ArewaDS website: https://arewadatascience.github.io
- Table of Contents (Arewa DS)
- Arewa Data Science and Machine Learning Curriculum!
The Arewa Data Science and Machine Learning Fellowship is a comprehensive, free program aimed at equipping aspiring data scientists and machine learning engineers with the skills and knowledge needed to excel in the field.
Our curriculum is carefully designed to guide participants through the basics of programming and data analysis to the more complex concepts of machine learning algorithms and applications. With a blend of theory and practical assignments, fellows engage in a hands-on learning experience that prepares them for real-world data science challenges.
- Structured curriculum covering Python, Data Science, and Machine Learning.
- Hands-on projects and challenges to apply learning in practical scenarios.
- Access to a community of mentors and peers for collaborative learning.
- Opportunities for real-world application through capstone projects.
Applications for Cohort 2.0 have now closed, but we welcome you to participate in our sessions and access our materials for self-study. Stay updated on future cohorts and get the latest information by following us on our social media pages. Additionally, join our Telegram group for regular updates and insights into our fellowship program.
- Website: Arewa Data Science Official Website
- Email: [email protected]
- Twitter | Facebook | LinkedIn | YouTube | Telegram
Welcome to the Arewa Data Science and Machine Learning Cohort 2.0 Fellowship!. Whether you're just starting or looking to deepen your existing skills, our fellowship offers a structured path to mastering the fundamentals and advanced concepts. We've organized the fellowship into three main parts:
- Stage 1: Getting Started - You'll learn the basics, set up your tools, and prepare for what's ahead.
- Stage 2: Data Science - This part covers data handling, from cleaning to analyzing.
- Stage 3: Machine Learning - Here, you'll dive into machine learning techniques and tools like Scikit-learn.
To graduate from the Arewa Data Science and Machine Learning Fellowship, fellows must meet the following criteria:
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Completion of Curriculum: Fellows must complete all modules within the curriculum, including the Python challenge, Data Science, Machine Learning sections, "Learning How to Learn," and "Writing in Science" courses.
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Assignments and Medium BlogPost: Submission of all required assignments and assigned blog post by the specified deadlines. Posts must meet the quality standards set by the mentors.
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Attendance: Maintain a 90% attendance rate for weekly office hours (Saturday and Sunday). See attendance list
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Capstone Project: Complete a capstone project that demonstrates the ability to apply learned skills to a real-world problem. The project must be approved by the ArewaDS Team.
You can find the list of accepted fellows, the mentor-mentee list, the recording of the kickoff event and the slides used during the presentation below.
| Component | Resource |
|---|---|
| Accepted Fellows Page | Visit the Accepted Fellows Page |
| Mentors/Mentee Info | mentor/mentee page |
| Communication (Telegram) | How to use Arewa Data Science Telegram Group |
| Kickoff Recording Ethiqueet | Link to Recording |
| Kickoff Slides | Link to slides |
We are excited to have you on board and can't wait to see the amazing things you'll accomplish.
| Duration: 6 weeks | attendance list |
The first part of our fellowship focuses on building a strong foundation in the essential tools used in Data Science and Machine Learning. We'll guide you through:
- Learning Strategies: Discover effective methods to enhance your learning and retention.
- Development Environment: Set up your development environment with tools like VSCode.
- Version Control: Learn to track and manage your code changes using Git and GitHub.
- Python Programming: Master the basics of Python, the language of choice for data analysis.
Before we delve into the technicalities, we'll explore techniques and strategies to enhance your learning process, helping you absorb and retain information more effectively.
Fellows are expected to complete the course "Learning How to Learn" from Coursera. Coursera offers financial aid. Below are the resources to get you started:
| Resource Description | Link |
|---|---|
| Learning How to Learn Course | Go to course |
| How to Apply for Coursera Financial Aid | Watch the video |
Kindly note that if you would like to get started immediately after applying for the financial aid, you would need to click on
Audit Courseat the final page of the application, otherwise, you would need to wait the two weeks for the financial aid application to be approved. In addition, while auditing the course, one can only watch the videos and learn without the ability to submit the graded quizzes or earn the certificate.
In this section, we'll cover how to set up your development environment using Visual Studio Code (VSCode), including how to use Jupyter notebooks within it. We'll also dive into using a python virtual environment, Git for version control, GitHub for collaboration, and Markdown for documentation.
We have provided detailed instructions, but you might not understand all the details of the setup for now. It will become clearer as you proceed with the course. So don't despair, put on your patience hat, and ask for help when needed. There's light at the other end of the tunnel.
| Title | Resource | Recording | Mentor |
|---|---|---|---|
| Initial Setup | MacOS | Windows | Linux | Tutorial | Dr. Idris |
| Basic Command Line Operations | CommandLine | recording | Dr. Idris |
| Setup Git and GitHub | Git/GitHub | Recording2 | Recording1 | Dr. Idris |
| Python Virtual Environments | Virtual Enviroment | Recording | Dr. Shamsuddeen |
| VSCode for DataScience | VScode for DS | Recording | Dr. Shamsuddeen |
| Introduction to Markdown | Markdown | Recording | Dr. Shamsuddeen |
| Customizing GitHub profile | Customizing Profile | Recording | Lukman |
| Working with GitHub in VS Code | GitHub in VS Code | TBD | |
| GitHub for Collaboration | Advaced GitHub | Dr Idris | |
| Google Colab | Google Colab | Recording | Dr. Idris |
| Python Functions and Modules | Functions and Modules | Recording | Dr. Shamsuddeen |
| Generative AI | TBD | ||
| Learning Programming with ChatGPT | TBD |
Over the course of 30 days, you'll learn Python basics, advanced features, and everything in between. Fellows are expected to practice and submit assignments for each day via Github repository.
