An Insider's Guide to Becoming a Data Analyst - From Basics to Pro

How to become a data analyst

It’s an exciting time to become a data analyst! If you’ve ever dreamed of working in data analysis, this insider’s guide will help you get started. This guide will also provide insights on how to develop your career as a data analyst, from entry-level to pro-level skills.

What is data analysis?

Data analysis is the process of working with data to uncover insights, find patterns, and inform decisions. It explores the questions “Why” and “How” that can be answered using data. Data analysts ask these types of questions every day:

Why are sales higher this month than last month?
How many customers have we lost?
How many hours does each employee work per week?
What do our profits look like?
How does our revenue compare to last year?

These are all examples of data analysis. Data analysts are responsible for asking and answering these questions, and many more. They use many different analysis tools to complete their work, from programming languages to excel spreadsheets.

They’re often required to solve problems creatively while working in a team environment. In short, data analysts take a raw data set and transform it into useful, actionable insights that help teams and organizations make better decisions.

Types of data analysis

There are several types of data analysis, each with its own unique applications. Data analysis can be applied to a variety of industries, whether it’s marketing, supply chain management, or HR — there isn’t a single sector where data analysis doesn’t play a role! Let’s take a look at the most common types of data analysis:

Descriptive analysis: This type of data analysis describes the existing state of a business through numbers, facts, and figures. Descriptive data analysis is meant to shed light on the status quo, and it doesn’t aim for any predictive insights.
Inferential analysis: Inferential data analysis takes descriptive data and uses it to draw conclusions about future trends. For example, if 14 out of 15 customers prefer a certain type of product, we might infer that customers prefer that product.
Predictive analysis: Predictive data analysis takes the information from the data and uses machine learning to extrapolate future outcomes. Predictive data analysis is best applied to large data sets.
Prescriptive analysis: Prescriptive analysis takes future projections, diagnoses the causes behind those projections, and recommends solutions to prevent unwanted outcomes.
Data analytics: Simply put, data analytics is the process of collecting, cleaning, and analyzing data to find meaningful insights. Data analytics is often used interchangeably with “data science.”
Data science: Data science is the use of analytics, algorithms, and visualization to explore and analyze data sets. Data scientists use a variety of skills, including programming, statistics, and business acumen, to analyze and interpret raw data.

What skills do you need to become a successful data analyst?

Becoming a data analyst requires a variety of skills. You’ll need to be able to work with data, identify patterns, ask questions, and communicate your findings to a variety of stakeholders. Although data analysts may have different backgrounds and skill sets, they all have one thing in common — they’re experts at finding insights into data. Data analysts are often asked to:

Understand the business problem and find the right data to solve it: Data analysts must understand the business problem and know how to find the right data to solve it. Sometimes, data analysts must work with other teams to find the right data set.
Clean and transform data to be usable: Data analysts must know how to clean and transform unstructured and messy data into clean and usable data. They must be able to identify missing data and fill in the gaps.
Analyze data and identify patterns: Data analysts must be able to identify patterns and trends in their data sets — and then communicate those findings to their stakeholders.
Visualize data to make it understandable: Data analysts must be able to visualize their data sets to make them understandable for their stakeholders. They must be able to transform numbers and graphs into something that tells a story.

How to develop your data analysis skills

If you’re interested in becoming a data analyst, you’ll need to develop your data analysis skills. And luckily, it’s never been easier to learn these skills! You can develop your data analysis skills in several ways:

Start collecting and cleaning data: You can start building your data analysis skills by collecting and cleaning your own data sets. Collecting and cleaning your own data will help you understand the process of data analysis, and it will help you get used to working with unstructured data sets.
Practice your Excel skills: Excel is one of the most commonly used tools for data analysis. If you want to become a data analyst, you’ll need to practice your Excel skills. Most data analysts use Excel to clean their data sets and visualize their findings.
Invest in your data visualization skills: Data visualization is an essential skill for data analysts. It’s how we make sense of data and turn it into actionable insights. If you want to become a data analyst, you’ll need to invest in your data visualization skills.
Learn how to use a programming language: Data analysts often use programming languages to clean their data sets or to automate certain tasks. Some data analysts even use programming languages to clean their data sets. If you want to become a data analyst, you’ll need to learn how to use one or more programming languages.

Data analysis tools and software

There are many tools and software that data analysts use to perform their day-to-day work. This can include a wide variety of tools, from Excel and Google Sheets to Tableau and Python. If you’re interested in becoming a data analyst, you’ll need to familiarize yourself with these data analysis tools and software. Let’s take a look at some of the most important tools for data analysts:

Microsoft Excel: Excel is one of the most commonly used tools for data analysis. It’s commonly used for data visualization, cleaning data sets, and building models to help with predictions.
Tableau: Tableau is a data visualization tool that’s commonly used by data analysts to build data visualizations and dashboards.
Google Sheets: Google Sheets is a data visualization tool that’s commonly used by data analysts to build data visualizations and dashboards.
Python: Python is a programming language that’s commonly used by data analysts. It’s often used to clean data sets and build models.
SQL: SQL is a database programming language that’s commonly used by data analysts.

How to find data analyst jobs

If you’re interested in becoming a data analyst, you’ll need to find data analyst jobs. The good news is that data analyst jobs are in high demand. According to data from the Bureau of Labor Statistics, there are approximately 207,000 data analyst jobs in the United States alone. And there are several ways to find data analyst jobs:

Start building your data analysis portfolio: Before you start applying for data analyst jobs, you’ll want to start building your data analysis portfolio. Your data analysis portfolio is a collection of your work: whether that be data visualizations, Excel models, or written analyses. Having a data analysis portfolio will make you stand out to employers, and it’ll help you get your foot in the door.
Apply to data analyst internships: If you’re fresh out of school, applying for data analyst internships is a great way to gain experience in the field. It’s also a great way to build your data analysis portfolio.
Attend data analyst meetups and networking events: Attend data analyst meetups and networking events. These events are great for meeting potential employers.