Introduction to Data Science
For aspiring Data Scientists with no prior coding experience, this course is a beginner-friendly launchpad designed to transform complete beginners into data-ready learners. Through hands-on training in Python and Google Colab, learners build practical data skills and gain the confidence needed to progress into the Data Science Bootcamp.
2026 Intakes in Progress
Full-time Remote
Start Date:
September 28th, 2026
Course Duration:
6 Weeks
Mode of Learning:
100% Remote Classes | Mon - Fri 8 am - 5 pm E.A.T
Tuition Fee:
Ksh 55,000
Brochure:
Part-time Remote
Start Date:
September 28th, 2026
Course Duration:
9 Weeks
Mode of Learning:
100% Remote Classes | Mon - Fri 6 pm - 9 pm E.A.T
Tuition Fee:
Ksh 60,000
Brochure:
A Beginner-Friendly Launchpad to Data Science
From tech to business, healthcare to government, data is driving smarter decisions everywhere. This beginner-friendly course offers a hands-on introduction to the essential tools and concepts of data science. You’ll learn to use Google Colab and beginner-friendly Python libraries, such as pandas, matplotlib, and seaborn, to analyze and visualize real-world data.
By the end of the course, you’ll be able to perform basic statistical analysis, build visualisations, and confidently draw data-driven conclusions — all with no prior coding experience required. This course is the ideal starting point for anyone interested in joining the Data Science Bootcamp. Start here, build your confidence, and take the first step toward a data career.
Start here | Build Confidence | Your career in Data Awaits
Course Details
Data Science is the process of using data to make smart decisions. It combines skills from math, statistics, and computer science to collect, clean, analyse, and interpret data into valuable insights. In simple terms, data scientists find patterns in data to answer questions for professionals in sales/marketing/health/traffic/customer-care, supermarkets etc
This course is designed for complete beginners with no prior programming experience.
You don’t need to be a math genius or a coding expert to get started. With a technical mentor’s guidance, anyone can learn the basics of data science.
- Have basic computer literacy and math skills.
- Proficiency in English – both spoken and written.
- Must have a computer or laptop with the following specs (Intel i5 11th Gen / Ryzen 5 4000+, 16GB RAM, 512GB NVMe SSD storage).
- Must have a desire and curiosity to learn.
No prior experience in programming is required.
You’ll build practical, hands-on skills to kickstart your data journey — no experience needed.
- Understand what data science is and the roles in the field
- Learn to write simple Python code to explore and clean data
- Use tools like pandas, matplotlib, and seaborn to analyse and visualise data
- Write basic SQL queries in databases
- Practice through weekly quizzes and lab exercises after each module
- Apply everything in a final capstone project, where you’ll solve a real problem using data
We guarantee you will learn market-aligned skills through our practical and comprehensive curriculum.
- Project-based learning with access to large data sets & real-world business case studies
- Technical Mentor Support
- Instructor-led lectures
- A certificate of completion from Moringa
- Dedicated support to join the Data Science Bootcamp after completion.
Learn Data Science From Scratch
Get Started - Apply NowCurriculum Overview
Objective: Understand what data science is, what data scientists do, and get comfortable using Google Colab to run basic Python commands.
- What is Data Science? Overview & lifecycle
- Roles in DS (Analyst vs Scientist) & Tools Overview
- Python Syntax: Variables, print(), data types (int, str, float, bool)
- Introduction to datasets
- More explorations
Objective: Learn basic programming logic and control structures in Python.
- Lists and indexing
- Loops (for, While )
- Conditional logic (if, elif, else)
- Functions: defining, calling, parameters
- Error messages + debugging basics
Objective: Learn how to read, clean, and manipulate datasets using pandas.
- Review: Loading CSVs & viewing (.head(), .columns)
- Cleaning data: removing nulls, renaming columns
- Selecting rows/columns: df[‘col’], df.query(), df[ condition ]
- Changing data types (astype) and formatting strings
- Exporting clean data to CSV
Objective: Learn to describe and visualise data using Python.
- Central tendency: Mean, Median, Mode
- Spread: Standard deviation, variance, range
- Outliers and skew
- Introduction to matplotlib and seaborn
- Chart storytelling: titles, axes, labels
Objective: Use SQL to explore tabular data with simple queries.
- What is a database? Tables, records, columns
- SELECT, FROM, WHERE
- Sorting and limiting: ORDER BY, LIMIT
- Logical filters: AND, OR, IN, LIKE
- Practice query scenarios
- Project Brief + Dataset Selection
- Data Exploration + Cleaning
- Data Analysis: Stats + Visualisations
- Final Presentation
Curious about Data Science?
This course is designed for complete beginners.
Career Opportunities
- Role: Interpret data, analyse results, and create reports to provide actionable insights.
- Industries: Finance, marketing, healthcare, technology, and more.
- Role: Bridge the gap between IT and business, using data analytics to drive business decisions and improvements.
- Industries: Retail, manufacturing, banking, and consulting.
- Role: Assess product performance and customer feedback to guide product development and enhancements.
- Industries: Consumer goods, technology, and software development.
- Role: Interpret data, analyse results, and create reports to provide actionable insights.
- Industries: Finance, marketing, healthcare, technology, and more.
- Role: Bridge the gap between IT and business, using data analytics to drive business decisions and improvements.
- Industries: Retail, manufacturing, banking, and consulting.
- Role: Assess product performance and customer feedback to guide product development and enhancements.
- Industries: Consumer goods, technology, and software development.
Are you ready to jumpstart your journey in Data Science?
