Overview

Are you interested in pursuing a career in data science or looking to enhance your data analysis skills? Our self-contained Data Science for Beginners course is a perfect opportunity to learn the fundamental concepts and skills of data science in a practical, hands-on manner.

This course is designed for beginners who have little to no prior experience in data science. You will learn how to use Python and popular data science libraries such as Pandas, NumPy, and Scikit-learn to clean, preprocess, analyze, and visualize data. You will also learn how to build machine learning models and deploy them.

• By participating in this course, you can expect to gain the following skills:

• Formulating a data science problem: You will learn the steps involved from stakeholder engagement to define a data science problem, to refinement and goal setting and relevant data collection.
• Data Cleaning and Preprocessing: You will learn how to clean and preprocess data to prepare it for analysis.
• Data Analysis and Visualization: You will learn how to analyze and visualize data to identify patterns and trends.
• Machine Learning Model Building: You will learn how to build machine learning models to solve real-world problems.
• Model Deployment: You will learn how to deploy your machine learning models using web frameworks.
• Collaborative Coding: You will learn how to work with others to write code collaboratively.
• Good Software Engineering Practices: You will learn how to follow good software engineering practices such as version control, modular code design, and unit testing.

By the end of the course, you will have the skills and knowledge needed to tackle data analysis and machine learning projects on your own. You will also have completed a capstone project, which will give you hands-on experience in applying the concepts you have learned.

Join the Data Science for Beginners course today to jumpstart your career in data science or take your data analysis skills to the next level!

Registering for this course

Registration for this course is now open! Registration will remain open until 23:59 Anywhere on Earth (AoE) June 23, 2023, providing ample time for interested participants to secure their spot. To register, please visit the application here and complete the online registration form. The process is quick and easy, and you will receive a confirmation email once your registration is complete. Classes for this course will be held on Mondays, Wednesdays and Friday from 18:00 to 20:00 UTC+0 via online meeting link to be provided to registered participants. The course will run for 5 weeks, starting on June 26, 2023, and ending on July 28, 2023. Please note that there are limited seats available, and registrations will be accepted on a first-come, first-served basis.

Don't miss out on this opportunity to kickstart your data science journey! Register now and join us for an engaging and comprehensive course that will equip you with the skills you need to excel in the field of data science. For any inquiries or further information, please contact us at . We look forward to welcoming you to our "Data Science for Beginners" course!

Course Payment

Payment for this course is required at the time of registration to secure your spot in the class. The course fee is $30 (NLE 600), which covers all course materials and resources. Payment can be made through Mobile Money (Orange Money - +232-75-460-610), PayPal or credit card or bank transfer. Please note that the course fee is non-refundable.

Course Prerequisites

The Data Science for Beginners course is designed for individuals who have little to no prior experience in data science. However, there are a few prerequisites that participants should meet to ensure they are prepared for the course:

• Motivation: We want you to bear in mind learning is fun albeit it can be challenging but that is why you are not alone. We have a community of like-minded learners and course staff to help support you in this journey. To successfully complete this course and get the best out of it, you have to be highly motivated. You need to dedicate some time during this bootcamp and practice the materials to build the necessary intuitions and muscle memory of the content covered.
• Access to a computer: You should have access to a computer with an internet connection and should be able to install Python and necessary packages such as NumPy, Pandas, and Scikit-learn. The steps to achieve all this will be covered in this course.

By meeting these prerequisites, you will be able to keep up with the course material and get the most out of it.

Detail Course Schedule

• Week 0: Elementary Math for Data Science

  Day 1: Basic Linear Algebra

  • Introduction to Vectors and Matrices
  • Vector Operations: Addition, Subtraction, Scalar Multiplication
  • Matrix Operations: Addition, Subtraction, Scalar Multiplication, Matrix Multiplication
  • Inverse and Transpose of a Matrix
  • Applications of Linear Algebra

  Resources:

  • Khan Academy's Linear Algebra course
  • Video lectures by Professor Gilbert Strang on Linear Algebra on MIT OpenCourseWare

  Day 2: Statistics and Probability Theory

  • Descriptive Statistics: Measures of Central Tendency (Mean, Median, Mode)
  • Measures of Dispersion (Range, Variance, Standard Deviation)
  • Probability: Basic Concepts, Probability Distributions (Normal), Bayes Theorem
  • Hypothesis Testing

  Resources:

  • Khan Academy's Statistics and Probability course
  • Video lectures on Probability and Statistics by Professor Blitzstein on Harvard OpenCourseWare

  Day 3: Calculus

  • Limits
  • Derivatives
  • Chain Rule
  • Applications of Derivatives (Maxima, Minima, Optimization)

  Resources:

  • Khan Academy's Calculus course
  • MIT OpenCourseWare Calculus Course

• Week 1: Introduction to Data Science

  Day 1: Introduction to Data Science

  • What is Data Science?
  • What is a Data Scientist?
  • What is the Data Science Process?
  • What is the Data Science Workflow?
  • What is the Data Science Pipeline?
  • What is the Data Science Toolkit?
  • What is the Data Science Venn Diagram?
  • What is the Data Science Maturity Model?
  • What is the Data Science Career Path?

