What is Data Science?
Data science is a multidisciplinary field using software and algorithms to uncover insightful findings from data to enable companies to make smarter business decisions. It employs concepts, techniques and theories drawn from the fields of mathematics, statistics, information science and computer science.  

Why Learn Data Science?
In recent years, digital information has become so essential that organizations use different kinds of data every day. Unfortunately, not many professionals have the capability to process and provide insights from this data. This is evident in the Kenyan and African context where many organizations lack data teams skilled enough to provide meaningful insights and analysis to drive growth and innovation. 

Here at Moringa School, you can grow to become a Junior Data Scientist with the skills to act as a consultant to organizations by understanding the complex behaviors, trends and characteristics within the collected and accumulated data.

After graduating from Moringa, you will have a highly technical skill set in data science that is very desirable in the Kenyan job market today.

Overall Course Overview (Prep & Core)

In the Moringa Data Science course, you will learn the essential data science skills needed to solve complex business problems. In addition, you will learn essential non-technical skills that will help you communicate your findings to stakeholders in the workplace, who will make decisions based on your findings.

Every day, as a data science student, you will take on real-world business problems and case studies and analyze data sets for new insights. Ultimately, you present your findings using statistics, critical thinking and business knowledge.

Full Duration (Prep & Core)

The prep program is a prerequisite class before the core program. This class is five weeks long and the core program lasts 18 weeks.

Overall Course Goals (Prep & Core)

By working on projects individually or in teams, you will apply your new data analytical skills towards building an impressive data analysis portfolio, as well as build the self-confidence needed to succeed in today’s data-driven economy.

Overall Learning Objectives (Prep & Core)

By the end of the course, you will be able to:

  • Source and store data

  • Perform data visualisation

  • Present insights and recommendations from data to solve complex business problems

Who Should Attend? (Overall Course Prerequisites)

Individuals with a basic knowledge in mathematics and statistics are encouraged to apply for this course. Basic knowledge in the following units will be an added advantage for your learning progress:

  • Linear algebra (i.e. vectors, matrices and least squares)

  • Calculus (i.e. differential calculus)

  • Statistics and probability








Learning Outcomes for Data Science Prep (5 Weeks)
Each data science topic in the Moringa Data Science Prep curriculum culminates with a summative assessment which aims to test your knowledge and understanding of that topic. It is also a way of evaluating your understanding and achievement of the following learning outcomes:

  1. Introduction to Data Science
    By the end of this unit, you should be able to:
    1. Understand what it takes to become a data scientist
      Subtopic: Becoming a data scientist
    2. Adapt the project life-cycle of a typical data science project
      Subtopic: the data science project lifecycle
    3. Demonstrate a sophisticated awareness of ethical implications relevant to the use of data
      Subtopic: Ethics in data science

    Tools: Standups, Check-ins, CRISP-DM.

  2. Logic for Data Science
    By the end of this unit, you should be able to:
    1. Write code and document your workflow in a programming environment
      Subtopic: Workflow and version control
    2. Recall the basics of Python programming for data science
      Subtopic: Subtopic: Python programming
    3. Obtain and manipulate data from various types of databases using the SQL language
      Subtopic: SQL programming
    4. Manage a team deliverable using a standard project management tool
      Subtopic: Project management with Jira

    Tools: Python, Numpy, SQL, MySQL, Postgres, Git, Github, Jira.

  3. Data Preparation
    By the end of this unit, you should be able to:
    1. Evaluate the integrity of data by making decisions on data quality issues
    2. Perform the extraction, querying and aggregation of data for analysis in multiple projects through common techniques and tools
    3. Understand mechanisms for missing data, outliers and analytic implications

    Tools: Python, Pandas, Matplotlib.

Course Topics for Data Science Core (15 Weeks)
Because of the proprietary nature of our Data Science Full-time Course. The Course Outline for Data Science Core Program is only available to those students who go through our Data Science Prep Program or to you, if you visit our Nairobi Campus for a brochure.

Overall Learning Objectives for Data Science Core (15 Weeks)
By the end of the course, you will have become a Junior Data Scientist. This means you will be able to:

  1. Analyze and Visualize Data
  2. Model Data
  3. Present insights and recommendations from data to solve complex business problems

Through working on projects individually or in teams, you will apply your new data science analytical and modeling skills towards building an impressive data scientist's portfolio and the confidence needed to succeed in today’s data-driven economy.

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 Samuel describes himself as a Data Scientist who simply does beautiful things with data.  He's the Co-Founder and Lead Data Scientist of Duara Analytics, a machine intelligence research company, and he is also the Data Science Technical Mentor at Moringa School. Previously, Samuel was a Machine Learning Engineer at Fellowship AI working on a collaborative project with Amazon. He has also had also worked with I&M Bank and Strathmore University where he pioneered their Certificate in Data Science Course. Samuel's passion is to inspire others to pursue careers in science and technology to generate a positive impact on the world. When he's not crunching numbers, he travels the world on his motorcycle appropriately named Arya. Samuel believes technology gives us superpowers. At Moringa School, we are building those superpowers.    See full Profile on LinkedIn

Samuel Kimani

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Valentine is a data curriculum specialist, designing and facilitating data science workshops that create engaging and interactive learning experiences for Young leaders with the potential of impacting 3 million entrepreneurial and impactful African leaders by the year 2035. Prior to that, Valentine identified instructional gaps with accounting courses in the Kenyan market and designed experiences to increase online student engagement which resulted to high performance in CPA examinations. His life mission is to impact a billion lives through technological innovations in education. He also continues to achieve this through coaching and technical mentorship to Young leaders.   See full Profile on LinkedIn

Valentine Mwangi

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Kamande currently leads Data Science at mSurvey, a platform that powers Integrated Customer Experience in Africa. In this role, he provides data support for current products and develops new Data products to drive consumer understanding through advanced analytics and machine learning. He holds a Masters’ degree in Statistics and a Masters’ degree in Computational Intelligence from The University of Nairobi and previously worked at Nielsen as a Data Science Associate for East Africa. See full Profile on LinkedIn

Samuel Kamande Wambui

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The program exceeded my expectations in terms of what I wanted to gain in a day.  For example, I have been able to understand R and some bit of supervised machine learning. Teacher Kamande was very helpful in terms of sharing his knowledge. I would recommend other guys to pursue this course.

Johnbosco Mulei

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I developed an interest in Data Science and I started looking at options on where I could do it. Moringa School came to mind because I had heard of so much good reviews about their programs. My initial expectation was that I would be here to learn more on exploratory data analysis and understand more about machine learning using R. They were all met. I am looking forward to joining the full-time program.

Carol Wambui