HOLAAAAAAA ! Welcome to my repository for the Introduction to Data Science Specialization from Coursera. This repository includes materials, projects, and notes from the various courses I completed as part of this specialization.
Please note that this specialization was taken as part of the Professional Data Science Certificate.
- Specialization Overview
- Course Breakdown
- Quizzes and Assignments Breakdown
- Repository Structure
- Conclusion
- Contact
This specialization includes four courses that cover the essential aspects of data science, from foundational knowledge to practical applications.
It is if you are a:
- Beginner who wants to explore a career in data science.
- Professional in other fields who wants to transition into data science.
- Anyone looking to gain hands-on experience in Python, SQL, and data science tools.
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.
- Basic understanding of programming concepts.
- Familiarity with statistics and mathematics.
Upon completion of the specialization, you will receive a certificate that validates your skills and knowledge in data science.
- Certificate Title: Introduction to Data Science
- Issuer: IBM
- Platform: Coursera
- Language: English (and more)
- Duration: Approximately 1 month (self-paced)
- Type: Specialization Certificate
- Skills Covered: Data Science, Python, SQL, Data Science methodology, Big Data Concepts, and more.
| Course | Duration | Rating | Key Concepts | Skills You'll Gain | Technologies Used | Link |
|---|---|---|---|---|---|---|
| Course 1: What is Data Science? | 11 hours | 4.7/5 (72,391 ratings) | - Defining Data Science - Career paths in Data Science - Insights from professionals |
- Data Science - Big Data - Machine Learning - Deep Learning - Data Mining |
- Python - R |
Course Link |
| Course 2: Tools for Data Science | 18 hours | 4.5/5 (29,062 ratings) | - Data Science toolkits - Python, R, and SQL basics - Jupyter, GitHub, RStudio |
- Python Programming - RStudio - GitHub - Jupyter Notebooks |
- Python - R - SQL |
Course Link |
| Course 3: Data Science Methodology | 6 hours | 4.6/5 (20,342 ratings) | - Data science methodology - CRISP-DM - Evaluating models |
- Data Analysis - CRISP-DM - Data Mining |
- Python - R |
Course Link |
| Course 4: Databases and SQL for Data Science | 20 hours | 4.7/5 (20,440 ratings) | - SQL and Python for databases - SQL queries, DDL/DML - Advanced SQL techniques |
- SQL - Relational Databases - Python Programming - Cloud Databases |
- SQL - Python |
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:
- Certificate for Course 1: What is Data Science?
- Certificate for Course 2: Tools for Data Science
- Certificate for Course 3: Data Science Methodology
- Certificate for Course 4: Databases and SQL for Data Science
- Specialization Certificate: Introduction to Data Science
| Quiz | Status | Due Date | Weight | Grade |
|---|---|---|---|---|
| Defining Data Science | Passed | Aug 21, 11:59 PM +01 | 10% | 100% |
| What Data Scientists Do | Passed | Aug 23, 11:59 PM +01 | 10% | 100% |
| Big Data and Data Mining | Passed | Aug 26, 11:59 PM +01 | 10% | 100% |
| Deep Learning and Machine Learning | Passed | Aug 28, 11:59 PM +01 | 10% | 100% |
| Data Science Application Domains | Passed | Aug 30, 11:59 PM +01 | 10% | 100% |
| Careers and Recruiting in Data Science | Passed | Sep 2, 11:59 PM +01 | 10% | 100% |
| Case Study Quiz | Passed | Sep 4, 11:59 PM +01 | 10% | 100% |
| Final Exam | Passed | Sep 4, 11:59 PM +01 | 30% | 100% |
| Quiz | Status | Due Date | Weight | Grade |
|---|---|---|---|---|
| Data Science Tools | Passed | Sep 18, 11:59 PM +01 | 10% | 90% |
| Languages | Passed | Sep 18, 11:59 PM +01 | 10% | 90% |
| Libraries, APIs, Data Sets, Models | Passed | Sep 20, 11:59 PM +01 | 10% | 100% |
| Jupyter Notebooks and JupyterLab | Passed | Sep 25, 11:59 PM +01 | 10% | 100% |
| RStudio & GitHub | Passed | Sep 30, 11:59 PM +01 | 10% | 100% |
| Peer-graded Assignment | Passed | Oct 3, 11:59 PM +01 | 25% | 88% |
| Final Exam | Passed | Sep 30, 11:59 PM +01 | 25% | 100% |
| Quiz | Status | Due Date | Weight | Grade |
|---|---|---|---|---|
| From Problem to Approach | Passed | Sep 16, 11:59 PM +01 | 10% | 100% |
| From Requirements to Collection | Passed | Sep 16, 11:59 PM +01 | 10% | 80% |
| From Understanding to Preparation | Passed | Sep 18, 11:59 PM +01 | 10% | 100% |
| From Modeling to Evaluation | Passed | Sep 20, 11:59 PM +01 | 10% | 100% |
| From Deployment to Feedback | Passed | Sep 20, 11:59 PM +01 | 20% | 80% |
| Peer-reviewed Final Assignment | Passed | Sep 23, 11:59 PM +01 | 10% | 100% |
| Final Quiz | Passed | Sep 23, 11:59 PM +01 | 30% | 80% |
| Quiz | Status | Due Date | Weight | Grade |
|---|---|---|---|---|
| Basic SQL | Passed | Oct 9, 11:59 PM +01 | 10% | 100% |
| Relational DB Concepts and Tables | Passed | Oct 11, 11:59 PM +01 | 10% | 100% |
| Refining Your Results | Passed | Oct 14, 11:59 PM +01 | 10% | 95% |
| Functions, Multiple Tables, and Sub-queries | Passed | Oct 16, 11:59 PM +01 | 10% | 100% |
| Accessing Databases using Python | Passed | Oct 18, 11:59 PM +01 | 10% | 100% |
| Assignment | Passed | Oct 23, 11:59 PM +01 | 15% | 80% |
| Final Exam | Passed | Oct 23, 11:59 PM +01 | 35% | 100% |
| Honors Assignments (Optional) | ||||
| Views, Stored Procedures and Transactions | Passed | Oct 25, 11:59 PM +01 | -- | 100% |
| JOIN Statements | Passed | Oct 28, 11:59 PM +01 | -- | 100% |
| Advanced SQL for Data Engineers | Passed | Oct 28, 11:59 PM +01 | -- | 70% |
| Course | Grade | Status |
|---|---|---|
| Course 1: What is Data Science? | 100% | Passed all assessments |
| Course 2: Tools for Data Science | 95% | Passed all assessments |
| Course 3: Data Science Methodology | 88% | Passed all assessments |
| Course 4: SQL for Data Science | 96.5% | Passed all assessments |
This specialization helped me build a solid foundation in data science, and I am excited to apply these skills to real-world problems. Feel free to explore the repository and contact me with any questions or feedback.
Feel free to reach out if you have any questions:
Email: [email protected]
LinkedIn: Amal El Mahraoui