Course Summary
Learn how to use statistical methods to analyze data in Python. This course covers topics such as probability, statistical inference, regression analysis, and hypothesis testing.Key Learning Points
- Gain a strong foundation in statistical methods and their applications in data science.
- Learn how to use Python to analyze data and perform statistical analysis.
- Apply statistical techniques to real-world problems and data sets.
Job Positions & Salaries of people who have taken this course might have
- USA: $62,000
- India: ₹4,50,000
- Spain: €25,000
- USA: $62,000
- India: ₹4,50,000
- Spain: €25,000
- USA: $113,000
- India: ₹12,00,000
- Spain: €42,000
- USA: $62,000
- India: ₹4,50,000
- Spain: €25,000
- USA: $113,000
- India: ₹12,00,000
- Spain: €42,000
- USA: $70,000
- India: ₹6,00,000
- Spain: €30,000
Related Topics for further study
Learning Outcomes
- Understand statistical concepts and their applications in data science.
- Use Python to analyze data and perform statistical analysis.
- Apply statistical techniques to real-world problems and data sets.
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming language
- Familiarity with basic mathematical concepts such as mean, median, and standard deviation
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Science Math Skills
- Applied Data Science with Python
Related Education Paths
Notable People in This Field
- Nate Silver
- Andrew Gelman
Related Books
Description
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.
Outline
- Course Introduction and Python Basics
- Welcome from your Instructors!
- Python Packages for Data Science
- Course Overview
- (Optional) Basics of Jupyter Notebooks
- Introduction & Descriptive Statistics
- 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 (α) 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.
- Other Resources
- IBM Digital Badge
- Opt-in to receive your badge!
Summary of User Reviews
Explore the world of statistics for data science with Python in this comprehensive Coursera course. Users have rated this course highly for its informative content and practical applications.Key Aspect Users Liked About This Course
The real-life examples and hands-on exercises were highly praised by users.Pros from User Reviews
- In-depth coverage of statistical concepts and their applications in data science
- Practical exercises and real-world examples make the course engaging and interactive
- Well-structured course with clear explanations and easy-to-follow instructions
Cons from User Reviews
- Some users found the pace too slow or too fast
- Occasional technical glitches with the platform
- Limited interaction with instructors and other students
Murtaza Haider
4.6 Raiting
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