Covering descriptive and inferential statistics, as well as hypothesis testing techniques and exercises, statistics puts the “scientist” in data scientist.
with Iliya ValchanovStart Course
Statistics is the driving force in any quantitative career. It is the fundamental skill that a data scientist needs in order to understand and design statistical tests and analysis using modern software packages and programming languages. In this course, we start from the very basics and gradually build your statistical thinking. This, in turn, enables you to understand the more complex analyses carried out later in the program.
Skills you will gain
What You'll Learn
This course begins with the very basics of statistics and builds up your arithmetic thinking. It gradually teaches you how to work with more complex analyses, statistical approaches, and hypothesis.
- Introduction Free2 Lesson 7 Min
- Descriptive Statistics Fundamentals Free13 Lesson 64 MinTypes of data and levels of measurement Free Levels of measurement Free Categorical Variables. Visualization techniques Free Numerical Variables. Frequency distribution table Free The histogram Free Cross table and scatter plot Free Mean, median, mode Free Skewness Free Variance Free Standard deviation and coefficient of variation Free Covariance Free Correlation Free Practical Example - Descriptive Statistics Free
- Inferential Statistics Fundamentals7 Lesson 22 Min
- Confidence Intervals11 Lesson 55 MinDefinition of confidence intervals Population variance known, z-score Confidence Interval Clarifications Student's T Distribution Population variance unknown, t-score Margin of error Confidence intervals. Two means. Dependent samples Confidence intervals. Two means. Independent samples (Part1) Confidence intervals. Two means. Independent samples (Part2) Confidence intervals. Two means. Independent Samples (Part 3) Practical Example - Confidence Intervals
- Hypothesis testing11 Lesson 53 MinNull vs Alternative Further Reading on Null and Alternative Hypothesis Rejection region and significance level Type I error vs type II error Test for the mean. Population variance known p-value Test for the mean. Population variance unknown Test for the mean. Dependent samples Test for the mean. Independent Samples (Part 1) Test for the mean. Independent Samples (Part 2) Practical Example - Hypothesis Testing
“Statistics is the foundation of any quantitative career. This course is where you start. And it is the perfect beginning! In no time, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations.”
Co-founder at 365 Data Science
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