Data is becoming increasingly important in today’s world, and as a result, the demand for data analysts is on the rise. However, getting started in data analysis can be overwhelming, especially if you’re just starting out. Fortunately, there are many free resources available online that can help you get started. Here are our top 5 online courses for becoming a data analyst for free:
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1.Data Visualization in Excel
In today’s “Big Data” driven age, we need to cut through the noise and present important information in a way that can be consumed and acted upon quickly, making data visualization an increasingly important skill. Visualization not only needs to present data in an easy-to-understand and engaging way, it also needs to provide context for the data, tell a story, and strike a delicate balance of form and function. Excel has a lot of competitors in this space, but it’s still an excellent choice, especially if that’s where your data resides. It offers many tools for creating visualizations besides charts, and the available charting options are constantly expanding and improving so that newer versions now include waterfall charts, sunshine charts, and even map charts. But what sets Excel apart is its flexibility, it gives us complete creative control over our designs, allowing us to create our own animated custom charts when needed to tell a true story about our data.
SKILLS YOU WILL LEARN
Data visualization (DataViz)
2.Learn Python basics for data analysis
This course looks at how to organize and group information in your program by data type, use program logic to make your program do what you want, and use and write functions so you can save time writing programs.
is designed to provide you with the basics of the Python programming language, specifically for data analysis purposes.
3. Performing Confirmatory Data Analysis in R
Welcome to this project-based course, Perform Confirmation Data Analysis in R. In this project, you will learn how to perform large-scale confirmation data analysis, which is similar to performing inferential statistics in R.
At the end of this 2-hour project, you will understand how to perform the chi-square test, which includes conformity tests, independence tests, and homogeneity tests. You will also learn how to calculate correlations for numerical variables and perform regression analysis. You will also learn to interpret test results and make informed decisions. Additionally, you’ll learn how to inspect multiple built-in R datasets to perform various tests.
Learn Step by Step
- We are preparing to start
- The chi-square test: the goodness test
- Chi-Square Test: Independence Test
- Chi-Square Test: Test for homogeneity
- Analysis of Variance (ANOVA) – Part I
- Analysis of Variance (ANOVA) – Part II
- Kruskal-Wallis test
4Statistics for Data Science with Python
This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and techniques for analyzing data. After completing this course, you will have knowledge of key statistical topics including – data collection, summarizing data using descriptive statistics, viewing and visualizing data, exploring relationships between variables, probability distributions, expected values, testing hypotheses, introduction to ANOVA (analysis of variance) ), regression and correlation analysis. You’ll take a hands-on approach to statistical analysis using Python and Jupyter Notebooks—the tools of choice for data scientists and data analysts.
WHAT YOU WILL LEARN
- Write Python code to run a variety of statistical tests, including t-tests, ANOVAs, and regression analyses.
- Interpret the results of your statistical analysis after testing the hypothesis.
- Calculate descriptive statistics and visualizations by writing Python code.
- Create a final project demonstrating your understanding of various statistical tests using Python and evaluate your colleagues’ projects.
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5.Power BI for Beginners: Preparing Data For Analysis
Use Power BI to load, clean, transform, and prepare data for analysis. For example, a data analyst may be asked to create a report with a visual of several data sets that need to be cleaned and transformed to produce useful information.
SKILLS YOU WILL DEVELOP
Learn Step by Step
- Load data from multiple sources and navigate the PowerQuery editor
- Learn how to use PowerQuery’s data profiling capabilities to clean up messy data sets
- Load CSV file and clean data with data profiler tool
- Learn basic transformations with PowerQuery
- Learn how to combine data sets in PowerQuery
- Learn how to create calculated columns in PowerQuery
- Change data in PowerQuery
- Capstone: Prepare data for analysis with Power BI