Data Science - Professional
- Length
- Internship
- Certificate
- Level
About the course
Data Science Professional is a comprehensive, hands-on program designed to take learners from programming fundamentals to building real-world data science solutions. The course begins with Python foundations and progresses through data manipulation with NumPy and Pandas, data visualization, and applied statistics. Learners gain strong SQL skills for data extraction, explore core machine learning concepts, and build predictive models using industry-standard techniques. The program culminates in an end-to-end capstone project where students solve a real business problem, analyze data, build models, and present insights through clear storytelling and dashboards—preparing them for practical data science roles.
Course Curriculum
Module 1 — Python Programming for Data Science (Foundations)
- Introduction to Python & Jupyter Notebook
- Variables, Data Types & Type Conversion
- Input/Output, Comments & Code Structuring
- Control Flow: if, elif, else
- Loops: for, while, break, continue
- Functions (def, parameters, return values)
- Python Collections: Lists, Tuples, Sets, Dictionaries
- Error Handling Basics
- Introduction to Modules & Packages
- Practical Coding Exercises
Module 2 — Data Manipulation with NumPy & Pandas
- NumPy arrays, indexing, slicing, broadcasting
- Creating and manipulating arrays
- Pandas Series and DataFrames
- Importing CSV, Excel, JSON datasets
- Data cleaning techniques
- Handling missing values
- Data transformation & feature engineering basics
- Groupby, merging, concatenation, joins
- Practical exercises and mini-projects
Module 3 — Data Visualization & Exploratory Data Analysis
- Introduction to Matplotlib & Seaborn
- Chart types: bar, line, scatter, heatmaps, histograms
- Multi-plot visualizations
- Visual storytelling principles
- Identifying trends, patterns, and outliers
- EDA workflow on real datasets
- Insights extraction for business problems
- Mini-project: EDA on a customer, finance, or health dataset
Module 4 — Statistics for Data Science (Applied)
- Descriptive statistics
- Probability concepts
- Random variables
- Sampling & distributions
- Hypothesis testing (t-test, chi-square, ANOVA basics)
- Correlation & covariance
- Statistical inference for decision-making
- Real-world statistical case studies
Module 5 — SQL for Data Scientists
- SQL syntax, SELECT, WHERE, ORDER BY
- Aggregations & grouping
- JOINS (Inner, Left, Right, Full)
- Subqueries
- Views & simple stored functions
- Data extraction for analysis
- Hands-on practice using real databases
- End-of-module SQL mini project
Module 6 — Introduction to Machine Learning
- What is Machine Learning?
- Types of ML (Supervised vs Unsupervised)
- Train-test split, cross-validation
- Evaluation metrics (accuracy, precision, recall, ROC AUC)
- Regression models: Linear, Multiple, Polynomial
- Classification models: Logistic, KNN, Decision Trees
- Clustering: K-Means
- Model deployment overview
- Build 2 ML mini-projects
Module 7 — Capstone Project (End-to-End Data Science Project)
- Problem definition
- Data sourcing & cleaning
- Exploratory Data Analysis
- Feature engineering
- Model selection & evaluation
- Insights reporting
- Dashboard/story presentation
Course Structure
- Saturday Live Classes for 4 months
- 1-Month Internship
- CV Review
- LinkedIn Optimization
- Interview Preparation
- Post-Training Support
- Mentorship Sessions
- Career Pathway Guidance
Chinyere Chukwuka
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