COMMUNITY & ENGAGEMENT
📌 Private Student Community (Slack, Discord, or Facebook Group)
- Network with fellow students and instructors
- Participate in real-time challenges and discussions
- Get feedback on your projects
📌 Monthly Live Q&A with Instructors
- Ask questions & troubleshoot challenges in real time
Get personalized advice with one-on-one mentorship from experienced instructors. Whether you need help with projects, career advice, or technical support, we’re here for you.
:Available for a limited number of students
- Schedule a 30-minute session to address your challenges
Career Development & Job Search Tips
Prepare for your career transition with expert tips on job hunting, resume building, and LinkedIn optimization for data professionals.
- Job search strategies for data analysts
- How to tailor your resume for data roles
- LinkedIn optimization guide for analytics professionals
Our Curriculum
1. Welcome
Available in
days
days
after you enroll
- 1.1 Welcome!
- 1.2 Programme introduction and instructions
- 1.3 Programme Overview
- 1.4 Programme Outline
- 1.5 Meet the Faculty
- 1.6 Meet your programme learning facilitator
- 1.7 Live webinars and engagement information
- 1.8 Introduce Yourself
- 1.9 What you need to get started
- 1.10 Excelling as an online learner
- 1.11 Requirements to earn our Certificate of Preparation
- 1.12 Capstone project
- 1.13 The learning platform
- 1.14 Pre-programme survey
- 1.15 Frequently asked questions (FAQs)
- 1.16 Code of Conduct and Agreement
- 1.17 Resources
2. Excel For Data Analysis
Available in
days
days
after you enroll
- 2.1 Introduction to Excel
- 2.1 Data Entry, Cleaning, and Preprocessing, Filter, Sort, Trim
- 2.2 Create formulas to solve a problem
- 2.3 Charts and Graphs
- 2.4 Split and Concatenate
- 2.5 Excel Formula and Functions – Simple, complex, relative, and absolute references and functions
- 2.6 VLOOKUP
- 2.7 Pivot table
3. SQL - PostGres
Available in
days
days
after you enroll
- 3.1 SQL Basics
- 3.11 Introduction and Installation of tools and resources
- 3.12 Overview of PostgreSQL
- 3.13 RDBMS Concepts
- 3.14 Databases
- 3.15 Syntax
- 2.16 Data Types
- 3.17 Operators
- 3.2 Data Definition Language
- 3.21 Data Definition Language - Create Databases and Schema
- 3.22 Data Definition Language - Drop Database and Schema
- 3.23 Data Definition Language - Accessing Database and Schema
- 3.24 Data Definition Language - Create Table
- 3.25 Data Definition Language - Drop Table
- 3.26 Data Definition Language - Insert Query and Values
- 3.27 Assignment I
- 3.3 Data Manipulation Language
- 3.31 Data Manipulation Language - Select Query
- 3.32 Data Manipulation Language - Where Clause
- 3.33 Data Manipulation Language - AND & OR Clause
- 3.34 Data Manipulation Language - Update and Delete Query
- 3.35 Data Manipulation Language - Like and Top Clause
- 3.36 Data Manipulation Language - Order By and Group BY
- 3.37 Data Manipulation Language - Distinct Keyword
- 3.38 Data Manipulation Language - Sorting Results
- 3.4 Additional Data Manipulation Language
- 3.41 Additional Data Manipulation Language - Constraints
- 3.42 Additional Data Manipulation Language - Using Joins
- 3.43 Additional Data Manipulation Language - Union Clause
- 3.44 Additional Data Manipulation Language - NULL Clause
- 3.45 Additional Data Manipulation Language - Alias Syntax
- 3.46 Additional Data Manipulation Language - Alter Commands
- 3.47 Additional Data Manipulation Language - Operators
- 3.48 Additional Data Manipulation Language - Using Views
- 3.49 Additional Data Manipulation Language - Having Clause
