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Requirements
- Familiarity with spreadsheets (Excel/Google Sheets)
- Basic computer skills
- Basic math knowledge (percentages, averages)
- Access to stable internet
- A willingness to learn and practice
Features
- Step-by-step lessons
- Real-world case studies
- Practical exercises
- Downloadable resources
- Quizzes and assessments
- Data science mini-project
- Beginner-friendly explanations
- Industry case studies
- Visual demonstrations
Target audiences
- Beginners with no data background
- Students in tech or business fields
- Entrepreneurs & business owners
- Marketing professionals
- Researchers
- Career switchers
- IT professionals
- Freelancers
- Analysts-in-training
Featured Review
“This course made data science simple and practical. I went from knowing nothing to analyzing real datasets with confidence. The step-by-step approach and real-life examples were amazing.” — Emma Njeri, Customer Data Analyst
Course Description
The Fundamentals of Data Science course provides a comprehensive introduction to the core principles, tools, and practical techniques used to analyze data and support informed decision-making.
Designed for beginners and aspiring data professionals, this course blends foundational theory with hands-on practice. Learners will explore the full data lifecycle—from data collection and cleaning to analysis, visualization, and interpretation—while gaining practical experience with real-world datasets.
By the end of the course, participants will have a solid understanding of how data science is applied across industries and how to transform raw data into meaningful insights.
What you’ll learn?
By the end of this course, learners will be able to:
- Understand how data science is applied in real-world scenarios across industries
- Use essential tools and techniques for data analysis (e.g., spreadsheets, Python basics)
- Clean, organize, and prepare data for analysis
- Interpret data accurately and draw meaningful conclusions
- Build simple predictive models for basic forecasting
- Communicate insights effectively using visualizations and reports
Curriculum
- 10 Sections
- 50 Lessons
- 5 Weeks
Expand all sectionsCollapse all sections
- SECTION 1: Introduction to Data Science5
- SECTION 2: Understanding Data5
- SECTION 3: Data Cleaning and Preparation5
- SECTION 4: Exploratory Data Analysis (EDA)5
- SECTION 5: Basic Statistics for Data Science5
- SECTION 6: Introduction to Python for Data Science5
- SECTION 7: Data Visualization5
- SECTION 8: Introduction to Machine Learning5
- SECTION 9: Real-world Data Science Applications5
- SECTION 10: Project and Career Path5
This course is ideal for beginners, students, professionals, and anyone interested in understanding data science without prior technical experience.
No prior programming experience is required. The course introduces basic concepts in a beginner-friendly way, including optional exposure to Python.
You will learn foundational tools such as spreadsheets (e.g., Microsoft Excel) and introductory Python concepts for data analysis.
The course combines both theory and hands-on practice, ensuring you gain real-world skills alongside conceptual understanding.
Yes. You will analyze real-world datasets to understand how data science is applied across different industries.
You will complete small practical exercises such as data cleaning tasks, basic analysis, simple visualizations, and introductory predictive models.
The course typically runs for 3 to 6 weeks, depending on the learning format (part-time or intensive).
Yes. A certificate of completion is awarded after successfully finishing all course requirements.
This course provides a foundation for roles such as Data Analyst, Junior Data Scientist, Business Analyst, and Reporting Analyst.
After completion, you can progress to advanced topics such as machine learning, big data analytics, and AI, or start applying data skills in your current role.
Requirements
- Familiarity with spreadsheets (Excel/Google Sheets)
- Basic computer skills
- Basic math knowledge (percentages, averages)
- Access to stable internet
- A willingness to learn and practice
Features
- Step-by-step lessons
- Real-world case studies
- Practical exercises
- Downloadable resources
- Quizzes and assessments
- Data science mini-project
- Beginner-friendly explanations
- Industry case studies
- Visual demonstrations
Target audiences
- Beginners with no data background
- Students in tech or business fields
- Entrepreneurs & business owners
- Marketing professionals
- Researchers
- Career switchers
- IT professionals
- Freelancers
- Analysts-in-training

