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Data Science — Codar Tech Africa
School Of Data

Data Science

Data science is an interdisciplinary field that combines statistics, machine learning, programming, and domain knowledge to extract insights and knowledge from data. It involves data collection, cleaning, analysis, visualization, and predictive modeling to support decision-making and automation.

200+ enrolled 26 weeks Beginner – Advanced English Physical + Virtual + Hybrid

About this course

Data science is an interdisciplinary field that combines statistics, machine learning, programming, and domain knowledge to extract insights and knowledge from data. It involves data collection, cleaning, analysis, visualization, and predictive modeling to support decision-making and automation.

What you'll learn

Practical, job-ready technical experience
Exposure to real-world industry workflows
Confidence in building modern digital solutions
Mentorship from experienced professionals
Collaborative and project-based learning experience
Strong foundation for career growth in tech
Portfolio projects that demonstrate your capabilities
Professional development and workplace readiness skills

Who this course is for

  • Career switchers — bankers, teachers, marketers wanting to enter tech
  • Primary/Secondary Students, NYSC corps members & recent graduates
  • University students wanting practical skills before graduation
  • Self-taught devs ready to formalise & level up their skills
  • Working professionals targeting remote/freelance income in foreign currency

Requirements

  • A laptop (Windows, Mac or Linux) with at least 4GB RAM
  • Reliable internet connection (3G, 4G, 5G mobile data is fine)
  • 10–15 hours per week to dedicate to learning
  • No prior coding experience needed — we start from zero

Course curriculum

26 weeks · 48 classes · 208 hours of live lessons · portfolio projects

  • Introduction to Database
  • Understanding Databases
  • What is DBMS?
  • Why Use SQL?
  • Common Practice in Writing SQL Queries
  • Types of SQL Commands
  • SQL Basics
  • SELECT
  • WHERE
  • AS
  • ORDER BY
  • GROUP BY
  • HAVING

  • SQL Intermediate
  • SQL Joins
  • CASE Statement
  • SQL Functions

  • SQL Advance
  • Sub Queries
  • Window Functions
  • SQL CTEs

  • Data Modelling
  • SQL DDL
  • CREATE
  • DROP
  • SQL Datatypes
  • SQL Keys
  • SQL Views
  • SQL Stored Procedures

  • Data Cleaning with SQL
  • Handling Missing Data
  • Removing Duplicates
  • Standardizing Data
  • Parsing and Transforming Strings
  • Data Type Conversion

  • Exploratory Data Analysis with SQL
  • SQL Optimization

  • Basics Python for data science
  • setup and installation
  • Operators
  • Variables and data types
  • Indexing and Slicing

  • Data Structures in Python
  • Tuples
  • Sets
  • Dictionaries
  • Methods

  • Control Flow
  • Loops
  • Lists and List Comprehension

  • Functions
  • Functions and Parameters
  • Lambda Functions
  • Useful Built-in Functions

  • Functions
  • Functions and Parameters
  • Lambda Functions
  • Useful Built-in Functions

  • File Handlings
  • Handling Errors
  • Exception Handling
  • Connecting to Database
  • SQLITE
  • MYSQL

  • Object-Oriented Programming (OOP)
  • Classes and Objects
  • __init__ constructor
  • Instance vs Class Variables
  • Methods
  • Inheritance
  • Encapsulation and simple real-world examples

  • Data Collection
  • Web Scraping
  • APIs
  • Web Scraping with requests and BeautifulSoup
  • Working with APIs using requests

  • Statistics for Data Science Part 1
  • Statistics and its types
  • Data Types and Measurement levels
  • Random Variable Data type
  • Descriptive Statistics
  • Measure of Central Tendency
  • Measure of Dispersion
  • Frequency Distribution
  • Propability distribution

  • Statistics for Data Science Part 2
  • Skewness
  • Kuetosis
  • Graphical Representation
  • Histogram Plot
  • Box Plot
  • Bar Chart
  • 5 Number Summary
  • Detecting Outliers
  • Removing Outliers
  • Hypothesis Testing

  • Numerical Python with Numpy Library
  • Introduction
  • Creating Numpy Array
  • Array Datatypes
  • Array Manipulation
  • Data Manipulation with Pandas Library
  • Pandas DataFrame and Series
  • Reindexing
  • Iteration
  • Sorting
  • Aggregation
  • GroupBY
  • Merging/Joining
  • Concatenation
  • Filtering
  • Descriptive Statistics
  • Removing Duplicates
  • String Manipulation
  • Missing Data Handling

  • Data Visualization
  • Introduction to Matplotlib
  • Basic Plotting
  • Properties of plotting
  • About Subplots
  • Line plots
  • pie chart and Bar Graph
  • Histograms
  • Box and Violin Plot
  • Scatterplot
  • Seaborn Library

  • Exploratory Data Analysis (EDA)
  • Uni - Variate Analysis
  • Bi - Variate Analysis
  • Multi-Variate Analysis
  • More on Seaborn Based Plotting

  • Exploratory Data Analysis (EDA)
  • Uni - Variate Analysis
  • Bi - Variate Analysis
  • Multi-Variate Analysi
  • Time-Series Analysis

  • GIT/GITHUB
  • Installation
  • Introduction to Git & Github
  • Getting started on GitHub
  • Local Repository Workflow
  • Creating a Git repository
  • Creating and editing files
  • Adding files to your Git repository

