Data analyst and scientists are more in demand each year than there are people with the right skills to fill those roles. But what are the most in-demand skills in the world of data? Some trending data science skills represent those with the most searches and subscriptions by the community of 87 million learners worldwide as of December 2021. Start developing these skills to prepare for a new career in the fast-growing field of data analysis.
SQL is the standard language for communicating with databases. If you know SQL, you can update, organize, and query data stored in relational databases and modify data structures (schemas).
Since nearly all data analysts must use SQL to access data in a company’s database, it’s an essential skill to get a job. It is common for interviews with data analyst to involve a technical evaluation using SQL.
Fortunately, SQL is one of the easiest languages to learn.
Master SQL: Build proficiency in SQL even if you have no programming experience with UC Davis’s Learn SQL Basics for Data Science specialization. Work on four progressive SQL projects as you learn to analyze and explore data.
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2. Statistical Programming
With statistical programming languages like R or Python, you can perform advanced analysis in ways that Excel can’t. Being talented in writing programs in these languages means you can clean, analyze, and visualize large data sets more efficiently.
Both languages are open, and it’s a good idea to learn at least one. There is a debate about which language is the most suitable for data analysis, and both languages can perform similar data science tasks. While R was explicitly planned for analytics, Python is the more popular and tends to be a more accessible language to learn (especially if it’s the former).
Learn your first programming language: The University of Michigan’s Python for Everybody is a good place to start if you’ve never written code before. After writing your first artless program, you can start creating more complex programs to collect, clean, analyze, and visualize data.
3. Machine Learning
Machine learning, a division of artificial intelligence (AI), has become one of the most critical developments in data science. This skill focuses on creating algorithms to find patterns in large data sets and improve their accuracy over time.
The more data a machine learning algorithm procedures, the “smarter” it becomes, allowing for more accurate predictions.
Data analysts are generally not expected to master machine learning. But emerging your machine learning skills could give you a competitive edge and pave the way for a future career as a data scientist.
Getting Started with Machine Learning: Andrew Ng’s Machine Learning course at Stanford was one of the most popular courses in 2020. In this introductory course, you’ll learn about the best machine learning techniques and also how to apply them to problems.
4. Probabilities and Statistics
Statistics refers to the area of mathematics and natural sciences that deals with data collection, analysis, interpretation, and presentation. It may sound familiar to you – it’s exactly the description of what a data analyst does.
With a solid foundation in probability and statistics, you can:
- Identify patterns and trends in data.
- Avoid prejudice, errors, and logical errors in your analysis
- Get accurate and reliable results
Master modern statistical thinking – get a refresher with the University of London’s Probability and Statistics course. If you’ve already learned a bit of programming, learn how to apply your skills to statistical analysis with Data with Python from the University of Michigan or Statistics with R from Duke University.
5. Data Management
Data management refers to the practices of gathering, organizing, and storing data in an efficient, secure, and cost-effective manner. While some organizations have dedicated data management roles (data architects and engineers, database administrators, and information security analysts), data analysts often manage data to some degree.
Different companies use different data management systems. As you develop your skills, it can help you gain a thorough understanding of how databases work in both physical and cloud environments.
Learn about data engineering Get an overview of the modern data ecosystem with IBM’s Introduction to Data Engineering. Learn about data analysts, scientists, and also engineers’ roles in data management.
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