Course Summary
Learn to use the essential data analysis tools like Jupyter Notebooks, RStudio, and Github to perform data analysis and manipulation effectively.Key Learning Points
- Learn to use Jupyter Notebooks, RStudio and Github for data analysis and manipulation
- Understand and apply fundamental programming concepts in Python and R
- Learn to visualize data using matplotlib and ggplot2
Job Positions & Salaries of people who have taken this course might have
- USA: $62,453
- India: ₹5,83,014
- Spain: €25,000
- USA: $62,453
- India: ₹5,83,014
- Spain: €25,000
- USA: $113,309
- India: ₹10,89,131
- Spain: €30,000
- USA: $62,453
- India: ₹5,83,014
- Spain: €25,000
- USA: $113,309
- India: ₹10,89,131
- Spain: €30,000
- USA: $74,775
- India: ₹7,00,000
- Spain: €25,000
Related Topics for further study
Learning Outcomes
- Understand the essential tools used in data analysis
- Perform data manipulation using Python and R
- Visualize data using matplotlib and ggplot2
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming concepts
- Familiarity with Python and R
Course Difficulty Level
BeginnerCourse Format
- Online
- Self-paced
Similar Courses
- Python Data Analysis
- Data Analysis and Visualization
Related Education Paths
Related Books
Description
In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.
Outline
- Hypothesis Testing and ANOVA
- Lesson 1 - The role of probability in inference
- Lesson 2 - From sample to population
- Lesson 3 - Steps in hypothesis testing
- Lesson 4 - What is a p value?
- Lesson 5 - How to choose a statistical test
- Lesson 6 - Ideas behind ANOVA
- SAS Lesson 7 - ANOVA: Explanatory variable with 2 levels
- SAS Lesson 8 - ANOVA: Explanatory variables with more than 2 levels
- SAS Lesson 9 - Post hoc tests for ANOVA
- SAS Lesson 10 - ANOVA summary
- Python Lesson 7 - ANOVA: Explanatory variables with two levels
- Python Lesson 8 - ANOVA: Explanatory variables with more than 2 levels
- Python Lesson 9 - Post hoc tests for ANOVA
- Python Lesson 10 - ANOVA Summary
- Choosing SAS or Python
- Getting Started with SAS
- Getting Started with Python
- Course Codebooks
- Course Data Sets
- Uploading Your Own Data to SAS
- SAS Program Code for Video Examples
- Python Program Code for Video Examples
- Getting set up for the assignments
- Tumblr Instructions
- Example: Running an analysis of variance
- Chi Square Test of Independence
- Lesson 1 - Ideas behind the Chi Square test of independence
- SAS Lesson 2 - Chi Square Test of independence in practice
- SAS Lesson 3 - Post hoc tests for Chi Square tests of independence
- SAS Lesson 4 - Chi Square summary
- Python Lesson 2 - Chi Square test of independence in practice
- Python Lesson 3 - Post hoc tests for Chi Square tests of independence
- Python Lesson 4 - Chi Square summary
- SAS Program Code for Video Examples
- Python Program Code for Video Examples
- Example: Running a Chi-Square Test of Independence
- Pearson Correlation
- Lesson 1 - Pearson Correlation
- Lesson 2 - Correlation Example
- SAS Lesson 3 - Calculating Correlation
- Python Lesson 3 - Calculating Correlation
- SAS Program Code for Video Examples
- Python Program Code for Video Examples
- Exploring Statistical Interactions
- SAS Lesson 1 - Defining moderation, a.k.a. statistical interaction
- SAS Lesson 2 - Testing moderation in the context of ANOVA
- SAS Lesson 3 - Testing moderation in the context of chi square
- SAS Lesson 4 - Testing moderation in the context of correlation
- Python Lesson 1 - Defining moderation, a.k.a. statistical interaction
- Python Lesson 2 - Testing moderation in the context of ANOVA
- Python Lesson 3 - Testing moderation in the context of Chi-Square
- Python Lesson 4 - Testing moderation in the context of correlation
- A Question of Causation (Used with Permission from Annenberg Learner)
- SAS Program Code for Video Examples
- Python Program Code for Video Examples
Summary of User Reviews
Discover the latest data analysis tools and techniques with this comprehensive online course from Coursera. Students praise the course's practical approach and engaging instructors.Key Aspect Users Liked About This Course
The course offers practical tools and techniques for data analysis.Pros from User Reviews
- Engaging instructors who make the material easy to understand
- Practical approach that emphasizes real-world applications
- Comprehensive coverage of the latest data analysis tools and techniques
- Flexible learning options that allow students to work at their own pace
- Highly relevant course content that prepares students for real-world data analysis challenges
Cons from User Reviews
- Some students find the course content too basic or introductory
- Limited interaction with instructors or other students
- The course can be overwhelming for students who are new to data analysis
- The course may not be suitable for students who are looking for more advanced or specialized data analysis tools
- Some students feel that the course is too focused on theory and not enough on practical applications
Jen Rose
4.5 Raiting
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