- 3.49 Assignment II
- 3.5 Advanced SQL
- 3.51 Advanced SQL - Transactions
- 3.52 Advanced SQL - Wildcards
- 3.53 Advanced SQL - Functions and Procedures
- 3.54 Advanced SQL - Temporary Tables
- 3.55 Advanced SQL - Grouping Sets, Cubes and Rollup
- 3.56 Advanced SQL - Sub Queries
- 3.57 Advanced SQL - Using Sequences
- 3.58 Advanced SQL - Handling Duplicates
- 3.6 Capstone Project (SQL)
- Quiz
4. Power BI
Available in
days
days
after you enroll
- 4.1 Power BI Basics
- 4.11 Introduction to Power BI and Installation Steps
- 4.12 Architecture
- 4.13 Supported Data Sources
- 4.14 Comparison with Other BI Tools
- 4.15 Data Modelling
- 4.16 Dashboards Options
- 4.17 Visualisation Options
- 4.2 Data Transformation and DAX Basics
- 4.21 Different Data Importation and Transformation
- 4.22 Data Visualisation
- 4.23 Dax Basics
- 4.24 DAX intermediate /RLS/Administration Role
- 4.3 Advanced Power BI Tools and Best Practices
- 4.31 Advanced Data Visualisation
- 4.311 Advanced Data Visualisation - Custom Visuals
- 4.312 Advanced Data Visualisation - Drilldown
- 4.313 Advanced Data Visualisation - Drillthrough
- 4.314 Advanced Data Visualisation - Controls
- 4.315 Advanced Data Visualisation - Filters
- 4.32 Power BI Best Practices
- 4.4 End-to-end Power BI Project
- 4.41 Lifecycle of a Power BI Project
- 4.42 End-to-end Power BI project (Hands-On)
- 4.5 Capstone Project (Power BI )
- Quiz
5. Python for Data Analysis
Available in
days
days
after you enroll
- 5.1 Introduction and history (3:06)
- 5.11 Python Syntax Basics (6:06)
- 5.12 Strings and Console Output (7:46)
- 5.13 Conditional Statements (7:20)
- 5.14 String Operations in Real Data (8:44)
- 5.15 Iris Dataset (3:17)
- 5.2 Functions, List and Dictionaries, List and Functions, Loops (6:35)
- 5.21 Functions in Python (8:00)
- 5.22 Lists (9:16)
- 5.23 Dictionaries (7:05)
- 5.24 Integrating Lists with Functions (6:38)
- 5.25 Loops for Data Processing (6:11)
- 5.3 Python for Data Science & Machine Learning
- 5.31 Numpy Basics
- 5.32 Data Handling with Python
- 5.33 Visualisation with Matplotlib and Seaborn
- 5.34 Introduction to Scikit-Learn
- 5.4 Pandas for Data Science & Machine Learning
- 5.41 Introduction to Pandas
- 5.42 Data Manipulation with Pandas
- 5.43 Advanced Data Analysis with Pandas
- 5.44 Pandas for Machine Learning Data Preparation
5. Machine Learning
Available in
days
days
after you enroll
- 5.1 Introduction to Machine Learning
- 5.11 Overview of Machine Learning and its Applications
- 5.12 Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- 5.13 Essential Python for Machine Learning
- 5.14 Data Pre-processing and Exploration
- 5.2 Supervised Learning
- 5.21 Linear Regression and Logistic Regression
- 5.22 Decision Trees and Random Forests
- 5.23 Support Vector Machines (SVM)
- 5.24 Evaluation Metrics for Classification and Regression
- 5.3 Unsupervised Learning
- 5.31 Clustering algorithms: K-means, Hierarchical, DBSCAN
- 5.32 Dimensionality Reduction: PCA and t-SNE
- 5.33 Association Rule Mining
- 5.4 Advanced Machine Learning Techniques
- 5.41 Ensemble Methods and Boosting
- 5.42 Feature Engineering and Selection
- 5.43 Hyperparameter Tuning and Optimisation
- 5.44 Model Deployment and Scalability
- 5.5 Special Topics in Machine Learning
- 5.51 Neural Network and Deep Learning
- 5.52 Natural Language Processing (NLP) basics
- 5.53 Introduction to Reinforcement Learning
- 5.54 Ethics and Bias in Machine Learning
- 5.55 Current Trends and Research Topics
STUDENTS SUCCESS STORIES
Sarah M., Junior Data Analyst
"I landed my first data analyst job!"
