How to build a career in Analytics –this is the first question comes to mind when a person wants to change this career path or just a student wants to start job with data analytics. This is a question is fundamentally true and thave been asked umpteen number of times by a lot of people!
The simplest answer is Analytics is all around you. And you just need to seize the opportunity to apply Analytics in the world of business.
Essential Skills You Need
- Basic Statistics
- Data Munging or Data wrangling
- Knowledge about data tools like: R, Python, SAS
- Little Knowledge about Machine Learning
- Very good knowledge in Data Visualization and Communication and BI Tools
- May not need very depth knowledge in Machine Learning Algorithm and Mathematics.
Data Analyst is one stage earlier than Data Scientist. To be a Data Scientist you need to have great knowledge about various fields that we discussed earlier. click here to read that. To be a data analyst you need to have enough knowledge in below fields:
There are a lot of varied roles in data science industry. A data visualization expert, a machine learning expert, a data scientist, data engineer etc are a few of the many roles that you could go into. If your are not aware of different roles in Data science industry then please read my other post about Different Job roles in Data Science and Analytics Industry.
The first thing you should do just choose the right role. Now if you have decided to be a data analyst and thinking how to get started, then the following write-up is for you.
Steps for Entering into Data Analytics Field
Step 1: Learn the tools for data analysis
There are lots of tool available in the market like SAS, SPSS, R, and SQL etc. Start with any tool that you can get access to. Learning is not about knowing everything, but learning substantial portions thoroughly and gaining sound knowledge about what you learn.
Step 2: Learn few useful tricks for data analytics
If you know the tools, your work is half done. Now you should learn the tricks of the analytics trade. Here are two options before you;
a. Learn from another experienced person or persons who are there in your organization
b. Learn from professional curriculums.
Step 3: Look for an opportunity
Quite often, people find it very difficult to identify where to start. There is no rule simple rule of thumb from where to start. But according to my knowledge I will suggest do not try and build a predictive model at the first go. First, just start by generating simple insights from the data which is not presently captured in the business reports. Create simple metrices. Then go and try models. The next step will be convert the facts into reports which can be generated for different time intervals and for different slices and dices of data.
Step 4: Make a case study of your work
First, you make a case study of your work and show case to the top management. Add those new initiatives to your resume. If your organization is not supportive of your Analytics initiative, look outside in the relevant domain. There would be plenty of opportunities outside for a person with your new found skills!
Step 5: Read plenty on Analytics
Read plenty of blogs and articles on Analytics. Join various analytics threads, follow Analytics companies and keep abreast of the latest happenings in Analytics. This will keep you well positioned for keeping a track on how Analytics is being applied in different business domains and functions and increase your knowledge in the field.
Learn Language like R, Python or SAS. Here are some good sources for learning R languages.
For Learning Python:
Learn basic concept of Statistics and Probability:
- Go to http://stattrek.com/ or http://swirlstats.com/ and start leaning about preliminary concepts. You can take some
- MOOCS from Standford Statistical Learning or from www. coursera.com. You can follow some good statistical blog like:
- http://simplystatistics.org/. You can also go to https://www.openintro.org/stat/textbook.php and pick some book and get stared.
Learn at least one BI tools:
Learn at least one BI tools from
Learn how to use some machine learning packages in R or Python
- Learn machine learning packages like: caret package in R from http://caret.r-forge.r-project.org/), scikit-learn in python from http://scikit-learn.org/.
Take part in Kaggle competition
- Go to https://www.kaggle.com/competitions and build a profile there. This will help you in future to get into good data related company. And it will also help to build solid concept in Analytics.