Statistics and Mathematics for Data Science and Data Analytics

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This course for absolute beginners provides you with the opportunity to systematically learn core statistical and probability concepts, descriptive statistics, hypothesis testing, regression analysis, analysis of variance (ANOVA), and advanced regression/ML methods such as logistics regressions, polynomial regressions, decision trees, and more.

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95 on-demand videos & exercises
Level: All Levels
11hrs 22mins
Access on mobile, web and TV

What to know about this course

If you aim for a career in data science or data analytics, this course will equip you with the practical knowledge needed to master basic statistics. You need good statistics and probability theory knowledge to become a data scientist or analyst.

The course begins with an introduction to descriptive statistics and explains the basics, including the mean, median, mode, and skewness. You will then learn more about ranges, interquartile range (IQR), samples and populations, variance, and standard deviation. The following section will explain distributions in detail, including normal distribution and Z-scores. Then, you will explore probability in detail, go over the Bayes theorem, the Central Limit theorem, the law of large numbers, and finally, Poisson’s distribution. Next, you will comprehensively explore linear regression and the coefficients of regression, mean square error, mean absolute error, and root mean square error. You will also explore hypothesis testing and type I and II errors in more detail and then learn comprehensively about the analysis of variance (ANOVA).

After completing this course, you will comprehensively acquire knowledge about statistical fundamentals, data analysis methods, decision-making processes, and machine learning concepts with examples. All resources are available at:

Who's this course for?

This beginner-level course has been niched to cater to an individual looking to master statistics and probability for data science and analysis, an individual looking to pursue a career in data science, or professionals and students wanting to understand statistics for data analysis.

The prerequisites for this course include absolutely no previous experience required and an eagerness and motivation to learn.

What you'll learn

  • Master basic statistics, descriptive statistics, and probability theory
  • Explore ML methods, including decision trees and decision forests
  • Learn probability distributions normal and Poisson distributions
  • Explore hypothesis testing, p-values, types I and II error handling
  • Master logistic regression, linear regression, and regression trees
  • Learn correlation, R-Square, RMSE, MAE, and coefficient of determination

Key Features

  • This course equips you with actual knowledge of statistics and probability when working with data
  • Learn from a mathematical expert/data scientist and balance theory and practice with real examples
  • Comprehensively master statistics and probability for actual data science and analysis methods

Course Curriculum

About the Author

Nikolai Schuler

Nikolai Schuler, as a data scientist and BI consultant, believes that the data world benefits from new tools and technologies, but it is extremely difficult to get trained in the field as practical courses with quality content are rare or are structured incompatible with a busy working life.
Nikolai's courses offer precious content and have an easy-to-follow structure. He aims to help anyone wishing to pursue their desired career by upgrading their data analysis skills. His courses have already found their audience in over 170 countries with numerous positive feedback and will equip you with the skillsets to master data science and analytics! If you are looking for qualitatively approachable training, then jump on board!

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