Course Summary
The IBM Data Analyst Capstone Project is a hands-on project-based course that focuses on data analysis and visualization. Students will apply what they have learned in the previous courses of the IBM Data Analyst Professional Certificate program to complete a real-world capstone project.Key Learning Points
- Learn to apply data analysis and visualization techniques in a real-world project
- Gain experience working with real datasets and tools used by data analysts
- Collaborate with peers and receive feedback on your work
Related Topics for further study
Learning Outcomes
- Ability to apply data analysis and visualization techniques to real-world problems
- Experience working with datasets and tools used by data analysts
- Collaboration and feedback skills
Prerequisites or good to have knowledge before taking this course
- IBM Data Analyst Professional Certificate
- Familiarity with data analysis and visualization techniques
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Visualization with Tableau
- Applied Data Science Capstone
- Data Science Methodology
Related Education Paths
Related Books
Description
In this course you will apply various Data Analytics skills and techniques that you have learned as part of the previous courses in the IBM Data Analyst Professional Certificate. You will assume the role of an Associate Data Analyst who has recently joined the organization and be presented with a business challenge that requires data analysis to be performed on real-world datasets.
Outline
- Data Collection
- Course Introduction
- Introduction To Capstone Project
- Syllabus
- (Optional) Hands-on Lab 1: Review Of Accessing APIs
- Hands-on Lab 2: Collecting Data Using APIs
- Hands-on Lab 3: Review Of Web Scraping
- Hands-on Lab 4: Collecting Data Using Web Scraping
- Hands-on Lab 5: Explore the Data Set
- Graded Quiz: Collecting Data
- Graded Quiz: Web Scraping
- Graded Quiz: Exploring Data
- Data Wrangling
- Assignment Overview
- Jupyter Notebook For Labs In This Module
- Hands-on Lab 8: Finding Duplicates
- Hands-on Lab 9: Removing Duplicates
- Hands-on Lab 6: Finding Missing Values
- Hands-on Lab 7: Imput Missing Values
- Hands-on Lab 10: Normalizing Data
- Graded Quiz: Duplicates
- Graded Quiz: Removing Duplicates
- Graded Quiz: Missing Values
- Graded Quiz: Imputing Missing Values
- Graded Quiz: Normalizing Data
- Exploratory Data Analysis
- Assignment Overview
- Jupyter Notebook For Labs In This Module
- Hands-on Lab 11: Finding How The Data Is Distributed
- Hands-on Lab 12: Finding Outliers
- Hands-on Lab 13: Finding Correlation
- Graded Quiz: Distribution
- Graded Quiz: Handling Outliers
- Graded Quiz: Correlation
- Data Visualization
- Assignment Overview
- Jupyter Notebook For Labs In This Module
- Hands-on Lab 14: Histograms
- Hands-on Lab 15: Box Plots
- Hands-on Lab 16: Scatter Plots
- Hands-on Lab 17: Bubble Plots
- Hands-on Lab 18: Pie Charts
- Hands-on Lab 19: Stacked Charts
- Hands-on Lab 20: Line Charts
- Hands-on Lab 21: Bar Charts
- Graded Quiz: Visualizing Distribution Of Data
- Graded Quiz: Visualizing Relationship
- Graded Quiz: Visualizing Composition of Data
- Graded Quiz: Visualizing Comparison of Data
- Building A Dashboard
- Assignment Overview
- Module 5 Assessment Information
- Final Assignment: Present Your Findings
- Elements Of A Successful Data Findings Report
- Best Practices For Presenting Your Findings
- Assignment Overview
- Exercise: Preparing Your Presentation
- Congratulations and Next Steps
- Course Credits and Acknowledgemen
Summary of User Reviews
Discover IBM Data Analyst Capstone Project course on Coursera. Read user reviews and feedback about this course. Learn about the overall rating and one key aspect that many users thought was good.Key Aspect Users Liked About This Course
real-life project experiencePros from User Reviews
- Great course for gaining real-life project experience
- Instructors provide clear and concise instructions
- Course material is up-to-date and relevant
Cons from User Reviews
- Some users found the course content to be too easy
- The course may be too technical for beginners
- Limited interaction with other students
Ramesh Sannareddy
4.6 Raiting
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