Data Science Master Class

Next Schedule Start Date (Live-Virtual):

“Live-Virtual Class. 5 Months. Start Date: March 27th
* Fridays: 8pm-10pm
* Saturdays (First Session): 8am-10am
* Saturdays (Second Session): 6pm-8pm”

1,200,000 Naira

(includes training and materials, certification exam fee)

Training Delivery Mode:

Course Overview

The Data Science Master Class is a comprehensive program designed to equip participants with the theoretical knowledge and practical
skills required to excel in data science. This course covers essential concepts such as data analysis, machine learning, data visualization, and real-world applications across multiple domains, making it ideal for aspiring data scientists and professionals aiming to upskill.

Course Objectives

Understand the fundamentals of data science and its practical applications.
Master the tools and techniques for data collection, preprocessing, analysis, and visualization.
Build predictive models using machine learning algorithms.
Develop hands-on expertise with Python, R, SQL, and data visualization platforms.
Apply data science solutions to solve real-world problems across various industries.

Learning Outcome

By the end of the course, participants will:

Demonstrate proficiency in programming for data science using Python and R.
Perform data manipulation and exploratory data analysis (EDA).
Build and evaluate machine learning models, including supervised and unsupervised techniques.
Create compelling data visualizations and dashboards using tools like Tableau and Power BI.
Apply domain-specific data science techniques to industries such as healthcare, finance, and marketing.
Complete a capstone project showcasing the ability to solve a real-world data science problem.

Course Module

Introduction to Data Science

Overview of Data Science and its Applications

Data Science Workflow

Career Paths in Data Science

Tools for Data Science

Data Science Foundation

Descriptive and Inferential Statistics

Probability Distributions

Linear Algebra and Calculus

Data Cleaning and Preprocessing

Exploratory Data Analysis (EDA)

Programming for Data Science

Python and R Basics

Essential Libraries (NumPy, Pandas, ggplot2)

SQL for Data Manipulation

Version Control – Git

Git Basics and Commands

Collaboration on GitHub

Best Practices for Version Control

Data Visualization and Storytelling

Visualization Principles

Python Libraries (Matplotlib, Seaborn, Plotly)

Dashboards with Tableau and Power BI

Machine Learning Fundamentals

Supervised and Unsupervised Learning

Regression Classification and Clustering Models

Model Evaluation Metrics

Advanced Machine Learning

Deep Learning with Neural Networks

Natural Language Processing (NLP)

Reinforcement Learning

Advanced Topics: GANs and Ensemble Methods

Domain Applications & Case Studies

Applications in Healthcare, Finance, Retail, Marketing, Sports, and more.

Capstone Projects

Real-World Problem Identification

Data Analysis, Model Building, and Evaluation

Final Report and Presentation

Mode of Delivery

Participants completing this course will be prepared for roles such as:

Data Scientist
Data Analyst
Machine Learning Engineer
Business Intelligence Analyst
AI Specialist
Data Engineer

Target Audience

Aspiring data scientists and analysts.
Professionals seeking to transition into data science roles.
Business professionals looking to leverage data for decision-making.
Students and researchers interested in data-driven solutions.

Certification

Participants will be guided to prepare for industry-recognized certifications such as:

PCEP-30-02: PCEP™ – Certified Entry-Level Python Programmer
DP-100: Microsoft Certified: Azure Data Scientist Associate