Part-time Data Science Course

Orientation, Pre Work and Introduction to Data Science Principles

During orientation, you learn more about Moringa, our policies, learning model, learning platform, classroom structure, and learning schedule.

The Data Science pre-work covers introductory Data Science concepts. By the end of pre-work, you will be prepared to dive into the course material and you will be at the same level as your coursemates.

You will also learn about Dat Science Principles, and Software Engineering principles and dive deep into python programming.

Data Analysis & Engineering

In this phase, students will be introduced to the fundamentals of python for Data Science. You’ll learn how to use Jupyter Notebooks, and will be familiarized with popular Python libraries that are used in data science such as Numpy and Pandas. To organize your data, you’ll learn about data structures, relational databases, ways to retrieve data, and the fundamentals of SQL for data querying structured databases. Furthermore, you will learn how to access data from various sources using APIs and perform Web scraping.

At the end of this phase, students will be able to use skills to collect, organize and visualize data with the goal of providing actionable insights.

Scientific Computing & Quantitative Methods

In phase 2, students learn about the fundamentals of probability theory like combinations and permutations. They also learn about statistical distributions and how to create samples, then apply this knowledge by running A/B experiments. At the end of this phase, students will be able to build their first data science model using linear regression.

Students will go through training with our professional development trainers in leading self, working with others, project management, career readiness, communicating for impact & Entrepreneurial Thinking

Machine Learning Fundamentals

In this phase, students learn about machine learning with a heavy focus on supervised learning. For starters, learners get into regression analysis and a new form of regression — logistic regression. In building regression models, students also learn penalization terms, preventing overfitting through regularization, and using cross-validation to validate regression models.

At the end of this phase students will be able to build and implement the most important machine learning techniques.

Students get a 1-week break to relax and boost energies to complete the remaining modules.

Advanced Machine Learning

This phase of the course focuses on a variety of Data Science techniques. Students learn about unsupervised learning techniques like clustering and dimensionality reduction. Students will be introduced to threading and multiprocessing to be able to work with big data. In doing so, you’ll learn about PySpark and AWS, and how to use those tools to build a recommendation system. You’ll also learn about deep learning, neural networks, and how to perform sentiment analysis.

Final Data Science Projects

In your final project, learners work individually or in groups to apply the technical and soft skills training and knowledge. Students will be required to create a large-scale data science and/or machine learning project. This final project provides an in-depth opportunity for you to demonstrate your learning accomplishments and get a feel for what working on a large-scale data science project is really like.