  • Create a GitHub repo
  • Git Push
  • Git Pull
  • Understanding Branches
  • Working with Branches
  • Pull Requests
  • Merging and Pull Requests
  • The General Workflow

  • • Introduction to Machine Learning • Types of Machine Learning: Supervised Versus Unsupervised Learning • Simple Linear Regression • Estimating the Coefficients • Assessing the Intercept and Coefficient Estimates

  • • Multiple Linear Regression •Estimating the Regression Coefficients • OLS Assumptions • Multicollinearity • Feature Selection

  • Decision Trees (Rule Based Learning)

  • •EnsembleMethodsinTreeBasedModels•WhatisEnsembleLearning?•WhatisBootstrapAggregationClassifiersandhowdoesitwork?•RandomForest•Whatisitandhowdoesitwork?•VariableselectionusingRandomForest•Boosting:AdaBoost
  • GradientBoosting•Whatisitandhowdoesitwork?•HyperparameterandPro'sandCon's

  • • Classification Techniques • An Overview of Classification • Difference Between Regression and classification Models. • Logistic Regression • Logistic Regression • Evaluation Metrics for Classification Models

  • • DISTANCE BASED MODULES • K Nearest Neighbors • How does the KNN algorithm work? • How do you decide the number of neighbors in KNN?

  • • Support Vector Machines • Hard and Soft Margin Classification • Classification with Non-linear Decision Boundaries • Kernel Trick • Tuning Hyper parameters for SVM

  • • Unsupervised Learning • Why Unsupervised Learning • How it Different from Supervised Learning • The Challenges of Unsupervised Learning • Principal Components Analysis• K-Means Clustering

  • Introduction of Deep learning
  • Understanding Basic Neural Networks
  • AI vs ML vs DL
  • Artificial Neural Networks (ANNs)
  • Single-layer Perceptron (SLP)
  • Multi-layer Perceptron
  • Activation functions e.g Sigmoid Relu etc
  • Forward & Backward Propagation
  • Optimization functions
  • Learning Rate

  • Pytorch Framework
  • basic syntax
  • Tensors
  • Datatypes
  • Operations
  • Aggregate Functions
  • Indexing
  • Tensors on GPU

  • Pytorch Workflow Fundamentals
  • Neural Network for Regression
  • Neural Network for Classification
  • Build and train a simple fully connected NN on a toy dataset (e.g. XOR or Iris)
  • Evaluation metrics
  • accuracy
  • precision
  • recall
  • F1
  • Train/val/test splits
  • Model saving/loading (torch.save / torch.load)

  • Computer Vision with CNNs
  • CNN layers
  • Train a CNN on MNIST or CIFAR-10
  • PytorchDataLoader
  • transforms
  • TorchVision datasets

  • Natural Language Processing (NLP)
  • Applications and Techniques
  • Applications
  • Text Processing
  • Tokenizaton
  • Lemmatization
  • Stop Words
  • Vectorization
  • TF-IDF Vectorization

  • Sentimental Analysis
  • Transformers and LLM
  • Hugging Face

  • An Introduction to Power BI
  • The Building Blocks of Power BI
  • The Power BI Desktop Interface
  • Connecting to Data Sources with Power BI Desktop
  • Viewing All Connection Options
  • Managing Data Sources
  • Power BI workflow

  • Creating a Report with Visualizations
  • Interacting with Visualizations
  • Doing More with Visualizations
  • Changing the Visualization Type
  • Formatting Visualizations
  • Viewing Visualization Data
  • Creating Custom Dashboard

  • Data Loading and Data Transformation (Power Query Editor)
  • Removing nulls
  • renaming columns
  • filtering rows
  • Merging and appending queries (Joins)
  • Changing data types

  • Data Modeling and Relationships
  • Data modeling concepts: primary & foreign keys
  • Star schema vs Snowflake schema
  • Fact and Dimension Table
  • Managing Relationships
  • Using the Relationships

  • What is DAX?
  • Creating a New Table
  • Creating a New Calculated Column
  • Creating a New Measure
  • Managing Relationships
  • Using the Relationships View
  • Creating Relationships
  • Editing Relationships
  • Deleting Relationships
  • Using the Manage Relationships Dialog

  • Continuation on DAX (Part 2)
  • Creating Interactive Dashboards
  • Publishing
  • Capstone Project

  • Capstone Project and Examination

  • Capstone Project and Examination

  • Capstone Project and Examination

  • Capstone Project and Examination

  • Capstone Project and Examination

  • Capstone Project and Examination

Frequently asked questions

No. We start from absolute zero and build up gradually. As long as you can use a computer and you're committed to 10–15 hours/week, you'll succeed.

Both options are available. Pick from live online cohorts, our any of Physical campuses — It's the same content and certificate either way.

Yes. We offer part-payments — that can be split. Talk to admissions for tailored plans.

Yes, we provide internship and job placement support including CV review, mock interviews and direct introductions to several hiring partners. 85% of our graduates land a tech role within 6 months.
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  3. NOTE: After payment, you’ll be issued an admission letter and receipt. You’ll then be assigned to a cohort, and your class schedule will be shared with you.
Important:

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  • Internship Placement Assistance
  • Mentorship
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