Full-time Data Science Course (Online)

A course for the passionately curious who want to work with data to:
➜ Discover lessons hidden in past events
➜ Gain unique insight for reasons behind recent events
➜ Innovate and predict future trends in business, life, and nature

Become a Moringa Certified Data Scientist and enjoy up to 12 months of alumni support from our Career Services Department.

23 Weeks of Modelling Data for Analysis, Visualization, & Machine Learning

Course Includes: Python, R, SQL, Pandas, NumPy, Supervised Learning, Advanced Regression, Decision Trees, Neural Networks, and More.

The credits are rolling on a movie you’ve just concluded watching and Netflix automatically queues the preview for another movie that you might like; You’re doing some online shopping on Jumia and while scrolling down the product description, you notice a section that says “Most popular” that has the same product from different vendors at different prices; this is Data Science at work and businesses are always looking for talented individuals to help them analyze and draw actionable insights from their data in order to make informed business decisions. Become a hot asset in one of the most sought-after career pathways of the 21st Century.

Next Intake Dates

11th April, 2022
Full Apply
18th July, 2022
Open Apply
10th October, 2022
Open Apply

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

Additionally, you are needed to be present in at least 80% of the classroom check-ins, have a working laptop (core i5 7th Gen and upwards, 4GB RAM, and at least 256GB of storage), and complete and submit your independent projects in time for grading.

  • Entry Assessment

    Entry Assessment

    You will need to take a short multiple-choice entry assessment after your application to be admitted to this course.

  • Class Hours

    Class Hours

    • This is a full-time class taking place on weekdays 8am – 5pm.
    • Students must attend 70% of classes.
    • Learners must check-in with their Technical Mentors in the morning and check out in the evening.
  • Laptop

    Laptop

    You need a personal laptop with the following minimum specifications: –

    • Core i5 7th gen, 4gb RAM, 256GB Hard Disk.
    • Laptop financing is provided for those who need it.
  • Internet

    Internet

    You need a stable internet connection for live classes, group sessions and to use online resources.

    We support students with internet bundles for online learning. We recommend a good enough, base-level connection.

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.

Beginner Module (Prep)
Advanced Module (Core)
Course Overview

Topics Covered:

  • Becoming a Data Scientist
  • The Data Science Project Life Cycle
  • Ethics in Data Science
  • Tools: Google Colabs/ Jupyter Notebooks

Learning Objectives:

  • Understand what it takes to become a Data Scientist.
  • Adapt the project life-cycle of a typical data science project
  • Demonstrate a sophisticated awareness of ethical implications relevant to the use of data.

Topics Covered:

  • Workflow and Version Control
  • Python Programming
  • SQL Programming
  • Project Management with Jira
  • Tools Used: Python, Numpy, SQL, MySQL, Git, Github, Jira.

Learning Objectives:

  • Write code and document your workflow in a programming environment.
  • Recall the basics of Python programming for data science.
  • Obtain and manipulate data from various types of databases using the SQL language.
  • Manage a team deliverable using a standard project management tool.

Topics Covered:

  • Data Integrity
  • Sourcing Data
  • Data Cleaning
  • Tools: Python, Pandas, Matplotlib

Learning Outcomes:

  • Evaluate the integrity of data by making decisions on data quality issues
  • Perform the extraction, querying, and aggregation of data for analysis in multiple projects through common techniques and tools
  • Understand mechanisms for missing data, outliers, and analytic implications.

