This repository serves as a personal documentation of my journey through DataCamp's Associate Data Scientist (Python Track). Here, I will track my progress, summarize key concepts, and share insights gained from each course and project. This README will be updated regularly to reflect my learning milestones.
The Associate Data Scientist (Python Track) by DataCamp is designed to equip learners with the foundational skills required to become a data scientist. The track covers essential topics such as data manipulation, visualization, statistical analysis, machine learning, and more, all using Python.
Below is a list of courses in the track, along with my progress and notes for each:
| Course | Status | Notes |
|---|---|---|
| Introduction to Python | Completed | Learned basics of Python syntax, Python Lists, Functions and Packages and Numpy. |
| Intermediate Python | Completed | Matplotlib, Dictionaries & Pandas, Logic, Control flow & filtering, Loops and Case study: Hacker Stastistics. |
| Data manipulation with Pandas | In Progress | Transforming DataFrames, Aggregating DataFrames, Slicing & Indexing DataFrames, and Creating & visualizing DataFrames |
| Python Data Science Toolbox (Part 2) | Not Started | Focuses on iterators, generators, and list comprehensions. |
| Importing Data in Python (Part 1) | Not Started | Learn to import and clean data from various sources. |
| Importing Data in Python (Part 2) | Not Started | Dive deeper into data importing and cleaning techniques. |
| Cleaning Data in Python | Not Started | Focus on handling missing data and data quality issues. |
| pandas Foundations | Not Started | Master the pandas library for data manipulation and analysis. |
| Manipulating DataFrames with pandas | Not Started | Learn advanced DataFrame manipulation techniques. |
| Merging DataFrames with pandas | Not Started | Explore how to combine and merge datasets. |
| Introduction to Data Visualization with Python | Not Started | Learn to create visualizations using Matplotlib and Seaborn. |
| Intermediate Data Visualization with Python | Not Started | Dive deeper into advanced visualization techniques. |
| Introduction to Statistics in Python | Not Started | Learn statistical concepts and their application in Python. |
| Supervised Learning with scikit-learn | Not Started | Explore machine learning algorithms for supervised learning. |
| Unsupervised Learning with scikit-learn | Not Started | Learn clustering and dimensionality reduction techniques. |
| Machine Learning with Tree-Based Models in Python | Not Started | Focus on decision trees, random forests, and gradient boosting. |
- Python Basics: Understanding Python syntax, data structures, and control flow is essential for any data science project.
- Data Manipulation: Mastering pandas is crucial for cleaning, transforming, and analyzing data.
- Data Visualization: Visualizing data helps in uncovering patterns and communicating insights effectively.
- Machine Learning: Learning supervised and unsupervised learning techniques is key to building predictive models.
Here are the projects included in the track, along with my progress and reflections:
| Project | Status | Reflections |
|---|---|---|
| Investigating Netflix Movies | In Progress | Will use foundational python skills[Introduction & Intermediate] to manipulate and visualize movie data. |
| Exploring NYC Public School Test Result Scores | Not Started | Will analyze and visualize educational data. |
| Exploring 67 Years of LEGO | Not Started | Dive into the history of LEGO sets and themes. |
| The GitHub History of the Scala Language | Not Started | Analyze the development of Scala through GitHub data. |
- Track Progress: Use the Courses and Progress table to see my journey through the track.
- Learn from Notes: Refer to the notes and key takeaways for summaries of each course.
- Explore Projects: Check out the projects section for detailed reflections and outcomes.
- Contribute: If you have suggestions or feedback, feel free to open an issue or pull request.
Happy learning! 🚀