Ementorhub

What you'll learn

Write Python code to conduct various statistical tests including a T test, an ANOVA, and regression analysis.

Interpret the results of your statistical analysis after conducting hypothesis testing.

Calculate descriptive statistics and visualization by writing Python code.

Create a final project that demonstrates your understanding of various statistical test using Python and evaluate your peer's projects.

Skills you'll gain

Statistical Hypothesis Testing

Statistical Analysis

Descriptive Statistics

Pandas (Python Package)

Probability Distribution

Statistics

Regression Analysis

Probability & Statistics

Data Analysis

Data Visualization

Data Science

Jupyter

Matplotlib

Exploratory Data Analysis

Correlation Analysis

Probability

There are 9 modules in this course

At the end of the course, you will complete a project to apply various concepts in the course to a Data Science problem involving a real-life inspired scenario and demonstrate an understanding of the foundational statistical thinking and reasoning. The focus is on developing a clear understanding of the different approaches for different data types, developing an intuitive understanding, making appropriate assessments of the proposed methods, using Python to analyze our data, and interpreting the output accurately. This course is suitable for a variety of professionals and students intending to start their journey in data and statistics-driven roles such as Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers. It does not require any computer science or statistics background. We strongly recommend taking the Python for Data Science course before starting this course to get familiar with the Python programming language, Jupyter notebooks, and libraries. An optional refresher on Python is also provided. After completing this course, a learner will be able to: ✔Calculate and apply measures of central tendency and measures of dispersion to grouped and ungrouped data. ✔Summarize, present, and visualize data in a way that is clear, concise, and provides a practical insight for non-statisticians needing the results. ✔Identify appropriate hypothesis tests to use for common data sets. ✔Conduct hypothesis tests, correlation tests, and regression analysis. ✔Demonstrate proficiency in statistical analysis using Python and Jupyter Notebooks.

Introduction & Descriptive Statistics

Data Visualization

Introduction to Probability Distributions

Hypothesis testing

Regression Analysis

Project Case: Boston Housing Data

Final Exam

Other Resources