  Resources:

  • What is Data Science?
  • What is a Data Scientist?
  • What is the Data Science Process?
  • What is the Data Science Maturity Model?
  • What is the Data Science Career Path?
  • Video: "What is Data Science?" by IBM Watson Analytics
  • Book: "Python for Everybody" by Charles Severance

  Day 2: Introduction to Python

  • Data types, variables, and operators in Python
  • Understanding data structures in Python: lists, tuples, dictionaries, and sets
  • Understanding control flow in Python: if, else, for, while, and try/except
  • Understanding functions in Python
  • Understanding classes in Python
  • Introduction to Jupyter Notebook
  • Python Virtual Environments

  Resources:

  • Khan Academy's Python course
  • Codecademy's Python course
  • DataCamp's Intro to Python for Data Science course
  • Article: "Python Data Types Tutorial" by Real Python

  Day 3: Functions and I/O Management in Python

  • Functions in Python
  • Reading and writing files in Python
  • Reading and writing data in different formats in Python

  Resources:

  • Article: "Python Functions Tutorial" by Real Python
  • Article: "Python File I/O Tutorial" by Real Python
  • DataCamp's Intro to Python for Data Science course
  • Article: "Python Data Types Tutorial" by Real Python

• Week 2: Data Manipulation, Analysis and Visualization

  Day 1: Introduction to NumPy and Pandas

  • Overview of NumPy and Pandas
  • Installation and Setup of NumPy and Pandas
  • Working with arrays and dataframes
  • Loading data from different sources (csv, excel, sql)
  • Selecting, Filtering and Sorting Data

  Resources:

  • Introduction to NumPy (Real Python)
  • Pandas Basics (Pandas)
  • NumPy and Pandas Cheat Sheet (DataCamp)

  Day 2: Data Cleaning and Preprocessing

  • Identifying and handling missing data
  • Removing duplicates
  • Dealing with outliers

  Resources:

  • Data Cleaning and Preprocessing (Real Python)
  • Dealing with Missing Data (DataCamp)
  • Handling Outliers (DataCamp)

  Day 3: Data Visualization with Matplotlib and Seaborn

  • Introduction to Data Visualization
  • Overview of Matplotlib and Seaborn
  • Creating basic plots, histograms, and scatter plots

  Resources:

  • Matplotlib Tutorial (DataCamp)
  • Seaborn Tutorial (Seaborn)

• Week 3: Machine Learning Fundamentals

  Day 1: Introduction to Machine Learning

  • Overview of Machine Learning
  • Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Applications of Machine Learning in various industries

  Resources:

  • What is Machine Learning (IBM)
  • What is Machine Learning? (Simplilearn)
  • Overview of Machine Learning Algorithms (Analytics Vidhya)

  Day 2: Supervised Learning

  • Introduction to Supervised Learning
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Support Vector Machines
  • Model evaluation metrics: R-squared, mean squared error, confusion matrix

  Resources:

  • Supervised Learning (IBM)
  • Regression vs Classification (Analytics Vidhya)
  • Linear Regression (DataCamp)
  • Logistic Regression (DataCamp)
  • Model evaluation metrics: R-squared, mean squared error, confusion matrix

  Day 3: Unsupervised Learning

  • Introduction to Unsupervised Learning
  • Clustering: K-Means and Hierarchical Clustering
  • Dimensionality Reduction

  Resources:

  • Unsupervised Learning (IBM)
  • Clustering (Analytics Vidhya)
  • K-Means Clustering (DataCamp)
  • Hierarchical Clustering (DataCamp)
  • Dimensionality Reduction (Analytics Vidhya)
  • Dimensionality Reduction (Scikit-learn)

  Day 4: Building and Evaluating Machine Learning Models

  • Model selection and training
  • Overfitting and underfitting
  • Model Evaluation
  • Cross-validation

  Resources:

  • Model Selection and Training
  • Overfitting and Underfitting (Analytics Vidhya)
  • Model Evaluation Metrics (Scikit-learn)
  • Cross-validation (Scikit-learn)

• Week 4: Capstone Project

  Day 1: Introduction to Capstone Project

  • Overview of the capstone project
  • Requirements for the capstone project
  • Project proposal guidelines and examples

  The capstone project

  The capstone project is a hands-on opportunity for you to apply the concepts and skills you have learned in the previous three weeks to a real-world data problem. You will work on a data analysis project that involves cleaning, preprocessing, and analyzing a given dataset, followed by building and evaluating a machine learning model to solve a particular problem.

