How to learn data analytics in 2023; a detailed guide

TechGig
3 min readMar 20, 2023

If you are interested in a data analytics career or want to restart your career in data analytics then follow these 5 steps recommended by us.

Data and statistical modeling are used in a process known as “data analytics” to spot patterns, forecast results, gauge performance, and create or optimise systems.

The process of drawing information from data-based resources is known as data analytics. It entails using methods and instruments to analyse data in order to discover trends, patterns, and insights. In order for decision-makers to grasp the context and significance of the data, it is necessary to gather, analyse, and display information from data sources in a certain way.

Businesses across all industries are utilising data to make better decisions because of the exponential data expansion that occurs every minute across the world and the insights and value that correct analysis of business data provides.

As the amount of data is continually rising, business data analytics is undoubtedly an industry that is expanding. The need for experts who can evaluate this enormous amount of data and bring value to businesses grows as a result.

Therefore if you are interested in a data analytics career or want to restart your career in data analytics then follow these 5 steps.

1) Focus on hands-on practice and not theoretical concepts
People focused too much on learning all concepts and not practicing them. Hands-on practice is important. Focus on creating projects and building a portfolio. Always go by the rule 3:7, spend 3 hours learning and 7 hours practicing hands-on. Websites to create projects: Kaggle.com, GitHub datasets, google public data sets etc. You can also participate in competitions on Maven Analytics, Fest man Challenge, Enterprise DNA etc.

2) Learn only a few tools
You do not need to learn all the tools. Also, you don’t need to have advanced knowledge in each
Mostly experts recommends: Excel, PowerBI (or Tableau), SQL, Python (comes later)

When you haven’t worked at a data job before, it feels like you must know all of them. You do not need to learn all. Every company uses different sets of tools. Learn the concepts using basic tools and get, if need be, shift the tool.

3) Choosing the right role as per your interests and not following the herd
Data is a very vast career option- you can have multiple options such as Data viz specialist, BI analyst, Data engineer, data analyst, machine learning engineer, etc. Don’t just blindly go for data science if you don’t have an interest. For e.g.- if you are transitioning from a non-tech background to getting into BI analyst, Data viz would be doable

4) Learning communication skills
People don’t usually associate communication skills with rejection in data science roles. They expect that if they are technically profound, they will ace the interview. This is actually a myth.
Simple way of learning: Watch English tv shows with subtitles and practice in front of the mirror!

5) Learn about Domain Knowledge\ business context
You don’t just need data skills, but also the knowledge of which data are you working on. For eg- Marketing, HR, Customer Experience, Sales etc. Till you don’t understand the data, you won’t be able to make sense of it.

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