Data Science Full-time Course

Orientation, Pre Work and Introduction to Data Science Principles ( 2 weeks )

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 with your coursemates.

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

Data Analysis & Engineering ( 3 weeks )

In this phase, students will be introduced to 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 API’s 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.

What is covered:– Variables, Booleans and Conditionals, Lists, Dictionaries, Looping, Functions, Data Structures, Data Cleaning, Pandas, NumPy, Matplotlib/Seaborn for Data Visualization, Git/Github, SQL, Accessing Data through APIs, Web Scraping.

Students will go through training with our professional development trainers in Leading self and working with others.

Scientific Computing & Quantitative Methods ( 3 weeks )

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.

What is covered:- Combinatorics, Probability Theory, Statistical Distributions, Bayes Theorem, Sampling Methods, Hypothesis Testing, A/B Testing, Linear Regression, Model Evaluation

Students will go through training with our professional development trainers in Communicating for Impact & Entrepreneurial Thinking.

Machine Learning Fundamentals ( 3 weeks )

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.

What is covered:– Linear Algebra, Logistic regression, Maximum Likelihood Estimation, Optimization Cost Function, Pipeline Building, Hyperparameter Tuning, Grid Search, Scikit-Learn, Gradient Descent, K-Nearest Neighbors, Decision Trees, Ensemble Methods

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

Students will go through training with our professional development trainers in Project Management and Career Readiness.

Advanced Machine Learning ( 3 weeks )

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.

What is covered:- Dimensionality Reduction, Clustering, Times Series Analysis, Neural, Networks, Big Data, Natural Language Processing, Text Vectorization, Natural Language, Toolkit, Regular Expressions, Word2Vec, Text Classification, Recommendation Systems

Final Data Science Projects ( 3 weeks )

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.