1. Data Analyst
The amount of data our world generates every day is truly mind-boggling. In
2020 alone, our world produced and consumed around 59 zettabytes of data. With
the advent of digitization and the arrival of the Internet of Things (IoT), this number
will only grow further. Based on a report by International Data Corporation (IDC),
our world is estimated to generate around 175 zettabytes of data. To put it in
perspective, one zettabyte is 8,000,000,000,000,000,000,000 bits or 1 billion
terabytes. Organizations worldwide have realized the true value of data for their
success and growth and have increased their investment in Big Data and Analytics
solutions. Based on a report by the KPMG survey, 92% of C-level executives are
using Data Analytics to gain deeper insights into marketing. One thing is certain -
Data Analytics will only gain momentum in the foreseeable future and will be at the
core of an organization's decision-making and strategic planning processes. This has
led the demand for Data Analytics to surge in recent years and is expected to be
high in the next decade. The U.S. Bureau of Labour Statistics has estimated a 25
percent growth in Data Analyst jobs during 2020-2030, substantially higher than
the 7.7 percent growth for other kinds of occupations. World Economic Forum has
identified Data Analyst as one of the fastest growing jobs for 2020-2030.
However, to become a Data Analyst, there are a certain set of skills you need to
learn and a few other steps that you have to follow. In this article, we will provide a
comprehensive guide on how to become a Data Analyst.
Who is a Data Analyst?
 Data Analytics leverage enterprise datasets to solve various business
problems by applying various visualization tools, statistical analysis, and basic
programming languages.
 Data Analytics bring technical expertise to examine the data, derive insights,
and present it in ways to help businesses and organizations make better
decisions.
What Does a Data Analyst Do?
 Data Analytics process and examine large amounts of data to produce
valuable insights by applying various visualization tools, statistical analysis,
and basic programming languages that can drive business decisions.
 Data Analytics are responsible for collecting, processing, and analysing
enterprise data to uncover underlying trends or monitor KPIs/metrics to help
business managers understand and solve problems. They must communicate
their findings and recommendations effectively to the business management.
 For example, a Data Analyst can collect the sales data for a company and
create a dashboard that can help the company to understand various
business metrics such as in which locations their sales are improving or
declining, which product is working better, and where they need to focus more
to increase the sales and revenue, etc.
 A typical Data Analysis project constitutes of five steps as mentioned in below
figure -

2. o Defining the business objective or problem statement
o Identify relevant data sources and collect data
o Cleaning data using programming languages and various tools
o Analyse data using various statistical and visualization techniques
o Interpret results to derive insights and communicate findings
How to Become a Data Analyst?
Data Analytics are in high demand across industries, and this job profile offers a
great career path along with high salaries. Whether you are a student or an
experienced professional, here are some steps toward becoming a Data Analyst
Academics/Educational Qualifications
 Data Analytics often hold a bachelor’s degree in STEM fields such as
engineering, statistics, economics, etc., or a master’s degree in MBA.
 While having a degree in the above fields can certainly be helpful but it is not
a mandatory requirement. Most Data Analytics come from a non-technical
background. In fact, most entry-level Data Analyst jobs require a bachelor’s
degree in any field. Based on a survey by IBM in 2017, 94% of the job
postings for Data Analytics had a bachelor’s degree as a minimum educational
requirement. Some organizations prefer a master’s degree such as MBA for
senior Data Analytics roles.
 Alternatively, you can consider pursuing a certification or online course to
learn and master all the required skills to become a Data Analytics. Inmakes
Academy provides a Data Science course that can help you learn all the
skills in one place. It includes live online classes, hands-on experience on
projects from top companies, 1:1 mentorship from expert Data Analytics and
Data Scientists, and secure placement assistance.
Get Technical Skills
To get your first job in the Data Analytics field, you need to acquire certain
technical skills. Whether you are learning through a degree program, certification, or
on your own, below are some essential technical skills you will be required to be
proficient in –
Programming Languages
o Data Analytics must have a good understanding of at least one
programming language. Data Analytics apply various programming
languages for many Data Analysis tasks such as Data Collection, Data
Cleaning, Statistical Analysis, Data Visualization, etc.
o The most popular programming languages used by Data Analysts
include -
 Python
 R
 SA
 Data Visualization

