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Data Science - Professional

The course equips learners with in-demand skills in Excel automation, Power BI analytics, SQL querying, and Python data analysis, enabling them to transform raw data into meaningful insights, dashboards, and reports that support strategic business decisions.
  • Length
2 Months
  • Capstone
1 Months
  • Certificate
Yes
  • Level
Professional
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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

  • 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
  • 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
  • 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
  • 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

Course Structure

  • Saturday Live Classes for 2 months
  • 1-Month Capstone
  • CV Review
  • LinkedIn Optimization
  • Interview Preparation
  • Post-Training Support
  • Mentorship Sessions
  • Career Pathway Guidance

Yusuf Ridwan Lanre

Data science facilitator

Ridwan Lanre Yusuf is a data scientist with a background in computer science, specializing in machine learning and data-driven application development. He has hands-on experience building and deploying models using modern tools like FastAPI and Docker, alongside strong interests in financial modeling and quantitative analysis.

He is also an active contributor to the data community, serving as a mentor and global ambassador, where he supports and guides aspiring data professionals.