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Data Analysis Tools

Approx. 10 hours to complete

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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

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

Beginner

Course Format

Online
Self-paced

Similar Courses

Python Data Analysis
Data Analysis and Visualization

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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

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