How to Become a Data Analyst?

Introduction

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 Analysts to surge in recent years and is expected to be high in the next decade.

The U.S. Bureau of Labor 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. Overall, the Data Analyst profile offers a promising career path and attracts high salaries as well.

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.

If you are interested in starting your career in Data Analytics, check out live classes provided by Scaler here.

Who is a Data Analyst?

• Data Analysts leverage enterprise datasets to solve various business problems by applying various visualization tools, statistical analysis, and basic programming languages.
• Data Analysts 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 Analysts 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 Analysts are responsible for collecting, processing, and analyzing 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 -
  • Defining the business objective or problem statement
  • Identify relevant data sources and collect data
  • Cleaning data using programming languages and various tools
  • Analyze data using various statistical and visualization techniques
  • Interpret results to derive insights and communicate findings

How to Become a Data Analyst?

Data Analysts 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 on How to Become a Data Analyst

Academics/Educational Qualifications

• Data Analysts 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 Analysts 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 Analysts had a bachelor’s degree as a minimum educational requirement. Some organizations prefer a master’s degree such as MBA for senior Data Analyst roles.
• Alternatively, you can consider pursuing a certification or online course to learn and master all the required skills to become a Data Analyst. Scaler 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 Analysts 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
  • Data Analysts must have a good understanding of at least one programming language. Data Analysts apply various programming languages for many Data Analysis tasks such as Data Collection, Data Cleaning, Statistical Analysis, Data Visualization, etc.
  • The most popular programming languages used by Data Analysts include -
    • Python
    • R
    • SA
• Data Visualization
  • 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.
  • Data Visualization is a must-have skill for a Data Analyst. Data Analysts 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.
  • The most popular tools and programming libraries used by Data Analysts to visualize the data are -
    • Tableau
    • PowerBi
    • Visualization libraries in Python and R
• Data Warehousing
  • A Data Warehouse is a central repository of information that can be further analyzed to make informed decisions and derive insights.
  • Not all Data Analysts are required to work on Data Warehousing, but some Data Analysts connect multiple databases to create a Data Warehouse to query and manage data.
• SQL Databases
  • SQL stands for Structured Query Language that is used by Data Analysts to query, update, and manage relational databases and extract data.
  • For years, organizations have been storing their data in relational databases due to their simplicity and ease of maintenance. Data Analysts must have a good understanding of SQL language to interact with SQL-based databases and collect required data for further analysis.
• Database Querying Languages
  • 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.
  • 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
  • Often Data Analysts 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.
  • Data Analysts 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
  • Microsoft Excel is prevalent among organizations. Based on an estimate, around 750 million people worldwide use the Microsoft Excel 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 Analysts. Excel has a limitation in exploring large data but it is very useful for Data Analysts to quickly analyze small datasets.
  • Data Analysts should have a good understanding of various modeling and analytics techniques through Excel.
• Machine Learning
  • Machine Learning is a field in the Computer Science discipline which enables computers to learn the patterns in the data without being explicitly programmed.
  • Though Data Analysts 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
  • Statistics is a branch of mathematics that deals with data collection, analysis, interpretation, and presentation. It provides various tools and methods for Data Analysts to identify patterns and trends in the data.
  • A Data Analyst must understand various statistical techniques such as regression analysis, hypothesis testing, etc.
• Linear Algebra and Calculus
  • Calculus, Linear Algebra, and Probability are the core concepts of any statistical analysis or machine learning algorithm.
  • Strong knowledge of these mathematical concepts can help Data Analysts 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
  • 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
  • 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

• 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 analyze 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 -
  • Data collection from a variety of sources
  • Data cleaning, processing, and analyzing
  • Data visualization through charts, graphs, dashboards, etc.
  • 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 Analysts are in high demand, and you will find a wide variety of Data Analyst 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.

What Tools Do Data Analysts Use?

Data Analysts use a variety of tools to collect, process, and analyze data. The most popular and important tools used by Data Analysts include -

• Microsoft Excel - It is prevalent among Data Analysts as it provides many in-built functions to quickly analyze a small dataset.
• Tableau - Data Analysts use Tableau for Data Aggregation and Analysis. Using Tableau, Data Analysts also can build dashboards to communicate findings or monitor KPIs.
• PowerBI - It is another visualization software heavily used by Data Analysts. They use it to create beautiful dashboards using various charts, graphs, etc. to effectively communicate insights and monitor trends.
• Google Analytics - Google Analytics is a platform that collects data from websites and apps to create reports that can provide insights into an organization’s business. Data Analysts use Google Analytics to understand customer data and identify trends and factors affecting customer experience.
• Jupyter Notebook - Jupyter Notebook provides a simple way for Data Analysts to build, test, and validate their hypotheses using a programming language.
• GitHub - It is used by Data Analysts to build Data Analytics projects and share them with other team members or the community.

Data Analyst Job Outlook

In today’s world, the success of any organization is strongly dependent on how it utilizes and processes data. Organizations today are generating large amounts of data, and it has become paramount for them to analyze it and extract valuable insights that can help them better understand their customers and their relationship with the business's products or services. As organizations across industries have realized the importance of data to succeed and stay ahead of the competition, they have increased their investment in implementing Big Data and Analytics tools and technologies. They are looking to hire data professionals who can dig into this data and generate valuable insights to drive business decisions. This has led to the demand for Data Analysts surge in recent years and is expected to be there for the next decade. The U.S. Bureau of Labor 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.

Data Analysts are not limited to a specific set of industries and are required in the majority of industries such as finance, healthcare, information, manufacturing, professional services, retail, etc. Overall, the future of Data Analysts is quite promising, and it offers a great path to build or pivot your career.

Data Analyst Salary

Data Analysts are one of the highest-paid professionals in the world. According to Glassdoor, In the USA, the average annual salary for a Data Analyst ranges between 65K - 70K USD while a Senior Data Analyst earns around 97K USD on average.

Based on Payscale, In India, a Data Analyst earns around 4.75 lacs per annum on average while the average salary for a Senior Data Analyst is approximately 8.25 lacs per annum.

However, the salary of a Data Analyst will vary based on multiple factors such as location, experience, skills, industry, companies, etc. If we factor in experience, in India, the salary of a Data Analyst ranges between ₹2 LPA - ₹10 LPA depending upon years of experience spent in the role. Let’s see how experience affects the salaries of Data Analysts in India.