  The capstone project will consist of the following steps:

  1. Data Collection: You will be given a dataset to work with or can propose their own dataset. The dataset should be relevant to the problem they want to solve.
  2. Data Cleaning and Preprocessing: You will clean the dataset and prepare it for analysis by handling missing values, dealing with duplicates, and scaling or transforming features as needed.
  3. Data Analysis and Exploration: You will perform exploratory data analysis (EDA) to gain insights into the data, visualize the data, and identify patterns and trends.
  4. Feature Engineering: You will identify relevant features for the machine learning model and engineer new features if needed.
  5. Model Building: You will build a machine learning model using scikit-learn or another Python library. You will evaluate the model's performance using appropriate metrics and tune the model if needed.
  6. Model Evaluation: You will evaluate the model's performance on the test set and compare it with the baseline model. You will also analyze the model's performance and identify areas for improvement.
  7. Model Deployment: You will deploy the model using Flask or other web frameworks, allowing others to use the model for predictions.
  8. Presentation: You will present your findings, including the problem they solved, the methodology used, the insights gained, and the machine learning model built, to the rest of the class.

  The capstone project will be an opportunity for you to showcase your skills and creativity, as well as to get hands-on experience in solving a real-world data problem. You will receive guidance and feedback from the us and have access to online resources to help them complete the project.

Guidelines for the capstone project

1. The Capstone project is mandatory and must be completed by each participant to receive a certificate of completion.
2. Participants must select a dataset relevant to their area of interest, subject to approval by the instructor.
3. Participants must document their work throughout the project and maintain a clean and well-organized repository on GitHub.
4. Participants must submit a final report that includes their findings, methodology, and any code or scripts used in the project.
5. Participants are encouraged to collaborate with other participants but must submit individual projects.
6. Participants must adhere to ethical guidelines for data collection, use, and analysis, and avoid misrepresenting or misinterpreting results.
7. Participants must follow good software engineering practices, such as modular code design, version control, and unit testing.
8. Participants must adhere to the project timeline and deadlines and seek assistance from the instructor or teaching assistants in case of any issues.
9. The final project must demonstrate proficiency in data cleaning, data analysis, and machine learning model building.
10. Participants will be evaluated based on the quality of their project, their ability to communicate their findings, and their adherence to the project guidelines.

By following these rules and guidelines, participants will be able to successfully complete their Capstone project and showcase their skills in data science and machine learning.

Datasets and resources to get started

Here are some datasets and resources to get you started on your capstone project:

Target Industries:

• E-commerce and retail
• Healthcare
• Finance and banking
• Marketing and advertising
• Education

Data Science Applications:

• Customer segmentation and targeting
• Fraud detection and prevention
• Predictive maintenance
• Recommender systems
• Sentiment analysis and opinion mining

Examples

1. Titanic Dataset: This dataset contains information on the passengers who were aboard the Titanic when it sank. It includes information on age, sex, class, and survival status. This dataset is often used for predicting survival outcomes based on various features https://www.kaggle.com/c/titanic/data
2. Iris Dataset: This dataset contains information on the petal and sepal lengths and widths of three different species of iris flowers. This dataset is often used for classification tasks and clustering analysis https://archive.ics.uci.edu/ml/datasets/iris
3. Wine Quality Dataset: This dataset contains information on the physicochemical properties of different wines, along with a quality rating. This dataset is often used for classification and regression tasks https://archive.ics.uci.edu/ml/datasets/wine+quality
4. Boston Housing Dataset: This dataset contains information on housing prices in Boston and various features that may influence those prices. This dataset is often used for regression analysis https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html
5. Breast Cancer Wisconsin (Diagnostic) Dataset: This dataset contains information on various features of breast cancer tumors and a binary classification of whether the tumor is malignant or benign. This dataset is often used for classification tasks https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
6. MNIST Handwritten Digits Dataset: This dataset contains a large number of images of handwritten digits, along with labels indicating the digit that each image represents. This dataset is often used for classification tasks, particularly image classification http://yann.lecun.com/exdb/mnist/
7. California Housing Dataset: This dataset contains information on housing prices in California and various features that may influence those prices. This dataset is often used for regression analysis https://www.kaggle.com/datasets/camnugent/california-housing-prices
8. Pima Indians Diabetes Dataset: This dataset contains information on various features of Pima Indian women and a binary classification of whether or not they have diabetes. This dataset is often used for classification tasks https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database
9. Adult Income Dataset: This dataset contains information on various features of individuals and a binary classification of whether their income is above or below $50,000 per year. This dataset is often used for classification tasks https://archive.ics.uci.edu/ml/datasets/adult
10. Bank Marketing Dataset: This dataset contains information on various features of individuals and whether or not they subscribed to a bank's marketing campaign. This dataset is often used for classification tasks and customer segmentation https://archive.ics.uci.edu/ml/datasets/bank+marketing

These datasets are widely used in beginner data science capstone projects and provide a great starting point for learning various data science concepts and techniques.