Here's the challenge you'll be undertaking:
| Day | Content | Link |
|---|---|---|
| 1-30 | 30 Days of Python Challenge | Start the Course |
Duration: 6 weeks | attendance list |
The second part of the fellowship is all about Data Science. You'll learn to clean, visualize, and analyze data, which are key steps in any data science project. In addition to the technical skills, fellows are expected to complete the "Writing in the Sciences" course on Coursera to hone their ability to communicate scientific findings effectively.
Recommended Reading: Atomic Habit and Deep Work.
| Date | Learning Objectives and Topics | Lesson Resources | Recording | Mentor |
|---|---|---|---|---|
| 20/01/2024 | Blogging using Medium: Learn how to start writing blogs using Medium | How to write Medium Article | Recording | Lukman |
| 20/01/2024 | Learn the basic concepts behind data science and its relationship with AI, machine learning, and big data. | Introduction to Data Science | Dr Ibrahim | |
| 21/01/2024 | Introduction to data classification and common data sources. | Understanding Data Types | Dr Ibrahim | |
| Data Preparation | Working With Data: Techniques for cleaning and transforming data to address challenges like missing or inaccurate data. | Data Preparation Techniques | ||
| Visualizing Quantities | Learn to use Matplotlib to visualize data, such as bird populations. | Visualizing with Matplotlib | ||
| Visualizing Distributions of Data | Visualize observations and trends within intervals. | Data Distributions Visualization | ||
| Visualizing Proportions | Visualize discrete and grouped percentages. | Proportions Visualization | ||
| Visualizing Relationships | Visualize connections and correlations between datasets and variables. | Relationships Visualization | ||
| Meaningful Visualizations | Create valuable visualizations for effective problem-solving and insights. | Creating Meaningful Visualizations | ||
| Communication | Present insights from data in an understandable way for decision-makers. | Data Science Communication |
Duration: 8 weeks
In the final part of the fellowship, we'll focus on Machine Learning. You'll learn about different algorithms and how to implement them using popular libraries like Scikit-learn.
| Topic | Learning Objectives | Lesson Resources | Mentor |
|---|---|---|---|
| Introduction to Machine Learning | Learn the basic concepts behind machine learning. | Lesson | TBD |
| The History of Machine Learning | Learn the history underlying this field. | Lesson | TBD |
| Techniques for Machine Learning | Discover the techniques ML researchers use to build ML models. | Lesson | TBD |
| Introduction to Regression | Get started with regression models using Python and Scikit-learn. | Lesson | TBD |
| North American Pumpkin Prices 🎃 | Visualize and clean data; build linear, polynomial, and logistic regression models. | Lesson | TBD |
| Introduction to Classification | Introduction to data cleaning, preparation, and visualization for classification. | Lesson | TBD |
| Delicious Asian and Indian Cuisines 🍜 | Learn about classifiers; build a recommender web app using your model. | Lesson | TBD |
| Introduction to Clustering | Learn about clustering and data visualization. | Lesson | TBD |
| Exploring Nigerian Musical Tastes 🎧 | Explore the K-Means clustering method with music data. | Lesson | TBD |
| Introduction to Natural Language Processing ☕️ | Learn the basics of NLP by building a simple bot. | Lesson | TBD |
| Common NLP Tasks ☕️ | Understand common tasks in NLP dealing with language structures. | Lesson | TBD |
| Translation and Sentiment Analysis |
Perform translation and sentiment analysis with literary texts. | Lesson | TBD |
| Romantic Hotels of Europe |
Conduct sentiment analysis with European hotel reviews. | Lesson | TBD |
| Introduction to Time Series Forecasting | Learn the basics of time series forecasting. | Lesson | TBD |
| Introduction to Reinforcement Learning | Get introduced to reinforcement learning with Q-Learning. | Lesson | TBD |
| Introduction to Kaggle | Learn how to participate in Kaggle competition | Lesson | TBD |
After completion of our program, we offer career services to support you as you make the pivotal transition from fellowship to career, ensuring you're well-equipped to navigate the competitive job market and emerge as a standout candidate in the world of data science and machine learning.
- Career Advising: One-on-one mentorship sessions to plan your career trajectory.
- Resume/CV and LinkedIn Reviews: Tailored advice to polish your CV and professional profiles.
- Development of Portfolio Website: Learn to create a personal website to feature your bio, CV, projects, and professional accomplishments.
- Capstone Project Showcase: Strategies to highlight your project for employers and peers.
- Presentation Skills: Training to present your ideas and findings clearly.
- Alumni Network: Access to our alumni community for networking and support.
- Scholarship Guidance: Assistance with applications for educational and research funding.
- Academic Paper Writing Support: Resources and mentorship for collaborating, writing and publishing papers.
- Join HausaNLP Research Group: Engage with NLP research and contribute to Hausa language technology projects.
Arewa Data Science Fellowship