6 Key Skills to Become A Data Analyst

Switching to a career in Data Analytics is a great opportunity for you to land high-paying jobs considering how the demand is growing tremendously. But, what are the skills needed for you to venture into Data Analytics?

Here are 6 Key Skills to Become A Data Analyst that you might want to know before you embark on your journey:

1. SQL (Structured Query Language)

A spreadsheet and computing tool that is capable of handling large sets of data and processing information much more quickly than more common spreadsheet software. SQL is a valuable tool for data analysts, so knowledge of its functionality is key.

2. Microsoft Excel

When you think of Excel, the first thing that comes to mind is likely a spreadsheet, but there is a lot more analysis power under the hood of this tool. While a programming language like R or Python is better suited to handle a large data set, advanced Excel methods like writing Macros and using VBA lookups are still widely used for smaller lifts and lighter, quick analytics.

If you are working at a lean company or startup, the first version of your database may even be in Excel. Over the years, the tool has remained a mainstay for businesses in every industry, so learning Excel is a must. Luckily, there is an abundance of great free resources online to help you get started, as well as structured data analytics classes for those looking for a deeper understanding of the tool.

Pro Tip: Excel is limited with big datasets, so learning a statistical programming language is often another must as you move up in your career as an analyst.

3. Statistical Programming

Statistical programming languages, like R or Python, enable you to perform advanced analyses in ways that Excel cannot. Being able to write programs in these languages means that you can clean, analyse, and visualise large data sets more efficiently.

Both languages are open source, and it’s a good idea to learn at least one of them. There’s some debate over which language is better for data analysis. Either language can accomplish similar data science tasks. While R was designed specifically for analytics, Python is the more popular of the two and tends to be an easier language to learn (especially if it’s your first).

Learn your first programming language. The ability to write programs in these languages means that you can clean, analyse, & visualise large data sets more efficiently. Python, R, and Go Language are excellent for beginners.

4. Machine Learning

Machine learning, a branch of artificial intelligence (AI), has become one of the most important developments in data science. This skill focuses on building algorithms designed to find patterns in big data sets, improving their accuracy over time.

The more data a machine learning algorithm processes, the “smarter” it becomes, allowing for more accurate predictions.

Data analysts aren’t generally expected to have a mastery of machine learning. But developing your machine learning skills could give you a competitive advantage and set you on a course for a future career as a data scientist.

5. Data Visualisation

After gathering, analysing and compiling the data, usually, data analysts must share their findings with the company. Creating an easy-to-understand data analysis visualisation is vital. Often, data analysts choose to use visuals like graphs or charts to help their colleagues understand what the data says quickly and clearly.

6. Probability and Statistics

Statistics refers to the field of math and science concerned with collecting, analysing, interpreting, and presenting data. That might sound familiar—it closely matches the description of what a data analyst does.

With a strong foundation in probability and statistics, you’ll be better able to:

  • Identify patterns and trends in the data
  • Avoid biases, fallacies, and logical errors in your analysis
  • Produce accurate and trustworthy results
Explore the world of Data Analytics and discover your potential to be an analyst TODAY!