IBM - Statistics for Data Science with Python
- Offered byCoursera
Statistics for Data Science with Python at Coursera Overview
Duration | 12 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Statistics for Data Science with Python at Coursera Highlights
- Earn a shareable certificate upon completion.
- Flexible deadlines according to your schedule.
Statistics for Data Science with Python at Coursera Course details
- This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks ? the tools of choice for Data Scientists and Data Analysts.
- 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.
Statistics for Data Science with Python at Coursera Curriculum
Course Introduction and Python Basics
Welcome from your Instructors!
Python Packages for Data Science
Course Overview
(Optional) Basics of Jupyter Notebooks
Welcome to Statistics!
Types of Data
Measure of Central Tendency
Measure of Dispersion
Practice Quiz - Introduction to Descriptive Statistics
Introduction and Descriptive Statistics
Data Visualization
Visualization Fundamentals
Statistics by Groups
Statistical Charts
Introducing the teacher's rating data
Practice Quiz - Data Visualization
Data Visualization
Introduction to Probability Distributions
Random Numbers and Probability Distributions
State your hypothesis
Normal Distribution
T distribution
Probability of Getting a High or Low Teaching Evaluation
Alpha (?) and P-value
Standard Normal Table
Practice Quiz - Introduction to Probability Distribution
Introduction to Probability Distribution
Hypothesis testing
z-test or t-test
Dealing with tails and rejections
Equal vs unequal variances
ANOVA
Correlation tests
Practice Quiz - Hypothesis Testing
Hypothesis Testing
Regression Analysis
Regression - the workhorse of statistical analysis
Regression in place of t - test
Regression in place of ANOVA
Regression in place of Correlation
Practice Quiz - Regression analysis
Regression Analysis
Project Case: Boston Housing Data
Project Case Scenario
Overview of Project Tasks
Task 1: Become familiar with the dataset
Task 2: Create or Login into IBM cloud to use Watson Studio.
Task 3: Load in the Dataset in your Jupyter Notebook
Task 4: Generate Descriptive Statistics and Visualizations
Task 5: Use the appropriate tests to answer the questions provided.
Task 6: Share your Jupyter Notebook.
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