Data Science Bootcamp
Master Python, Machine Learning, AI, and advanced analytics to become a complete data scientist ready for high-impact roles
Course Description
Step into the world of Full Stack Data Science — a comprehensive field where theoretical knowledge meets real-world application and every algorithm you build solves complex business problems. This hands-on program bridges the gap between learning and doing, designed for complete beginners and experienced professionals ready to transition into the AI-driven future of data science.
Master Core Data Science Skills
Build your foundation with Python programming, advanced machine learning techniques, computer vision, and natural language processing — the essential skills that power modern AI and data science
Build Real-World AI Solutions
Through hands-on projects and real-world applications, learn to create predictive models, image recognition systems, and AI-powered applications that solve complex business problems
Launch Your Data Science Career
Graduate with 20+ hands-on projects in your impressive portfolio, backed by a 2-month virtual internship and the confidence to tackle any data science challenge in today's AI-driven world
Through intensive training, you'll master Python programming, advanced machine learning, computer vision, and natural language processing. You'll build predictive models, create image recognition systems, and develop AI-powered applications. With 20+ hands-on projects and a 2-month virtual internship, you'll graduate with an impressive portfolio and the confidence to tackle any challenge in today's AI-driven world.
Why Full Stack Data Science?
Join one of the fastest-growing fields with unmatched opportunities worldwide
AI Revolution & High Demand
Data Science is at the forefront of the AI revolution. With machine learning, computer vision, and NLP transforming industries, skilled data scientists are more valuable than ever.
The World Economic Forum projects Data Science roles to be among the fastest-growing professions, with demand outpacing supply by 3:1 in most markets.
Premium Salaries Globally
Data Scientists enjoy premium compensation, remote work flexibility, and opportunitiesto work on cutting-edge AI projects.
Master These Essential Skills
Python Programming
Machine Learning Algorithms
Statistical Analysis
Data Visualization
SQL and Database Management
Computer Vision
Natural Language Processing
Deep Learning
Tableau Analytics
Advanced Excel
Big Data Technologies
Cloud Computing (AWS/Azure)
8 core competencies to launch your data analytics career
Course Modules
A structured learning path designed to take you from beginner to job-ready data analyst
- What is Data Science?
- Stages of learning Data Science
- Real-world applications and relevance
- Types of Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
- Roles in the field: Data Scientist vs Analyst vs Engineer vs ML Engineer
- CRISP-DM Data Science Lifecycle
- Introduction to tools: GitHub, Jupyter, ChatGPT
- Data Science Career Pathways
- LinkedIn Profile Optimization
- Installation and Setup
- Data Science Fundamentals - Deep Dive
- Problem-solving with Data Science
- Building a Data Science Portfolio
- Assessment: Multiple Choice + Use case walkthrough
- Excel Interface and Advanced Operations
- Environment, Navigation, Shortcuts
- Cell Referencing, Paste Special, Freeze Panes
- SUM, AVERAGE, COUNT, MIN, MAX, ROUND
- TRIM, CLEAN, SUBSTITUTE, UPPER, LOWER
- Data Cleaning and Transformation Functions
- Removing Duplicates, Flash Fill, Text to Columns
- IF, IFS, AND, OR, IFERROR, Nested IFs
- VLOOKUP, XLOOKUP, INDEX-MATCH
- Statistical Functions and Analysis
- Conditional Formatting, Data Validation
- Pivot Tables & Pivot Charts
- Dashboard Design & Data Storytelling
- Forecasting Techniques
- Excel Automation Techniques
- Using ChatGPT to assist with formulas and summaries
- Project: Build an Excel dashboard with real business data
- Mean, Median, Mode, Range, Variance, Std Dev
- Visualizing Categorical and Numerical Data
- Descriptive and Inferential Statistics
- Normal Distribution, Skewness, Kurtosis
- Central Limit Theorem, Missing Data Treatment
- Probability Distributions and Hypothesis Testing
- Hypothesis Testing, P-values, Confidence Intervals
- ANOVA, Chi-Square, T-Tests
- Statistical Visualization Techniques
- Correlation vs Causation
- Simple and Multiple Linear Regression
- Covariance, Multicollinearity
- Correlation and Regression Analysis
- Predictive Modeling Fundamentals
- Assessment: Case study analysis and regression exercise
- What is SQL? Relational Databases Explained
- Tables, Primary Keys, Foreign Keys
- SELECT, WHERE, ORDER BY, LIMIT, LIKE
- Database Design and Data Modeling
- GROUP BY, HAVING
- SUM, COUNT, AVG, MIN, MAX, DISTINCT
- Conditional Expressions: CASE, COALESCE
- Advanced SQL Queries and Functions
- INNER, LEFT, RIGHT, FULL JOINS