Topics Covered in Week 6:

  • Variables
  • Ethics in Predictive Modelling
  • Regression

Topics Covered in Week 7:

  • Predictive Modeling vs Traditional Analysis
  • Feature Engineering
  • Regression

Topics Covered in Week 8:

  • Decision trees
  • Support Vector Machine (SVM)

Topics Covered in Week 9:

  • KNN Algorithm
  • Naive Bayes

Topics Covered in Week 10-11:

  • Introduction to Neural Networks
    • Intuition – Foundation, Neurons, nodes, layers
    • Loss and Cost functions
    • Activation functions
    • Feed forward pass
    • Batch Gradient Descent
    • Mini Batch Gradient Descent
    • Stochastic Gradient Descent

Topics Covered in Week 12-14:

  • Model Performance
  • Dimensionality Reduction
  • Clustering

Topics Covered in Week 15:

  • R Fundamentals
  • R Data Preparation, Cleaning, Analysis, EDA
  • Supervised and Unsupervised Learning with R

Complete the final independent project that helps demonstrate your ability to create, supervise, audit, and execute complex projects as a Data Scientist.

Prep is the BEGINNER 5-week foundational stage for this course that introduces you to Data Science, Logic for Data Science, and Data Preparation.

Duration:

5 Weeks

Mode:

Live & Online

Fees:

Ksh 43,000 (USD 430)

At the end of the prep module, 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

Course Overview

Topics Covered:

  • Becoming a Data Scientist
  • The Data Science Project Life Cycle
  • Ethics in Data Science
  • Tools: Google Colabs/ Jupyter Notebooks

Learning Objectives:

  • Understand what it takes to become a Data Scientist.
  • Adapt the project life-cycle of a typical data science project
  • Demonstrate a sophisticated awareness of ethical implications relevant to the use of data.

Topics Covered:

  • Workflow and Version Control
  • Python Programming
  • SQL Programming
  • Project Management with Jira
  • Tools Used: Python, Numpy, SQL, MySQL, Git, Github, Jira.

Learning Objectives:

  • Write code and document your workflow in a programming environment.
  • Recall the basics of Python programming for data science.
  • Obtain and manipulate data from various types of databases using the SQL language.
  • Manage a team deliverable using a standard project management tool.

Topics Covered:

  • Data Integrity
  • Sourcing Data
  • Data Cleaning
  • Tools: Python, Pandas, Matplotlib

Learning Outcomes:

  • Evaluate the integrity of data by making decisions on data quality issues
  • Perform the extraction, querying, and aggregation of data for analysis in multiple projects through common techniques and tools
  • Understand mechanisms for missing data, outliers, and analytic implications.

In the ADVANCED 18-Week core class, you will deep-dive into data analysis, data visualization, and an introduction to machine learning. You will get to work on both individual and team-based projects, learning how to leverage modern programming languages and tools to collect and analyze real-world data.

Duration:

18 Weeks

Mode:

Live & Online

Fees:

Ksh 131,000 (USD 1,310)

You will have access to our experienced technical mentors who will guide you on how to build an impressive data scientist portfolio and acquire the confidence needed to succeed in the profession.

Course Overview

Topics Covered in Week 6:

  • Variables
  • Ethics in Predictive Modelling
  • Regression

Topics Covered in Week 7:

  • Predictive Modeling vs Traditional Analysis
  • Feature Engineering
  • Regression

Topics Covered in Week 8:

  • Decision trees
  • Support Vector Machine (SVM)

Topics Covered in Week 9:

  • KNN Algorithm
  • Naive Bayes

Topics Covered in Week 10-11:

  • Introduction to Neural Networks
    • Intuition – Foundation, Neurons, nodes, layers
    • Loss and Cost functions
    • Activation functions
    • Feed forward pass
    • Batch Gradient Descent
    • Mini Batch Gradient Descent
    • Stochastic Gradient Descent

Topics Covered in Week 12-14:

  • Model Performance
  • Dimensionality Reduction
  • Clustering

Topics Covered in Week 15:

  • R Fundamentals
  • R Data Preparation, Cleaning, Analysis, EDA
  • Supervised and Unsupervised Learning with R

Complete the final independent project that helps demonstrate your ability to create, supervise, audit, and execute complex projects as a Data Scientist.

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. The following are some career options you can pursue after graduating from Moringa:

Data Scientist

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 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 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 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.