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7 In-Demand Data Analyst Skills to Get You Hired in 2023

 7 In-Demand Data Analyst Skills to Get You Hired in 2023


These seven trending data science skills represent those that are some of the most searched by Coursera’s community of million global learners. To prepare for a new career in the high-growth field of data analysis, start by developing these skills.   


Let’s take a closer look at what they are and how you can start learning them.



1. SQL

Structured Query Language, or SQL, is the standard language used to communicate with databases. Knowing SQL lets you update, organize, and query data stored in relational databases, as well as modify data structures (schema). 


Since almost all data analysts will need to use SQL to access data from a company’s database, it’s arguably the most important skill to learn to get a job. In fact, it’s common for data analyst interviews to include a technical screening with SQL. 


Luckily, SQL is one of the easier languages to learn.


2. 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, analyze, and visualize 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).



3. 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. 


4. Probability and statistics

Statistics refers to the field of math and science concerned with collecting, analyzing, 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 into your analysis


Produce accurate and trustworthy results


5. Data management

Data management refers to the practices of collecting, organizing, and storing data in a way that is efficient, secure, and cost effective. While some organizations will have roles dedicated to data management—data architects and engineers, database administrators, and information security analysts—data analysts often manage data in some capacity.


6. Statistical visualization

Gleaning insights from data is only one part of the data analysis process. Another fundamental part is telling a story with those insights to help inform better business decisions. That’s where data visualization comes in. As a data analyst, you can use charts, graphs, maps, and other visual representations of data to help present your findings in an easy-to-understand way. 


7. Econometrics

With econometrics, analysts apply statistical and mathematical data models to the field of economics to help forecast future trends based on historical data. Understanding econometrics is key for data analysts looking for jobs in the financial sector, particularly at investment banks and hedge funds.


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