Course Prerequisites
To have a good chance of completing and graduating from this course, you will need to:
- Have a basic understanding of mathematics and statistics concepts.
- Complete a multiple-choice assessment on probability and statistics to get admission.
- Have a laptop with these specifications: Core i5 & above, 8GB RAM, and at least 256GB of storage.
- Have internet access since the classes are online
- Be available to attend part-time classes running from Monday to Friday 6pm – 9pm.
- Attend at least 80% of classes and submit project work to graduate successfully.
Data Science remains one of the most exciting fields in Computer Science because it allows practitioners to achieve three significant kinds of results: discovery, insights, and innovation.
Curriculum
Data Science is one of the most highly sought-after jobs due to the abundance of data science career pathways and a lucrative pay scale. We maintain a strong and established connection with key industry figures who help in developing our curriculum in line with industry trends and current demands of the African job market. We aim to ensure learners are best prepared for an ever-evolving industry with the right foundation to find long-term success in their chosen career pathway.
Prep is the beginner/ foundational stage of the course. Students get introduced to Data Science fundamentals, Python for Data Science, Logic for Data Science, and Data Preparation.
Duration:
8 Weeks
Mode:
Live & Online from 6 pm - 9 pm
Fees:
Ksh 50,000 (USD 500)
At the end of the prep, you will be able to adapt the project life-cycle of a typical data science project while writing code and documenting your workflow in a programming environment.
Core is the advanced stage of the course. You will learn how to leverage modern programming languages and tools to analyze real-world data & work on both real-world projects.
Duration:
25 Weeks
Mode:
Hybrid (online & in-person)
Fees:
KSH 150,000(USD 1,500)
At the end of Core, learners will be able to present insights and recommendations from data, work on individual + team projects to build an impressive data scientist portfolio and acquire the confidence needed to succeed in the profession.
Topics covered:
- Fundamentals of Python programming
- The Data Science Project Life Cycle
- Ethics in Data Science
- Tools: Google Colabs/ Jupyter Notebooks
Topics covered:
- SQL Programming
- Project Management with Jira
Tools Used:
- Python
- Numpy
- SQL
- MySQL
- Git
- Github
- Jira.
Topics covered
- Data Sourcing & Preparation
- Data Integrity
- Data Visualization with Python & Tableau
Topics Covered
- Descriptive Analysis
- Sampling Distribution & Time series
- Hypothesis testing
- Final Assessment Week
Topics covered
- Regression
- Decision Trees
- KNN
- Neural Networks
- Final Assessment Week
Topics Covered
- Model Performance
- Dimensionality Reduction
- Clustering
Topics Covered;
- R fundamentals
- R & supervised learning
- R & unsupervised learning
- Final projects & presentations
Prep is the beginner/ foundational stage of the course. Students get introduced to Data Science fundamentals, Python for Data Science, Logic for Data Science, and Data Preparation.
Duration:
8 Weeks
Mode:
Live & Online from 6 pm - 9 pm
Fees:
Ksh 50,000 (USD 500)
At the end of the prep, you will be able to adapt the project life-cycle of a typical data science project while writing code and documenting your workflow in a programming environment.
Topics covered:
- Fundamentals of Python programming
- The Data Science Project Life Cycle
- Ethics in Data Science
- Tools: Google Colabs/ Jupyter Notebooks
Topics covered:
- SQL Programming
- Project Management with Jira
Tools Used:
- Python
- Numpy
- SQL
- MySQL
- Git
- Github
- Jira.
Topics covered
- Data Sourcing & Preparation
- Data Integrity
- Data Visualization with Python & Tableau
Core is the advanced stage of the course. You will learn how to leverage modern programming languages and tools to analyze real-world data & work on both real-world projects.
Duration:
25 Weeks
Mode:
Hybrid (online & in-person)
Fees:
KSH 150,000(USD 1,500)
At the end of Core, learners will be able to present insights and recommendations from data, work on individual + team projects to build an impressive data scientist portfolio and acquire the confidence needed to succeed in the profession.
Topics Covered
- Descriptive Analysis
- Sampling Distribution & Time series
- Hypothesis testing
- Final Assessment Week
Topics covered
- Regression
- Decision Trees
- KNN
- Neural Networks
- Final Assessment Week
Topics Covered
- Model Performance
- Dimensionality Reduction
- Clustering
Topics Covered;
- R fundamentals
- R & supervised learning
- R & unsupervised learning
- Final projects & presentations
Data Science is one of the most highly sought-after jobs due to the abundance of data science career pathways and a lucrative pay scale. By the end of the course, graduates will be fit for positions such as:

Data Scientist
A Data Scientist is a generalist who knows a bit of everything
Being a data scientist entails dealing with all aspects of a project. Often, in big companies, team leaders in charge of people with specialized skills are data scientists; their skill set allows them to overlook a project and guide them from start to finish.

Data Analyst
Data Analysts prepare reports that effectively show the trends and insights gathered from their analysis.
Data analysts are responsible for different tasks such as visualizing, transforming and manipulating the data. Sometimes they are also responsible for web analytics tracking and A/B testing analysis.

Data Engineer
Data engineers are responsible for designing, building, and maintaining data pipelines.
They need to test ecosystems for the businesses and prepare them for data scientists to run their algorithms. In short, they make sure that the data is ready to be processed and analyzed.

Data Storyteller
Data Storytellers find the narrative that best describes the data to express it to stakeholders
Data Storytelling lies right in the middle of pure, raw data and human communication. A data storyteller needs to take on some data, simplify it, focus it on a specific aspect, analyze its behavior, and use his insights to create a compelling story that helps people better understand the data.