Data analysis. Power BI. SQL. These tools and skill sets have become popular over the past decade as organizations rely more on data to drive key business decisions and strategies.
Learning these skills has become more critical as data analytics-related skills become highly in demand across industries. Fortunately, there are many online courses to learn the fundamentals of data analysis, data preparation, and data visualizations. Here are some of the best ways you can learn them on your own.
What are the basic data analytic skills?
Fundamental data analysis does not necessarily mean you need to learn complex mathematics and specialized domain knowledge such as machine learning, linear regression, or artificial intelligence (unless your goal is to be a data scientist). You can do plenty of data analysis if you’re familiar with tools like Python and Excel.
Below, you’ll find the following skills are excellent for both data analysts aspirants and professionals looking to round out their analytical thinking abilities.
1. Data visualization
Data visualization, or the graphical representation of data, conveys a story with large data sets. Data analysts often use visualization tools like Tableau or Google Charts to create charts and graphs to communicate data.
Data visualization skills are critical for most major business functions. Marketing professionals who run campaigns, for example, are generally expected to present their results and key findings in an easily digestible manner.
Data scientists and analysts often use programming to compute complex equations or scrape data. But programming has other applications and uses, even if you aren’t trying to analyze massive data sets. One of the best uses of programming is to automate tasks.
The most popular programming languages for data science professionals are Python and R. Python is generally considered the most beginner-friendly programming language because of its easy-to-read syntax and ability to support many of today’s technologies.
“For beginners, I would suggest Python (or similar scripting languages) to start with. It is an easy language to pick up, highly productive, and also is useful in many practical situations such as data analysis,” said Dr. S.M. Yiu, professor at the Department of Computer Science of the University of Hong Kong.
This Python course provides a beginner-friendly introduction to Python for Data Science. Practice through lab exercises, and you'll be ready to create your first Python scripts on your own with IBM's Python Basics for Data Science.
According to Forrester Research, over 750 million people use Excel spreadsheets. While that number seems surprising, there are many reasons why spreadsheets have retained their popularity. Spreadsheets are versatile tools that can perform data manipulations, data processing, and even create data visualizations.
Build the fundamental knowledge required to use Excel spreadsheets to perform basic data analysis from IBM.
How to teach yourself data analytics skills: 3 easy steps
There are many resources and courses specifically for learning data analysis. But to demonstrate expertise, you must apply what you’ve learned to real-world problems.
Here are three steps you can take to learn data analysis on your own.
1. Learn fundamental mathematics
When starting data analysis from scratch, you’ll need to learn fundamental mathematics like statistics.
Gwen Britton, an instructor for the SNHUx's Business Analytics Foundations MicroBachelors® program, says that learning applied statistics is critical for telling stories with data.
“Statistics tells this story of how to describe what the data looks like or how these two things relate to each other or this is the trend and this is what we can anticipate in the future."
“Statistics tells this story of how to describe what the data looks like or how these two things relate to each other or this is the trend and this is what we can anticipate in the future,” said Britton. “Each of those different things really has an underlying story behind it that is really based on statistics."
2. Learn Python
Python is a beginner-friendly programming language that is often used by data professionals to build applications and machine learning pipelines. However, many developers and other tech professionals use Python for other types of projects, including web development purposes. This makes Python one of the most flexible and highly in-demand programming languages globally.
3. Solve business problems
The real added value to learning data analytics skills is to derive solutions to real-world problems that businesses are facing. Data analysis solves high-level problems such as figuring out how companies can generate more revenue, creating more efficiencies, or identifying ways to save money.
Of course, you do not need to start solving these complex problems right away. You can try tackling more minor business problems you encounter at work. Some examples of these include:
- Creating reports for management
- Building dashboards to automate manual data analysis
- Identifying trends within large data sets using tools like Excel
Learn core data analytics skills on edX
There are virtually no downsides to mastering data analysis skills. If you’re interested in learning data science fundamentals or want to add data analysis skills to launch your career as a data analyst, explore industry-aligned courses and programs on edX today.