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Univelcity
EngineeringBeginner

Data Science

Univelcity offers an intensive Data Science Course designed to equip students with core programming and analytical tools to extract insights and drive business decisions through data. The 12-week program builds foundational skills from scratch, covering Python programming fundamentals, database modeling with SQL, web scraping, data manipulation, and exploratory data analysis.

Participants gain hands-on practice with specialized data libraries and tools, including NumPy, Pandas, Matplotlib, Seaborn, MySQL, Beautiful Soup, Git, and Jupyter Notebooks, along with an introduction to Machine Learning concepts. Designed for beginners as well as professionals seeking to transition into data roles, the course emphasizes practical problem-solving through weekly assignments and real-world analytical projects.

Who it's for

Anyone ready to extract insight, not just extract data.

  • Analysts who want to move beyond dashboards into prediction and experimentation.
  • Engineers and product people who want to understand what the data actually says.
  • Career changers with quantitative backgrounds ready to build a new skill.

What you'll build

A complete data science project with real business impact.

End-to-end pipeline from raw data to deployed model including exploratory analysis, feature engineering, model selection, and a documented business recommendation built on real datasets and shipped as a portfolio case study.

Course Outline & Brochure Summary

  1. 01
    Week 1: Introduction to Python & Environment Setup

    Basic programming terms, overview of Python and its interpreter, areas of application, installing development tools, executing initial Python code, and file handling basics.

  2. 02
    Week 2: Variables, Syntax & Built-in Functions

    Python syntax rules, variable assignment, arithmetic operations, data type conversion, and overview of standard built-in functions.

  3. 03
    Week 3: Conditional Logic, Control Flow & Iteration

    if/elif/else conditional logic, for and while loops, iterators, generators, functional operations (map, filter, reduce), list comprehensions, and building an exhaustive enumeration puzzle project.

  4. 04
    Week 4: Python Data Structures

    Deep dive into lists and list operations, dictionaries, tuples, sets, and zipped collections using zip().

  5. 05
    Week 5: Functions, Scopes & Decorators

    Function declaration, return values, variable arguments (*args, **kwargs), nested functions, recursion, lambda functions, Python decorators, and building a Note library project.

  6. 06
    Week 6: Object-Oriented Programming (OOP)

    Core Classes, objects, constructors, class vs. instance attributes, and dynamic attribute/method creation and deletion.

  7. 07
    Week 7: Object-Oriented Programming (OOP)

    Advanced Inheritance, method overriding, polymorphism, abstraction, operator overloading, and extending the Notepad project using OOP design patterns.

  8. 08
    Week 8: File Handling & Web Scraping

    Working with CSV files, HTTP requests with the requests module, web scraping fundamentals, and parsing HTML with BeautifulSoup.

  9. 09
    Week 9: Relational Databases & MySQL

    Structured Query Language (SQL) basics, database server setup, single and multi-table design, normalization, foreign keys, JOIN operations, and connecting Python to MySQL via pymysql.

  10. 10
    Week 10: Numerical Computing with NumPy

    Setting up Anaconda and Jupyter Notebooks, introduction to Data Science workflows, indexing 1D/2D/3D NumPy arrays (image processing examples), and statistical calculations.

  11. 11
    Week 11: Data Analysis with Pandas & Regular Expressions

    Working with DataFrames and Series objects, row/column operations, grouping, handling missing values, sorting, cross-tabulation, time series analysis, string manipulation, Regular Expressions (RegEx), and executing a loan performance analysis project.

  12. 12
    Week 12: Data Visualization with Matplotlib & Seaborn

    Constructing line charts, bar plots, histograms, and donut charts, plot styling and formatting, applying Seaborn themes, statistical visualization, and executing a Nigerian COVID-19 dataset growth analysis project.

  13. 13
    Week 13: Capstone Projects & Graduation

    Finalizing end-to-end data analytics capstone projects and program completion.

Course Prerequisites

  • Prior Programming Knowledge: None required; curriculum is designed to teach programming fundamentals from the ground up.

  • Computer Literacy: Basic operating system knowledge and general computer literacy.

  • Hardware Requirements: A functional laptop with at least 4GB RAM minimum.

  • Personal Commitment: Strong willingness to learn and commit time to weekly assignments and hands-on analytical projects.

01

Small cohorts, real projects

Work on datasets that matter with feedback every week.

02

Learn by doing

No theory divorced from practice. Every algorithm lands in your portfolio project.

03

Taught by practitioners

Learn from people building models at real companies, making real trade-offs.

04

Leave with proof

A certification and a portfolio piece you can show an employer.

Start building this month.

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