ArewaDS website: https://arewadatascience.github.io

Table of Contents (Arewa DS)

• Table of Contents (Arewa DS)
• Arewa Data Science and Machine Learning Curriculum!
  • Key Features:
  • Interested in Joining the Fellowship?
  • Contact & Community:
  • Welcome to Cohort 3.0 ArewaDS Fellowship
  • Graduation Requirements
  • Stage 1: Getting Started
  • Stage 2: Data Science
  • Stage 3: Machine Learning
  • Career Services, Soft Skills and Mentorship

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.

Key Features:

• 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.

Interested in Joining the Fellowship?

• Applications for Cohort 3.0 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 Discord group for regular updates and insights into our fellowship program.

You can find the list of accepted fellows below.

Component	Resource
Accepted Fellows Page	Visit the Accepted Fellows Page
Communication channel (Discord)	Click here to join our Discord channel

Contact & Community:

• Website: Arewa Data Science Official Website
• Email: arewadatascience@gmail.com
• Twitter | Facebook | LinkedIn | YouTube | Telegram

Welcome to Cohort 3.0 ArewaDS Fellowship

Welcome to the Arewa Data Science and Machine Learning Cohort 3.0 Fellowship!. We've organized the fellowship into three main parts:

• Stage 1: Python Programming for Data Science - You'll learn the basics of pythons, set up your tools, and prepare for what's ahead. This stage is covered here
• 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.

Graduation Requirements

To graduate from the Arewa Data Science and Machine Learning Fellowship, fellows must meet the following criteria:

• 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.

• 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.

• 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.

Stage 1: Getting Started

• The first stage in this fellowship is Python Programming Fellowship

• This stage must be completed before progressing to the next one.

Stage 2: Data Science

Duration: 6 weeks

• 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.

• Recommended Reading: Python for Data Analysis and Python Data Science.

Learning Objectives and Topics	Lesson Resources	Recording	Mentor
Learn the basic concepts behind data science and its relationship with AI, machine learning, and big data.	Introduction to Data Science	Recording	Shamsuddeen Muhammad
Numpy	Numpy Notebook | numpy-100-exercise	Cohort1 | Cohort2	Lukman
Pandas.	Pandas Notebook | Pandas exerices	Recording	Lukman
Working With Data: Techniques for cleaning and transforming data to address challenges like missing or inaccurate data.	Data Preparation Techniques	Cohort1 |Cohort2	Dr Mahmud Ahmad
Introduction to Matplotlib.	Working with Matplotlib	Cohort2	Shamsudden Muhammad
Visualizing Quantities : Learn to use Matplotlib to visualize data, such as bird populations.	Quantities Visualizing	Recording
Visualizing Distributions of Data: Visualize observations and trends within intervals.	Data Distributions Visualization	Recording
Visualizing Proportions: Visualize discrete and grouped percentages.	Proportions Visualization	Recording
Visualizing Relationships : Visualize connections and correlations between datasets and variables.	Relationships Visualization	Recording
Meaningful Visualizations : Create valuable visualizations for effective problem-solving and insights.	Creating Meaningful Visualizations	Recording

Stage 3: Machine Learning

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.

Recommended Book:

• Machine Learning with PyTorch and Scikit-Learn Book and Youtube Series on this book chapters.

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems

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 Group	Lesson Resources	Recording	Mentor
Introduction to Machine Learning: Learn the basic concepts behind machine learning.	Introduction	Lesson Recording	TBD	Shamsuddeen, Falalu
The History of Machine Learning: Learn the history underlying this field.	Introduction	Lesson	TBD	Shamsuddeen, Falalu
Techniques for Machine Learning: Discover the techniques ML researchers use to build ML models.	Introduction	Lesson	TBD	Shamsuddeen, Falalu
Introduction to Regression: Get started with regression models using Python and Scikit-learn.	Regression	Lesson	Recording	Ibrahim, Falalu
North American Pumpkin Prices 🎃: Visualize and clean data; build linear, polynomial, and logistic regression models.	Regression	Lesson	Recording	Ibrahim, Falalu
Introduction to Classification: Introduction to data cleaning, preparation, and visualization for classification.	Classification	Lesson |Session Notebook	Recording	Shamsuddeen, Falalu
Delicious Asian and Indian Cuisines 🍜: Learn about classifiers; build a recommender web app using your model.	Classification	Lesson |Session Notebook	Recording	Shamsuddeen, Falalu
Introduction to Clustering: Learn about clustering and data visualization.	Clustering	Lesson	TBD	TBD, Falalu
Exploring Nigerian Musical Tastes 🎧: Explore the K-Means clustering method with music data.	Clustering	Lesson	TBD	TBD, Falalu
Introduction to Natural Language Processing ☕️: Learn the basics of NLP by building a simple bot.	Natural Language Processing	Lesson | Notebook	Recording	Idris, Falalu
Common NLP Tasks ☕️: Understand common tasks in NLP dealing with language structures.	Natural Language Processing	Lesson | Notebook	Recording	Idris, Falalu
Translation and Sentiment Analysis ♥️: Perform translation and sentiment analysis with literary texts.	Natural Language Processing	Lesson	TBD	TBD, Falalu
Romantic Hotels of Europe ♥️: Conduct sentiment analysis with European hotel reviews.	Natural Language Processing	Lesson	TBD	TBD, Falalu
Introduction to Time Series Forecasting: Learn the basics of time series forecasting.	Time Series	Lesson	Recording1 Recording2	Zahraddeen Karami Lawal, Falalu
Introduction to Reinforcement Learning: Get introduced to reinforcement learning with Q-Learning.	Reinforcement Learning	Lesson	Recording	Mustapha Abdullahi, Falalu
A guide to Upwork freelancing	Upwork	Lesson	Recording	Munzali Alhassan, Falalu
Completing DataCamp Data Scienec Certification	Data Science Certification	Lesson	Recording	Lukman, Falalu
Introduction to Kaggle: Learn how to participate in Kaggle competition	Kaggle	Lesson	TBD	TBD, Falalu
MLFlow**: Learn how to get started with MLflow	MLFlow	Lesson	TBD	TBD, Falalu

https://youtu.be/wWFHdIzWMp8

Career Services, Soft Skills and Mentorship

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