3. o Visualization is the graphical representation of the data using various
visual elements such as charts, graphs, etc., which can help convey
the information in a way that is easily understandable and pleasant to
the eye.
o Data Visualization is a must-have skill for a Data Analyst. Data
Analytics collect the enterprise data stored in disparate repositories and
visualize it using various programming languages and tools to
understand the underlying trends, patterns, etc., and monitor the
various business-related KPIs/metrics.
o The most popular tools and programming libraries used by Data
Analysts to visualize the data are -
 Tableau
 Power Bi
 Visualization libraries in Python and R
 Data Warehousing
o A Data Warehouse is a central repository of information that can be
further analysed to make informed decisions and derive insights.
o Not all Data Analytics are required to work on Data Warehousing, but
some Data Analytics connect multiple databases to create a Data
Warehouse to query and manage data.
 SQL Databases
o SQL stands for Structured Query Language that is used by Data
Analytics to query, update, and manage relational databases and
extract data.
o For years, organizations have been storing their data in relational
databases due to their simplicity and ease of maintenance. Data
Analytics must have a good understanding of SQL language to interact
with SQL-based databases and collect required data for further
analysis.
 Database Querying Languages
o The most common database querying language used by Data
Analysts is SQL. Though in recent years, NoSQL databases such
as MongoDB, Cassandra,, etc. have gained popularity among
organizations. NoSQL databases are different from SQL-based
databases and can store structured and unstructured data.
o Any aspiring Data Analyst should also focus on learning NoSQL skills
for data retrieval from these databases as it can help to stand out from
the crowd.
 Data Mining, Cleaning, and Munging
o Often Data Analytics have to deal with raw or unstructured data. This
data which is unusable at the beginning, needs to be transformed and
cleaned into a format that is usable and understandable.
o Data Analytics apply various programming languages and tools to
transform raw data into a structured format and perform several steps
to clean the data, such as discarding irrelevant information, removing
duplicate entries, handling or imputing missing values, filtering outlier
values, etc.
 Advanced Microsoft Excel
o Microsoft Excel is prevalent among organizations. Based on an
estimate, around 750 million people worldwide use the Microsoft Excel

4. platform. Microsoft Excel provides a wide range of features such as in-
built mathematical functions, pivot tables, visualizations, etc. It has its
own language, VBA, which is used to write macros that can help save
a lot of time for Data Analytics. Excel has a limitation in exploring large
data but it is very useful for Data Analytics to quickly analyse small
datasets.
o Data Analytics should have a good understanding of various modelling
and analytics techniques through Excel.
 Machine Learning
o Machine Learning is a field in the Computer Science discipline which
enables computers to learn the patterns in the data without being
explicitly programmed.
o Though Data Analytics do not frequently work on Machine Learning
projects. But having a general understanding of relevant Machine
Learning concepts can provide you an edge during your interview.
 Statistics
o Statistics is a branch of mathematics that deals with data collection,
analysis, interpretation, and presentation. It provides various tools and
methods for Data Analytics to identify patterns and trends in the data.
o A Data Analyst must understand various statistical techniques such as
regression analysis, hypothesis testing, etc.
 Linear Algebra and Calculus
o Calculus, Linear Algebra, and Probability are the core concepts of any
statistical analysis or machine learning algorithm.
o Strong knowledge of these mathematical concepts can help Data
Analytics to grasp the fundamentals of any statistical analysis or
machine learning algorithm.
Build Soft Skills
Along with the technical skills required to become a Data Analyst, they must also
have a certain set of interpersonal/soft skills. Let’s get into key soft skills required
to become a Data Analyst.
 Strong and Effective Communication
o As a Data Analyst, you will be required to understand business
problems and communicate your findings and recommendations to
business management or stakeholders. It is therefore essential to
develop strong communication skills for a Data Analyst in order to
communicate effectively. Strong Communication is the key to success
for a Data Analyst.
 Creative and Analytical Thinking
o Deriving valuable insights from the data to solve business problems is
not a trivial task. A Data Analyst must be able to think through
problems creatively and analytically. A Data Analyst must have the
ability to figure out where to look for the information in the data, as
most of the time, valuable insights would not be apparent.
Work on Projects with Real Data

5.  The best way to test and sharpen your technical skills is through working on
projects with real-world data. Getting a Data Analyst job is essential as
practical applications are always preferred over theoretical knowledge.
 You can look for courses or certification programs that provide hands-on
experience with industry projects. Alternatively, you can find out a variety of
free datasets to analyse on many Data Analytics related websites such as
Kaggle, GitHub, etc.
Develop a Portfolio
 A strong portfolio can help you demonstrate your skills and capabilities to
organizations and hiring managers. So, once you complete your hands-on
assignments with real-world projects, make sure to save them to create and
build your portfolio.
 A strong Data Analytics portfolio should contain projects that can
demonstrate the following skills or abilities -
o Data collection from a variety of sources
o Data cleaning, processing, and analysing
o Data visualization through charts, graphs, dashboards, etc.
o Derive actionable insights
 You can sign up for a GitHub account and start posting your projects and
code to the site to build your portfolio and include it in your resume.
Apply to Relevant Data Analyst Jobs
 Once you have completed hands-on assignments on real-world projects and
built your portfolio, it's time to prepare a strong resume and begin applying for
entry-level Data Analyst jobs.
 Data Analytics are in high demand, and you will find a wide variety of Data
Analytics jobs across industries. Before applying, you should choose the most
relevant jobs based on your skills, experience, and interests.
Consider Certification or an Advanced Degree
 As you gain more experience in a Data Analyst profile, multiple avenues for
the senior and advanced roles will open for you. You can consider having
relevant certifications or advanced degrees to develop and learn additional
skills to move into higher roles.
 For e.g., if you consider advancing into a Data Scientist profile, you can either
go for a master’s degree or consider a Data Science certification or courses to
learn additional skills required for a Data Scientist job.