Welcome to my GitHub repository for the Applied Data Science Specialization from IBM. Below is an overview of the specialization, course breakdown, and key projects completed during the program.
Please note that this specialization was taken as part of the Professional Data Science Certificate.
The Applied Data Science Specialization is a 5-course series offered by IBM on Coursera, designed for data science enthusiasts to gain practical experience with real-world data. Throughout the courses, I learned essential skills such as Python programming, data wrangling, data visualization, and predictive modeling, working on real-world data problems. Below, I present a detailed breakdown of the courses and key projects from the specialization.
This action-packed Specialization is for data science enthusiasts who want to acquire practical skills for real world data problems. If you’re interested in pursuing a career in data science, and already have foundational skills or have completed the Introduction to Data Science Specialization , this program is for you!
This 5-course Specialization will give you the tools you need to analyze data and make data driven business decisions leveraging computer science and statistical analysis. You will learn Python–no prior programming knowledge necessary–and discover methods of data analysis and data visualization. You’ll utilize tools used by real data scientists like Numpy and Pandas, practice predictive modeling and model selection, and learn how to tell a compelling story with data to drive decision making.
Through guided lectures, labs, and projects in the IBM Cloud, you’ll get hands-on experience tackling interesting data problems from start to finish. Take this Specialization to solidify your Python and data science skills before diving deeper into big data, AI, and deep learning.
However, this course may not be ideal if:
- You are already an advanced data scientist looking for more specialized or niche topics.
- You are seeking highly advanced techniques and algorithms beyond an introductory level.
Upon completion of the specialization, you will receive a certificate that validates your skills and knowledge in data science.
- Certificate Title: Applied Data Science
- Issuer: IBM
- Platform: Coursera
- Language: English (and more)
- Duration: Approximately 2 months (self-paced)
- Type: Specialization Certificate
- Skills Covered:
| Course Title | Duration | Rating | Key Topics | Skills Gained | Course Link |
|---|---|---|---|---|---|
| Python for Data Science, AI & Development | 25 hours | 4.6/5 (38,186 ratings) | Python fundamentals, data structures, APIs, web scraping | Python, Pandas, Numpy, Jupyter Notebooks | Course Link |
| Python Project for Data Science | 8 hours | 4.5/5 (4,292 ratings) | Build dashboards, work with real datasets | Data Analysis, Pandas, BeautifulSoup, Plotly | Course Link |
| Data Analysis with Python | 15 hours | 4.7/5 (18,423 ratings) | Data cleaning, exploratory analysis, regression models | Data Analysis, Regression, Pandas, Scikit-learn | Course Link |
| Data Visualization with Python | 20 hours | 4.5/5 (11,804 ratings) | Charts, advanced visualizations, dashboards | Matplotlib, Seaborn, Dash, Plotly | Course Link |
| Applied Data Science Capstone | 13 hours | 4.7/5 (7,154 ratings) | End-to-end data science project, model development | Data Wrangling, Machine Learning, SVM, Decision Trees | Course Link |
Note: After completing each course, learners receive a certificate of completion. Upon completing all four courses, learners receive the specialization certificate.
