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 is a course for the passionately curious that want to work with Data to:
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 Data Science Course at Moringa.
Live & Online | Mon to Fri from 8am – 5pm
Ksh 174,000 ( USD 1740 )
Download fees installment plans on the Data Science Full-time fees installation plans document
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.
All applicants need to meet the criteria outlined below to gain admission and succeed in the 20 weeks program:-
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.
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.
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.
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.
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
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.