- Facts and Dimension Tables, ERD Creation
- UNION, INTERSECT, EXCEPT
- Joins, Subqueries, and Window Functions
- Subqueries, Nested SELECT, Common Table Expressions (CTE)
- SQL Views, Window Functions, Stored Procedures
- Connecting SQL to Excel and Power BI
- Database Optimization Techniques
- Project: Write and execute queries on a sales dataset + visualize in Power BI
- Git Basics and Version Control
- Setting up Repositories, Branching, Merging
- GitHub Collaboration and Pull Requests
- Pushing Projects from Jupyter and VS Code
- Writing Good READMEs and Docs
- GitHub and Version Control - Advanced
- Project: Create and push a data analysis project to GitHub
- Connecting Data Sources
- Dimensions vs Measures, Filters, Drilldowns
- Advanced Tableau Techniques
- Maps, Dual Axis, KPIs, Custom Tooltips
- Storyboards, Dashboards, Annotations
- Interactive Dashboard Creation
- Data Storytelling with Visualizations
- Joins vs Blends
- Creating Calculated Fields and Parameters
- Exporting & Publishing Dashboards
- Publishing and Sharing Reports
- ChatGPT for Data Narratives
- Project: Build an interactive Tableau dashboard for a business case
- Power BI Desktop Overview
- Get Data, Power Query Editor
- Cleaning, Filtering, Merging Tables
- Star Schema Design, Relationships, Cardinality
- Calculated Columns, Measures
- Intro to DAX: SUM, DIVIDE, CALCULATE, FILTER
- Creating Visuals: Cards, KPIs, Bar, Table, Slicers
- Bookmarks, Tooltips, Drill-throughs
- Publishing to Power BI Service + Sharing
- Using ChatGPT to generate report summaries
- Project: Executive dashboard using Power BI and SQL data
- Jupyter Notebook, Python Syntax
- Data Types, Variables, Functions, Loops, Conditionals
- Python Data Types and Control Structures
- Lists, Tuples, Dictionaries, Sets
- Map, Filter, Lambda, Comprehensions
- Functions and Object-Oriented Programming
- Arrays, Indexing, Broadcasting, Random Generators
- Matrix Operations, Linear Algebra Basics
- NumPy for Numerical Computing - Advanced
- DataFrames, Series, Reading CSVs
- Data Cleaning, Missing Values, Duplicates
- Merging, GroupBy, Pivot, Melt
- Pandas for Data Manipulation - Advanced
- Project: Data transformation and cleaning pipeline with Pandas
- Matplotlib, Seaborn, Pandas Visuals
- Histograms, Heatmaps, Boxplots, Scatter
- Data Visualization with Matplotlib and Seaborn
- Univariate, Bivariate, Multivariate EDA
- Exploratory Data Analysis (EDA) - Advanced
- ChatGPT-assisted EDA Narratives
- Statistical Analysis with Python
- Project: Perform full EDA with visual story on real dataset
- Regression, Classification
- Algorithms: Linear/Logistic Regression, Decision Trees, Random Forest, KNN
- Evaluation: Confusion Matrix, AUC, Precision, Recall, F1
- Machine Learning Theory and Applications
- Classification and Regression Algorithms
- Model Evaluation and Cross-Validation
- Clustering (KMeans, DBSCAN), PCA
- Feature Engineering, Scaling, Encoding
- Model Optimization: Cross Validation, Grid Search
- Clustering and Dimensionality Reduction
- Principal Component Analysis (PCA)
- Association Rules and Recommendation Systems
- Anomaly Detection Techniques
- Hyperparameter Tuning and Optimization
- Project: Model real-world data with at least 2 supervised and 1 unsupervised model
- Text Cleaning, Sentiment Analysis with VADER
- NLTK, TF-IDF, Text Classification
- Natural Language Processing (NLP) - Advanced
- Sentiment Analysis and Text Mining
- OpenCV for image processing, face detection
- Image Processing with OpenCV
- Feature Detection and Recognition
- Deep Learning for Computer Vision
- Real-world Computer Vision Projects
- Microsoft Fabric: Lakehouse, Data Factory overview
- AI Integration with ChatGPT
- Recommendation Systems - Advanced
- Capstone Project: Data Cleaning → EDA → Modeling → Dashboard → GitHub
- Peer Reviews, Final Presentations, Certification
Meet Your Expert Instructors
Dr. Jedidiah Usara
Lead Data Science Instructor
Daniel Orunnaiye
Senior Machine Learning Engineer
Dr. Victor Nwobodo
AI Research Scientist
Career Opportunities
With our Data Analytics Certificate, you can work in any of these high-demand roles
Data Scientist
Machine Learning Engineer
AI Research Scientist
Computer Vision Engineer
NLP Developer
Big Data Analyst
Quantitative Analyst
Research Scientist
Business Intelligence Developer
Predictive Modeler
Data Engineer
Deep Learning Engineer
Data Scientist
Machine Learning Engineer
AI Research Scientist
Computer Vision Engineer
NLP Developer
Big Data Analyst
Quantitative Analyst
Research Scientist
Business Intelligence Developer
Predictive Modeler
Data Engineer
Deep Learning Engineer
Investment in Your Future
What You Get:
- 6 months of intensive instructor-led classes
- 2 months of hands-on virtual internship
- 20+ real-world projects for your portfolio
- Expert mentorship and career guidance
- Lifetime access to materials and updates
- Industry-recognized certification