Part-time Data Science Course

Study part-time and become a Moringa Certified Data Scientist
Part-time Data Science Course
  • Enroll now for the November 21st intake

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Have a demanding schedule? Explore our part-time option

Learn Python for Data Science, Data Analysis, Data Visualization, Data Modelling & Machine Learning in 30 weeks

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.

Now more than ever, industries are leveraging data to monitor behaviors and trends. Data Scientists are equipped with the knowledge of how to make use of data, tell a story, and derive insights for businesses. In order for businesses to succeed in this day and age, they need Data Scientists to fill the gaps, use data to set business goals, and find opportunities that could not be considered were it not for the data insights.

Data Science Jobs are among the most sought-after roles according to Linkedin reports.

This Data Science course is for the passionately curious that want to work with Data to:

  • Help businesses leverage data for innovation and success
  • Innovate and predict future trends in business and other industries
  • Learn how to analyze data, and provide data-driven insights to make decisions
  • Elevate their careers or switch to Data Science in as little as 30 weeks

Become a Certified Data Scientist with access to 12 months of Graduate Support to land your next career opportunity once you complete your course work.

Course Details

Find out the pacing options available, price, and more information about the Part-time Data Science Course at Moringa.

Curriculum Developed by:

Flatiron School

Duration:

30 weeks

Mode:

Live & Online | Mon to Fri from 6pm – 9pm | Saturdays from 9am – 6pm

Tuition Fees:

Ksh 200,000 ( USD 2000 )
Download fees installment plans on the Data Science Part-time fees installation plans document

Course Prerequisites

To become a data scientist, you will need some understanding of Software Engineering fundamentals, Statistics, and the ability to apply all the knowledge in new and dynamic domains. 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.

All applicants need to meet the criteria outlined below to gain admission and succeed in the 30 weeks program:-

  1. Have a basic understanding or strong background in math & statistics concepts.
  2. Have a university/college education (ongoing students or graduates will be more eligible to join).
  3. All applicants are required to complete the application process by taking a technical assessment test and pass.
  4. Have a laptop with the following specs (core i5 upwards, 8GB RAM, 500GB upwards of storage).
  5. Have stable internet access.
  6. Students are required to be available for the part-time course from Mon-Fri and be present in at least 90% of the class sessions.

Moringa Part-time Data Science Course Curriculum

At Moringa we guarantee you a unique learning experience and curriculum design. We deliver a cutting-edge and comprehensive curriculum by offering you:-

  • Project-based learning with real-life data sets
  • Technical Mentors
  • Live instructor classes
  • Access to community
  • Career & Graduate support services

The Data Science Course is beginner friendly and takes 30 weeks. During the first 8 weeks, students will be introduced to the Moringa Learning platforms and cultural norms. After orientation students will deep dive into phase 0 & phase 1 where they will learn Data Science principles, Software Engineering principles, Introduction to Python Programming and Data Analysis & Engineering fundamentals. Later on they deep dive into 22 weeks of Advanced Data Science, Scientific Computing & Quantitative Methods, Machine Learning, Project Work and Soft Skills for Tech Professionals.

Orientation, Pre Work and Introduction to Data Science Principles ( 3 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 as your coursemates.

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

Data Analysis & Engineering ( 5 weeks )

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.

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.

Scientific Computing & Quantitative Methods ( 6 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 leading self, working with others, project management, career readiness, communicating for impact & Entrepreneurial Thinking

Machine Learning Fundamentals ( 5 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.

Advanced Machine Learning ( 5 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 ( 5 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.

Career Opportunities for our Data Science Graduates

Ready to take a step in transforming your career?