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

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Arewa Data Science and Machine Learning Curriculum!

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 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.

Contact & Community:

Website: Arewa Data Science Official Website
Email: [email protected]
Twitter | Facebook | LinkedIn | YouTube | Telegram

Welcome to Cohort 2.0 ArewaDS Fellowship

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.

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.
Attendance: Maintain a 90% attendance rate for weekly office hours (Saturday and Sunday). See attendance list
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.

Fellowship Kickoff

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.

Stage 1: Getting Started

| 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.

Learning How to Learn

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 Course at 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.

Setup and Installation

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	Shamsudden
VSCode for DataScience	VScode for DS	Recording	Shamsudden
Introduction to Markdown	Markdown	Recording	Shamsudden
Customizing GitHub profile	Customizing Profile		Lukman
Working with GitHub in VS Code	GitHub in VS Code		TBD
GitHub for Collaboration	Advaced GitHub		Dr Ibrahim
Google Colab	Google Colab		Dr. Idris
Generative AI			TBD
Learning Programming with ChatGPT			TBD

30 Days of Python Challenge

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

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. 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.

Topic	Learning Objectives	Lesson Resources	Mentor
Defining Data Science	Learn the basic concepts behind data science and its relationship with AI, machine learning, and big data.	Introduction to Data Science	TBD
Defining Data	Introduction to data classification and common data sources.	Understanding Data Types	TBD
Data Preparation	Working With Data: Techniques for cleaning and transforming data to address challenges like missing or inaccurate data.	Data Preparation Techniques	TBD
Visualizing Quantities	Learn to use Matplotlib to visualize data, such as bird populations.	Visualizing with Matplotlib	TBD
Visualizing Distributions of Data	Visualize observations and trends within intervals.	Data Distributions Visualization	TBD
Visualizing Proportions	Visualize discrete and grouped percentages.	Proportions Visualization	TBD
Visualizing Relationships	Visualize connections and correlations between datasets and variables.	Relationships Visualization	TBD
Meaningful Visualizations	Create valuable visualizations for effective problem-solving and insights.	Creating Meaningful Visualizations	TBD
Communication	Present insights from data in an understandable way for decision-makers.	Data Science Communication	TBD

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.

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

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

fuadsaniibrahim / arewads-machine-learning Goto Github PK

arewads-machine-learning's Introduction

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