Online – completed none, got intimidated by the vast knowledge he needed to gain to make a career in analytics. He was probably more confused than what he was, when he started his journey.
or
K was an experienced analyst with a big retailer. She had 6 years of experience, but all of it on SAS. She wanted to learn a second (and open source) tool. I thought this would be easy for her. She had a lot of experience in the field and just needed guidance on learning a new tool. I laid out a learning path for her and thought she would take it from there. When I checked a month after our discussion, she had not written a single line of code in R or Python!
In these scenarios (and many more like these), people knew what they should be doing and probably had the intention to do it at some point. That is why they would have reached out to me. But they faltered somewhere during the journey. They couldn’t convert their chances even after being in a position to do so.
The problem here was not that people didn’t know what to do. They very well knew what was to be done. The problem was that of implementation and execution. And this is an equally bigger problem. Since, I have not addressed this issue directly in my posts till now – I thought, I would do it now. This problem is some what similar to that of training yourself at the gym – you know the benefits of training yourself. You also know what needs to be done – but still only a few of us are able to train ourselves at the gym regularly.
The key to solving this problem lies in taking small and focused steps with clear mind. The solution lies in taking the first step rather than questioning whether 15th step would be right or not!
Here are a few tips, for people who face challenges in executing their analytics learning plan.
- Start out easy (but do start) – This is probably the biggest problem people face – they just don’t start. They know what to learn and where to learn – but they still don’t start learning. They keep searching for more content, more resources – but don’t start with what they already have. Don’t look out for that perfect book or course – just start with what best you have access to. Log on to any MOOC and start learning the subject. Nothing beats action bias! While you start, start easy. Look out for simple solutions. If you have to install a software – look for an executable rather than compiling the code yourself. If there is a GUI version of a tool available – learn on that first. e.g. When I started learning SAS (my first analytics tool apart from Excel), I decided that I’ll use SAS Enterprise Guide – it has a GUI interface and I don’t need to learn the language from start. Once I grew more comfortable with the language and interface, I started picking up the code on the side. This made the process far easy for me.
- Define learning objectives clearly – You might face this challenge as soon as you start learning. You start with a video on Business Analytics, find out a term “Business Intelligence”. Next, you search for Business Intelligence and before you know, you are actually watching a tutorial on Tableau! This is the problem of plenty. You need to clearly define what you want to learn and focus only on that. One practice, which has helped me well to do so, is to define these objectives clearly at the outset. Not only do I define what I want to learn, but I also go to the extent of what I won’t learn. For example, when I started learning Python – I had committed that I want to learn Python only for data analysis and not for other purposes (e.g. Web development).
- Learn every day – You will likely run in this problem 3 – 7 days down the line. You started well, defined what you wanted to do – but then you ran into something – a new assignment at work, a new commitment to yourself or a new topic within analytics. As a result, you break your learning momentum. The efforts you made would go down the drain, if you do this every time. Once you have decided what needs to be done, you have to make sure that you do it daily. Take out time – however small to learn daily. It could be watching a video for 15 minutes or running a small code – but spend some time daily. You would see how quickly the momentum builds up
- Set aside some time – The best way to make sure that you learn daily is to set some time aside. It could be early morning, lunch time at your office or once you are back from work. But set a time aside for your learning and follow the routine. Soon, your mind will become accustomed to learning at this time.
- Take one problem, one tool and one technique to learn at a time – Don’t over commit yourself. Quite a few starters I meet promise to learn R and Python in next 6 months. My only advice to them is to pick only one tool. Most of the things which R can do, Python can do them as well and vice versa. By learning both of them simultaneously, you will become good in none! Similarly, learn one technique at a time and work on one problem at a time.
- Focus on your strengths – If you know that you enjoy reading – find the right book. If you don’t enjoy coding – start with a GUI tool. If you learn best late at night – learn at that time. Idea is to make learning as simple as possible. Take all the hassle away and just focus on learning.
- Do not rationalize – If you were not able to deliver on your learning plan – watch out! Your mind can quickly come up with rationalizations – why you couldn’t learn as much as you thought? Don’t give in to these rationalization. Accept that you failed and start again – but don’t start the blame game. Don’t blame your manager for the extra work or your family for extra commitment – find out a way and start learning.
What do you think about these tips? Have you faced similar problems, while executing your learning path? How do you overcome them? Are there any other tips you can share with us, which can help our audience learn faster.
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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.
