It often seems like entering the analytics profession requires an impossible set of credentials.

Have you been coding Map Reduce processes for Hadoop since before it was cool? Are you a wizard at presenting data to executives and completely fluent in R, C++, Java, SPSS, Mandarin Chinese, and FORTRAN? If so, you might be a data unicorn: the rare beast that knows everything about data science, data analytics, business intelligence, and the fundamental properties of the universe!

But if you aren’t a unicorn, this article is for you. Below is a list of practical rules on how to enter the analytics profession.


Rule 1: Specialize in an Analytics Discipline
Unless you have endless time to devote to data science, there are too many fields to acquire skills on a normal time scale. Pick one of the disciplines to specialize in. There are many overlaps and you will almost certainly learn techniques and information from other disciplines, but you should specialize in one area:

  • Business Intelligence: technology-driven process for data analysis and presenting that information to managers, executives, and business decision-makers. Includes development of dashboard, visualizations, and queries against the data.
  • Programming: Software coding and system development. As a rule, you’ll need: a scripting language (e.g. Python), a data language (e.g. SQL), and a middle-level language (e.g. Java).
  • Data Warehousing: Development of relational or non-relational databases designed for query and analysis. Also includes Extraction, Transformation, and Loading (ETL) of data.
  • Statistics: Mathematical understanding underlying decision-making and analysis of large data sets.


Rule 2: Learn a Technology that Supports Your Discipline
You need to know at least one supporting piece of software for your analytics discipline. Most of these programs have free demos and there are plenty of YouTube videos and cheap online courses that use these software.

  • Business Intelligence: Tableau, IBM Cognos, Salesforce Analytics Cloud
  • Programming: R, Python, SAS, SQL
  • Data Warehousing: SQL, Hadoop, Spark, IBM Netezza, Apache Pig, Amazon Redshift
  • Statistics: SPSS, SAS, R


Rule 3: Learn to Communicate
You need to be able to communicate why the information or service you provide is critical to the business. You need to be able to tell a story with your skills. You need to feel comfortable interacting with IT coworkers as much as you are comfortable with business coworkers.


Rule 4: Learn SQL
There are many analytics jobs that do not require development of SQL queries, but learning SQL will teach you about structured data and the logic of searching on data. The fundamentals of SQL can be learned in a few hours, and knowledge of SQL serves as a signal that you are knowledgeable about data.


If you can follow these 4 rules, you will set yourself up for success. Do not attempt to learn everything there is to know about the analytics profession prior to entering the profession. A unicorn cannot be made in day. Instead, focus on one area where you will specialize and continue to expand your skills from that foundation.

For a free class on analytics, check out our MOOC on Harvard’s EdX platform, made in collaboration with Microsoft. Also, with data science courses on FutureLearn, you can discover key concepts in data analysis