Statistics and Applied Data Analysis
About this course
A comprehensive understanding of statistics and data analysis is paramount for the fields of data science, data analytics, and machine learning. In the first course of this specialization, you will learn traditional and applied statistics (descriptive statistics, probability, and discrete and continuous probability distributions) from ground zero (i.e., beginner level). The second course is all about inferential statistics and making decisions (sampling distributions, one- and two-sample hypothesis tests, analysis of variance) and creating predictive mathematical models (linear and nonlinear regression). Throughout both of the first two courses, you will learn how to visualize data and solve various statistical problems using Microsoft Excel. In the final course of the specialization, you will use the statistical computing software R (using RStudio) for statistical hypothesis tests, data visualization, and analysis of variance (ANOVA).
90/100
CourseAsk score
- What the provider tells you
- 39/45
- Who stands behind it
- 35/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- Understand descriptive and inferential statistics
- Apply statistical concepts to real-world problems
- Perform data visualization using Excel and R
- Conduct hypothesis tests and regression analysis
Course objectives
- Develop a comprehensive understanding of statistics
- Learn to analyze and interpret data effectively
- Gain proficiency in using statistical software for data analysis
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