Is analytics career for me? [ Part – 2 ]
Target Audience:
This blog is a sequel to my previous blog “Is Analytics Career for me?”. This blog is in the continuation with the previous blog for students and working professionals wanting to make a career in Analytics.
Skills required for Analytics
In the previous I had highlighted the three critical skills required to be successful in the field of analytics:
- Logical Thinking and Business Understanding is very essential for one to be able to gain domain knowledge and thereby solve the business problem.
- Mathematics and Flair for Numbers is important as all your analytical outputs will be full of numbers and statistics
- Ability to work on Data Management Tools as you will be constantly working on data, data and data.
Self-Assessment Tips
In this blog, I attempt to go one level deeper to help you understand the skills you may have to acquire to build a successful Business Analytics / Decision Science career. I am sharing some of the observations based on
- My professional experience in analytics
- Career counselling calls I keep getting from students / working professionals
- Interaction and feedback from students who attend my training programs on R, SAS, Business Analytics, Machine Learning Techniques and Predictive Modeling
If I have to segment the candidates looking to make a career in analytics then I would segment them into four broad categories as shown below.
Common to all the above categories, first and foremost you have to have a passion for analytics. There should be glitter in your eye when you hear words like DATA, ANALYSIS, PUZZLES, and MATHEMATICS. You should not be lured just for money sake…. easy said than done J
Okay, let me try highlighting specific skill gaps commonly observed for each of the categories and these should be their skill development focus areas:
- BI Professionals wanting to make a career in analytics could try the following
a. Get back to basic mathematics and statistics. Self-learn some of the below statistics with their business applications
i. Measures of Central Tendency (Mean, Median, Mode)
ii. Measures of Dispersion (Standard Deviation & Variance)
iii. Correlation and Covariance
iv. Hypothesis Testing, Chi-Sq Test, p-value
v. Weighted Average, Ratios, Proportions and Percentiles… Yes, I repeat Weighted Average, Ratios, Proportions and Percentiles
b. Move up the curve from Dimension, Measures & Facts and start interpreting the reports, dashboards and trends you generate from a business perspective. Don’t merely churn out reports/dashboard but also try to read and interpret the numbers.
2. Business Consultant wanting to become Business Analysts or Data Scientists will have to break their inhibition for coding. Quite often I come across Business Consultants who ask do we have to learn to code. The answer is a big YES.
If you find writing code to be very difficult then start with tools which provide easy to use graphical interfaces. Slowly and gradually get your hands dirty with coding.
3. Fresher MBA Students with no technical background quite often find themselves to be in a somewhat disadvantage situation for a Data Scientist / Business Analyst role in Analytics because of their lack of technical knowledge. Such students can be successful in analytics provided they put in some extra effort to learn and know things like:
a. What is Programming Language & Scripting Language?
b. What is API? How does it work?
c. What is an Editor? What is the Console?
d. What are bits and byte? How is data represented as bits?
e. What is Variable? What is Environment Variable? Local & Global variable
f. What is Function and Procedure in SQL?
g. What is a Unique Identifier? Why do you require a unique identifier?
h. What is RDBMS?
i. SQL and Data Management Skills
Many of the above questions are trivial for students with technical understanding, however, I have observed many students struggle to understand these concepts and consequently they are unable to cope up with coding (programming). With some perseverance, I am sure anyone can learn and be successful.
- Post Graduate / Graduate freshers with technical skills are on the advantageous side. It is observed that engineers are more successful in the Data Scientist role. Here I would like to mention a few points for the freshers in this segmenta. Develop business acumen and deep domain knowledge
b. Get much grounded on your Concepts. In my training I keep stressing to students “Concepts -> Logic -> Syntax”. Focus on concepts, build logic and only then bother about syntax. However, I see techie students be more obsessed to learn syntax.
c. Learn to automate repetitive tasks.
The category wise suggestions are just indicative and generic. Each individual is different. E.g. in my career, I have worked and mentored individuals who have moved from Accounting Job to Analytics Job, Boiler Maintenance Job Industry to Business Analytics Job. So, I am confident you can also make a successful career in analytics if you are passionate about it.
Final Word
The views expressed above are generic and not specific to any individual falling in any of the above categories. Feel free to share your comments, like and dislike for the above blog. You can get in touch with me by dropping an email.
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