You can view my certificates for the individual courses and the overall specialization here:
- Course 1: Python for Data Science, AI & Development
- Course 2: Python Project for Data Science
- Course 3: Data Analysis with Python
- Course 4: Data Visualization with Python
- Course 5: Applied Data Science Capstone
Course 1: Python for Data Science, AI & Development
| Item | Status | Due | Weight | Grade |
|---|---|---|---|---|
| Module 1 Graded Quiz: Python Basics | Passed | Oct 7, 11:59 PM +01 | 15% | 100% |
| Module 2 Graded Quiz: Python Data Structures | Passed | Oct 11, 11:59 PM +01 | 15% | 80% |
| Module 3 Graded Quiz: Python Programming Fundamentals | Passed | Oct 16, 11:59 PM +01 | 15% | 90% |
| Module 4 Graded Quiz: Working with Data in Python | Passed | Oct 21, 11:59 PM +01 | 15% | 90% |
| Module 5 Graded Quiz: APIs and Data Collection | Passed | Oct 25, 11:59 PM +01 | 10% | 100% |
| Final Exam for the Course | Passed | Oct 25, 11:59 PM +01 | 30% | 100% |
| Total Grade | 94% |
Course 2: Python Project for Data Science
| Item | Status | Due | Weight | Grade |
|---|---|---|---|---|
| Extracting Stock Data Using a Python Library | Passed | Oct 7, 11:59 PM +01 | 20% | 66.66% |
| Extracting Stock Data Using a Web Scraping | Passed | Oct 7, 11:59 PM +01 | 20% | 100% |
| Analyzing Historical Stock/Revenue Data and Building a Dashboard | Passed | Oct 9, 11:59 PM +01 | 60% | 100% |
| Total Grade | 93.33% |
Course 3: Data Analysis with Python
| Item | Status | Due | Weight | Grade |
|---|---|---|---|---|
| Graded Quiz: Importing Data Sets | Passed | Oct 4, 11:59 PM +01 | 10% | 100% |
| Graded Quiz: Data Wrangling | Passed | Oct 7, 11:59 PM +01 | 10% | 80% |
| Graded Quiz: Exploratory Data Analysis | Passed | Oct 9, 11:59 PM +01 | 10% | 100% |
| Graded Quiz: Model Development | Passed | Oct 11, 11:59 PM +01 | 10% | 100% |
| Graded Quiz: Model Evaluation and Refinement | Passed | Oct 14, 11:59 PM +01 | 10% | 100% |
| Submit your Project and Review Others | Passed | Oct 16, 11:59 PM +01 | 14% | 90% |
| Final Exam | Passed | Oct 16, 11:59 PM +01 | 36% | 95% |
| Total Grade | 94.80% |
Course 4: Data Visualization with Python
| Item | Status | Due | Weight | Grade |
|---|---|---|---|---|
| Graded Quiz: Introduction to Data Visualization Tools | Passed | Oct 9, 11:59 PM +01 | 15% | 100% |
| Graded Quiz: Basic and Specialized Visualization Tools | Passed | Oct 11, 11:59 PM +01 | 15% | 80% |
| Graded Quiz: Advanced Visualizations and Geospatial Data | Passed | Oct 16, 11:59 PM +01 | 15% | 90% |
| Graded Quiz: Creating Dashboards with Plotly and Dash | Passed | Oct 18, 11:59 PM +01 | 15% | 80% |
| Final Assignment: Part 3 - Submission and Grading | Passed | Oct 21, 11:59 PM +01 | 25% | 100% |
| Final Exam: Data Visualization with Python - Timed Quiz | Passed | Oct 21, 11:59 PM +01 | 15% | 93.33% |
| Total Grade | 91.50% |
Course 5: Applied Data Science Capstone
| Item | Status | Due | Weight | Grade |
|---|---|---|---|---|
| Graded Quiz: Data Collection API with Webscraping | Passed | Oct 7, 11:59 PM +01 | 10% | 100% |
| Graded Quiz: Data Wrangling Quiz | Passed | Oct 9, 11:59 PM +01 | 10% | 75% |
| Exploratory Data Analysis using SQL | Passed | Oct 9, 11:59 PM +01 | 10% | 100% |
| Exploratory Data Analysis for Data Visualization | Passed | Oct 9, 11:59 PM +01 | 10% | 100% |
| Graded Quiz: Interactive Visual Analytics and Dashboard | Passed | Oct 11, 11:59 PM +01 | 10% | 100% |
| Graded Quiz: Predictive Analysis | Passed | Oct 11, 11:59 PM +01 | 10% | 100% |
| Peer Review: Submit your Work and Review your Peers | Passed | Oct 14, 11:59 PM +01 | 40% | 100% |
| Total Grade | 97.50% |