If you are an aspiring Data Scientist, Data Analyst or Machine Learning Engineer this is the right program for you. Stay ahead of the curve with our world-class curriculum crafted in partnership with Flat Iron School [ renowned US-based Tech Bootcamp].
Our comprehensive Data Science course will move you from beginner to mid-level. In 25 weeks you will learn Python for Data Science, Data cleaning, analysis, visualization, machine learning and so much more through practical hands-on projects and mentorship from industry experts.
At the end of the program, you will be an exceptionally skilled professional, ready to apply your knowledge to solve real-world challenges through the power of data.
Data Science is an interdisciplinary field that deploys algorithms, and other scientific methods and processes to acquire insights and knowledge from data. Data Scientists are equipped with the knowledge of how to use data, tell a story, and derive insights for businesses. Many industries are now leveraging data for decision-making in their day-to-day operations and forecasting.
Aspiring data scientists, analysts, and anyone eager to harness the power of data to drive decisions. This program is for you if you want to work with Data to:
If you are in search of a unique learning experience this is the place for you. We guarantee you will learn market-aligned skills through our practical and comprehensive curriculum.
Become a hot asset in the most in-demand career pathways in tech of the 21st century
Find out the pacing options available, price, and more information about this course.
2 days In-person & 3 days Online | Mon – Fri 8 am – 5 pm E.A.T
Ksh 200, 000
Download fees installment plans on the here
Our Data Science course will teach you the technical and soft skills that will have you adapt faster, learn how to learn, and stay relevant in the industry for a long time.
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.
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.
Students will go through training with our professional development trainers in Leading self and working with others.
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 Communicating for Impact & Entrepreneurial Thinking.
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.
Students will go through training with our professional development trainers in Project Management and Career Readiness.
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.
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.
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 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.
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 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.