Data analysts and scientists are in more demand each year than there are qualified candidates to fill those positions. The US Bureau of Labor Statistics projects that analysts will have 23% more job vacancies between 2021 and 2031, a rate much greater than the country’s predicted average job growth of 5%.

But which abilities are most in demand in the digital world?

These top seven data science trends are those that Coursera’s 87 million global learners are searching for and enrolling in (as of December 2021). Start by honing these abilities to prepare for a new profession in data analysis, which is experiencing rapid growth.

1. SQL

Structured Query Language, or SQL, is the chosen language for communicating with databases. The ability to query, organize and update data in relational databases is made possible by knowing SQL (schema).

Most data analysts will need to use SQL to obtain data from a company’s database, making it an essential skill to master to get a job. In actuality, data analyst interviews usually include a technical SQL test.

Fortunately, one of the simpler languages to learn is SQL.

Develop SQL proficiency with the Learn SQL Basics for Data Science Specialization from UC Davis, even if you have no prior coding experience. Complete four progressive SQL projects as you learn how to examine and interpret data.

2. Statistical computing

Thanks to statistical programming languages like R or Python, you can run complex analyses in ways Excel cannot. You can more effectively clean, analyze, and visualise substantial data sets if you can create programs in these languages.

Learning at least one of the two open-source languages would be wise. Which language is superior for data analysis is a topic of some discussion. Similar data science tasks can be completed using either language. While Python is the more widely used of the two and is typically a more straightforward language to learn (especially if it’s your first), R was created specifically for analytics.

Learn your first programming language: Python for Everybody from the University of Michigan is an excellent place to start if you’ve never written code before. You can begin to create more complex programs used to gather, clean, analyze, and visualise data once you’ve written your first straightforward application.

3. Machine learning

Machine learning, a branch of artificial intelligence, is one of the most critical developments in data science (AI). This study focuses on developing algorithms designed to find patterns in massive data sets while gradually improving their accuracy.

A machine learning algorithm becomes “smarter” as it analyses more data, improving prediction accuracy.

In general, machine learning expertise is not expected of data analysts. Nevertheless, mastering machine learning techniques could provide you with a competitive edge and put you on the path to a future as a data scientist.

Start learning about machine learning with Andrew Ng’s Stanford course, which has received the highest ratings on Coursera. In this introductory course, you will learn about the most excellent machine-learning methods and how to use them to solve issues.

4. Statistics and probability

The subject area of math and science that deals with gathering, analyzing, interpreting, and presenting data is known as statistics. That may sound familiar since it roughly resembles what a data analyst does.

A solid understanding of probability and statistics will enable you to:

• Determine the data’s patterns and trends.
• Avoid including biases, logical fallacies, and other inaccuracies in your analysis.
• Produce reliable and accurate findings.

Understanding current statistical thinking The University of London’s Probability and Statistics course might help you brush up. If you’ve already mastered some programming, look at Statistics with Python from the University of Michigan or Statistics with R from Duke University to learn how to put your knowledge to use in statistical analysis.

5. Data administration

The techniques of gathering, organizing, and keeping data in a way that is effective, secure, and economical are referred to as data management. Data analysts frequently manage data in some way, even though some organizations have jobs such as data architects and engineers, database administrators, and information security analysts.

Data management systems are utilized differently by various businesses. It can be beneficial to thoroughly understand how databases function in both physical and cloud contexts as you build your skill set.

Learn about data engineering with IBM’s Introduction to Data Engineering, which introduces the contemporary data environment. Learn how engineers, scientists, and data analysts contribute to data management.

6. Visualizing statistical data

Only one part of the analytical process involves identifying insights into the data. Another crucial element is utilizing those insights to create a narrative that can aid in making wiser business decisions. Data visualization may be of assistance. Using graphs, maps, charts, and other visual representations of data, you may effectively explain your conclusions as a data analyst.

Learning visualization tools like Tableau is frequently necessary to advance your data visualization abilities. Using this software, you may turn your analysis into dashboards, data models, visualizations, and business intelligence reports.

The Data Visualization with Tableau Specialization from UC Davis will help you practice producing compelling visualizations of your data if you’re experienced working with data and data sets.

7. Econometrics

To understand economics, economists employ econometrics, which uses mathematical and statistical data models to predict future trends based on historical data. Data analysts looking for work in the finance industry, particularly at investment banks and hedge funds, need to be well-versed in econometrics.

Learn the three fundamental econometric techniques and put your newfound knowledge to use by enrolling in Erasmus University Rotterdam’s Enjoyable Econometrics course.

How to list talents as a data analyst on a resume

As you expand your toolkit as a data analyst, don’t forget to update your resume. Include a section titled “Skills,” where you state your top five data skills in a list of bullet points. During the interview, be prepared to discuss any talents you said on your CV.

Furthermore, it makes sense to use your abilities in the appropriate context. When mentioning data analysis projects or previous tasks, try to include a sentence outlining how you employed given expertise to complete a task (for example, “Wrote a Python script to scrape data using the official Twitter API” or “used Tableau to show product sales over time”).

Learn what data professionals believe hiring managers are looking for in data analysts.