Businesses today generate enormous amounts of data. And highly skilled Data Analysts are key to making sense of all that data and helping businesses make informed decisions. If you love solving problems, diving into data and spreadsheets, and formulating conclusions and insights, then a career in data analysis may be the right fit for you.
This article will show you what a Data Analyst does and how you can get started by learning the skills you need to succeed. Specifically, we’ll explain the best data analysis programming languages to learn if you’re just starting out, so you’ll know which skills to build.
What does a data analyst do?
You might be surprised to learn that quite a few businesses don’t know what to do with all the data they collect. From purchase records to website traffic to customer service logs, all the data collected can inform business decisions.
Data Analysts are the technical experts that know how to search through data, manipulate it, and analyze it for notable trends. They’re the ones who then create reports of their findings, which help business leaders make decisions based on hard data rather than gut feelings.
A Data Analyst can use specific programming languages to help them access and organize data to perform the necessary calculations.
The best data analysis programming languages
There are many programming languages out there, but if you are just starting out and have your heart set on a career as a data analyst, we recommend learning two languages to start: Python and SQL. And in our Data Analyst Career Path, you’ll learn both Python and SQL to equip you with the skills you’ll need to succeed in an entry-level position. If you’re already familiar with Python, then consider learning R for data analysis.
Of course, there will be additional languages and libraries you can learn to further your skills, but it’s a good idea to start with the basics and get familiar with these three first. And if you’re wondering about additional programming languages and skills, such as machine learning, a career in data science might be of interest to you. While there is a lot of overlap, Data Analysts and Data Scientists rely on different key skills to perform their tasks.
Python for data analysis
Both Python and R are programming languages frequently used for data analysis. Python is a multipurpose programming language that is compact and easy to read. The benefit of learning Python for data analysis is that you can then use the language for many other projects and purposes, like building websites, coding applications, and extracting information online.
When you learn to use Python for data analysis, you’ll also want to download libraries for that purpose. While Python is a beginner-friendly programming language, it does take some time to set it up correctly for data analysis. Here are the libraries you can look into for Python:
- Pandas
- NumPy
- Matplotlib
- Seaborn
- SciPy
- Statsmodels
If you’re not comfortable downloading libraries yet and aren’t sure where to start, you can begin with learning the latest version, Python 3. Then once you’ve gotten the basics down, our free course Getting Started with Python for Data Science will show you how to use the language for data science, and you can take your skills even further with our Analyze Data with Python skill path.
R for data analysis
R, on the other hand, was built for data analysis. It’s rather easy to pick up and start using without the need to install so many additional libraries, like in the case of Python. R lets you find patterns and trends within your data. You can also build statistical models and produce stunning visualizations for your results.
You can also download libraries for R that will save you from writing additional pieces of code for your data analysis. If you decide to learn R, you’ll also want to check out the following libraries packages:
- Tidyverse
- Dplyr
- Ggplot2
- Caret
Check out the Analyze Data with R Skill Path to get started.
SQL for data analysis
SQL is a programming language that lets you communicate with, search within, and extract data from relational databases. SQL is also easy to learn because it’s designed for a specific purpose, plus the syntax is intuitive. If you’re new to the language, you can take our online course in less than 10 hours.
For example, Jorge from dataPlor learned SQL with Codecademy in order to perform data analysis, even though it wasn’t part of his role at the time. He explains:
“I got started with SQL because I needed the ability to query my company’s database for sales sampling purposes. My initial goal was to swiftly pull useful data from our database using SQL. As I’ve developed more skills, I’ve created multiple, company-wide business intelligence dashboards, provided custom datasets for everyday operations, and assumed command of a variety of processes at the console level.”
Since then, Jorge has been promoted to a Data Analyst position, where he focuses on providing insights and intelligence from the masses of data stored in his company’s databases. If you’re considering a career as a Data Analyst, look into the Analyze data with SQL Skill Path.
SQL can be used for a number of crucial data analysis tasks, and once you have that foundation, you can take more advanced courses on how to analyze business data with SQL or transform tables with SQL.
How to start a career in data analysis
Now that you know the best data analysis programming languages to learn before applying for your first position in the field, you should also know that learning these languages doesn’t have to be a chore. You don’t need to get a degree in mathematics or statistics to become a Data Analyst. Instead, you can learn Python, R, and SQL through online courses.
If you want a more well-rounded approach to gaining the data analysis skills you need for a new career, you can follow the Data Analyst Career Path. On top of learning Python and SQL, you’ll also learn about data acquisition, statistics, and data visualization, as well as starting your own portfolio. You’ll even earn a certificate for all your hard work, which you can include on your resume.
And if you want the fastest path to a career in data analysis, check out our Business Intelligence Data Analyst career path. Not every business needs a data scientist, but they all need someone who knows how to pull insights from data – and we’ll show you how. With focus and commitment, you can be ready to start applying for BI data analysis jobs in a few months.
