It’s often said that data is the “language” of business, because practically every company relies on data to inform decision-making. And you don’t have to be a Data Scientist or Business Intelligence Data Analyst to benefit from learning how to work with data. No matter what kind of job you have or field you work in, learning a few key data skills can help you increase your individual impact.

From healthcare providers who rely on data to measure patient outcomes, to teachers who track students’ performance with data, and even musicians who analyze streaming metrics to reach new audiences — there are so many ways you can combine your domain knowledge with data science. Curious where you should start? Here are six data skills that are relevant to all types of jobs or industries, plus the courses and paths to take to learn the techniques.

Programming in Python

Python is a wildly popular programming language for beginners, and it’s the go-to language for data analysis thanks to its handy pre-written libraries and frameworks. Once you learn Python’s English-like syntax, you can jump right in and use data science libraries like pandas, NumPy, and Matplotlib. 

You might be wondering: Why use Python when I’m already a pro at Microsoft Excel?Python is a little more robust than a no-code tool like Excel. With Python, you can work with larger datasets, do more advanced data analysis, and even incorporate machine learning. In our beginner-friendly course Getting Started with Python for Data Science, you’ll start working with real datasets right away.  

Being able to “talk numbers”

Some people can just effortlessly rattle off complex numbers and stats, while the rest of us feel like “the math lady meme” anytime we’re put on the spot. 

Luckily, there are tangible ways to become more data literate, which is a term used to describe how well you can read, understand, and leverage data. In our course Principles of Data Literacy, you’ll not only learn how to think (and talk) about data, but you’ll also uncover how data collection methods, data quality, and bias can make or break an analysis. 

Tidying, cleaning, and wrangling data

You might’ve heard the phrase “garbage in, garbage out” used in data science — basically, this means that your data-driven conclusions are only as good as the dataset you use. It’s important to make sure your raw data is clean before you can start analyzing it or building anything. This might seem tedious or boring, but Data Scientists actually spend most of their time cleaning, validating, and manipulating data.

Making raw data usable involves things like structuring your data so it’s tidy and organized, dealing with missing data, reshaping data, or labeling data properly. In our beginner-friendly course Principles of Data Literacy, you’ll learn how to clean data with the programming language Python. If you already know the Python basics, you can take our course How to Clean Data with Python to practice pulling and cleaning data from the web. 

Creating dashboards and data visualizations

If you spend a lot of time planning and perfecting slideshows and decks for your job, it’s worth it to learn how to create sophisticated and interactive data visualizations and dashboards. Data visualizations allow you to bring your data to life and tell an impactful story with data. 

Tableau is a very popular and user-friendly visual analytics platform that’s often used in business intelligence. In our free course Learn Tableau for Data, we’ll teach you how to make a compelling data dashboard with Tableau, and introduce you to storytelling techniques that will level-up your presentations. Or you can take our Business Intelligence Data Analyst career path to learn even more ways to visualize data (BTW, this path is the lowest-code way to get into data analytics).

If you already know some Python, data visualization is a great way to keep developing your programming skills. In the course Learn Data Visualization with Python, you’ll learn how to process, manipulate, and visualize data in Python with Matplotlib and Seaborn.

Using descriptive analysis

Often the first step in data analysis is to describe, summarize, and visualize our data so that patterns can come into focus, a process called “descriptive analysis.” In short, we’re drawing basic and surface-level conclusions about our data based on the initial trends and relationships we find. 

For example, maybe you’re measuring the mean, median, and mode of a dataset, or creating a chart to visualize the spread. If your job entails analyzing financial statements, that’s a form of descriptive analysis. Another example is conducting customer research through surveys and summarizing the findings. Teachers who measure students’ grades using GPAs are also technically using descriptive analysis. 

Descriptive analysis is often used as a baseline that informs the next steps to take with our data, but you can’t apply your findings from this step to other datasets (that’d require more detailed analysis). You can learn more about descriptive analysis and how it feeds into the other types of data analysis in the path Data Science Foundations. 

Statistical thinking

The word “statistics” might give you flashbacks to high school math class. In truth, statistical thinking is mostly about interpreting numbers and figuring out the story behind data. If you work with numbers and data, you should know how to think critically about statistics so you can make informed decisions about what to do with them.

Understanding numerical summary statistics, for example, helps us distill complex data down to a few numbers that provide insights about an entire dataset. In our course Learn Statistics with Python, we’ll walk you through how to use Python to calculate and interpret descriptive statistics using the library NumPy. Or you can check out our free course Statistics: Mean, Median, Mode to get briefed on basic summary statistics. You can take your Python skills a step further and learn how to summarize data using numerical statistics and data visualizations in our path Master Statistics with Python.

Ready to learn these data skills? Sign up for a Codecademy plan and start taking a course today. We recently added a new tier called Codecademy Plus that’s ideal for building specialized skills or creating your own learning roadmap.