What you need to know to become a Data Analyst

and how to learn it

Aakash N S

Feb 11, 2021

A repository of resources to boost your career in the data domain

Index Of Contents
  ∘ 1. Basic Programming (preferably Python)
  ∘ 2. Excel, Databases, and SQL
  ∘ 3. Data Analysis with Python / R
  ∘ 4. Basic Statistics and Probability
  ∘ 5. Documentation, Presentation, and Communication

If you’re pursuing a career in data science & AI, the Data Analyst role is a great starting point. Here’s what you need to know to become a data analyst:

1. Basic Programming (preferably Python)

You must be comfortable with variables, statements, functions, loops, classes, modules, etc., and have the ability to write basic programs in Python (or any other programming language).
You may need to create automation scripts to periodically pull data from DBs, do some analysis & store the results back or plot some graphs and send emails, etc.
Familiarity with the Unix shell, Git, SSH, etc. will help especially if you’re part of a small team

Learn it here: https://jovian.ai/aakashns/first-steps-with-python

2. Excel, Databases, and SQL

You’ll be pulling data out of databases (MySQL, MongoDB) and data warehouses (RedShift, BigQuery) using some variant of SQL
You will often need to do some quick analysis, computation, or plotting using Excel / GSheets
Most companies also use some proprietary tools like Tableau or PowerBI

Learn it here: https://www.coursera.org/specializations/excel-mysql

3. Data Analysis with Python / R

Load data files using Python / R and use Jupyter notebooks / RStudio for analysis
Clean, process, transform and analyze tabular data with numpy & Pandas or R
Create visualizations using libraries like Matplotlib & Seaborn (or ggplot in R)

Learn it here: https://jovian.ai/learn/data-analysis-with-python-zero-to-pandas

4. Basic Statistics and Probability

Basic familiarity with the concepts of statistics (mean, median, standard deviation, etc.)
Some knowledge of probability and random distributions (Gaussian, Poisson, etc.)
Knowledge of correlation, estimation, hypothesis testing, p-values will help

Learn it here: https://www.khanacademy.org/math/statistics-probability

5. Documentation, Presentation, and Communication

Your projects will require communication with stakeholders of different teams & designations
Capturing and documenting insights from your analysis is a critical part of your role
You will be required to present your findings with clear explanations and data to back them up

You’ll have to learn these the hard way! Build projects, write good documentation, publish them online, give presentations or talks at meetups.

P.S.: We’re working on a new intensive bootcamp that will help you become career-ready and land your first data science role. If you’re interested in being part of the first batch, please provide your details here: http://bit.ly/jovian-data-analyst-bootcamp