"Before this course, I struggled to understand SQL and data visualization. The hands-on projects and real-world datasets made all the difference! Within three months, I landed my first data analyst role. Thank you for such an amazing program!"
Maria J., Freelance Data Consultant
"I used these skills to start freelancing!"
"The freelancing module was a game-changer for me! I learned how to showcase my projects and find clients online. Now, I earn as a freelance data analyst while still learning!"
David O., Data Analytics Intern
"This course gave me the confidence to transition into data!"
"I had zero background in data analytics, but this course broke everything down step by step. The SQL and Power BI lessons were my favorite, and I now feel confident working with data. I highly recommend it to anyone looking to switch careers!"
Frequently Asked Question (FAQ)
1. What is this program about?
This program is designed for beginners and those looking to transition into data analytics. You’ll learn SQL, Python, and Power BI, gaining hands-on experience in data analysis to help you land your first data-related job.
2. Who is this program for?
- Aspiring data analysts, scientists, and engineers
- Professionals looking to transition into data analytics
- Beginners with no prior experience in data but a keen interest in learning
3. How does the program work?
- You’ll receive step-by-step video lessons covering Excel, SQL, Python, and Power BI.
- The course includes hands-on assignments and a capstone project.
- You’ll have access to live webinars and an online community for support.
4. Do I need prior experience in data analytics?
No prior experience is needed! The course starts with the basics and gradually builds your skills to an advanced level.
5. What tools and software will I learn?
You’ll gain hands-on experience with:
- Excel – Data cleaning, formulas, pivot tables, and visualization
- SQL (PostgreSQL) – Writing queries, manipulating databases, and performing advanced operations
- Power BI – Creating dashboards and reports
- Python (if included in the curriculum) – Data manipulation and analysis
6. What’s included in my subscription?
- Full access to all course materials and resources
- A 7-day free trial to explore the content risk-free
- Continuous updates and new content
- Community support and live engagement sessions
7. How much does it cost?
We offer two pricing plans:
- $25/month – Monthly access with the option to cancel anytime
- $99/year – Annual subscription at a discounted rate
8. Will I earn a certificate?
Yes! Upon completing the program and the capstone project, you’ll earn a Certificate of Preparation to showcase your skills.
9. Can I cancel my subscription?
Yes! You can cancel anytime from the account management page. If you cancel, you will have access until the end of your current billing cycle.
10. How do I enroll?
Click the “Enroll Now” button on the course page, choose your pricing plan, and start learning immediately!
11. Who are the instructors?
This course is led by Dr. Wilson Adejo and Dr. Emmanuel Ogungbemi, experienced professionals in data analytics and business intelligence.
Extra Session: Master Advanced SQL Queries & Optimization
Master advanced SQL techniques to enhance your skills and optimize queries for better performance with large datasets.
What You’ll Learn:
- Advanced Joins & Subqueries: Combine multiple tables and write nested queries.
- Query Optimization: Speed up your queries and improve database performance.
- Window Functions: Use window functions for advanced analytics.
- Indexing & Transactions: Enhance query speed and manage concurrent transactions.
Why Take This Session:
- Advanced Skills: Essential for handling complex datasets.
- Career Boost: In-demand skills for data professionals.
-
Practical Application: Learn techniques you can use right away.
Check your inbox to confirm your subscription