RealLife Data, Interactive Online Classes
Start your data science career by joining our online classes today. The classes are live, with realtime support, and practical sessions and projects.
Applications close on August 17th 2020.
Apply now
Data Science Prep Application Form
What is data science?
Data science is the discipline concerned with extracting knowledge from different types of data. Organizations and businesses then use these insights to make decisions.
The Moringa School Online Data Science course prepares you for a wide range of careers in business, health, engineering, and other industries. This comprehensive course entails;
Interactive learning experience
 Tutors deliver lessons online via video using Zoom
 You have access to course notes on Canvas (our learning system)
 You have daily checkins via Zoom to meet up with classmates
 You work on practical projects with real data to cement your skills
 You can book your tutor for one on one sessions
 Daily classes on weekdays from 8 am to 5 pm except during public holidays
Real projects with real data
Moringa School has partnered with Dalberg, a global firm with a large research footprint to provide practical data.
You will use real data to work on different problems and projects to prepare for a successful career.
Market Inspired Course Content
You will use technologies and tools that are being used in the real world to gain relevant skills.
The curriculum at Moringa School is designed to meet current and future needs of businesses and organizations.
The course is in two stages;
Prerequisites
To join this course you must have completed high school. You’ll also do a short test after applying.
Moringa School Online Data Science Prep
What you’ll learn
Moringa School’s Data Science Prep an introductory course.
After Data Science Prep, you will get the skills to learn various tools and tactics that are taught in the Data Science Core program.
You’ll interact with data and learn the basic principles of data science.
At the end of the course, you’ll know how to use tools such as Pandas and NumPy.
You’ll also be able to use Python and SQL and understand how to manage and contribute to projects.
Curriculum
Introduction to Data Science
 Becoming a data scientist. By the end of this unit, you should be able to understand what it takes to become a data scientist.
 The data science project lifecycle. Adapt the project lifecycle of a typical data science project.
 Ethics in data science. Demonstrate a sophisticated awareness of ethical implications relevant to the use of data.
Tools: Standups, Checkins and CRISPDM.
Logic for Data Science
 Workflow and version control. By the end of this unit, you should be able to write code and document your workflow in a programming environment.
 Python programming. Recall the basics of Python programming for data science.
 SQL programming. Obtain and manipulate data from various types of databases using the SQL language.
 Project management with Jira. Manage a team deliverable using a standard project management tool.
Tools: Python, Numpy, SQL, MySQL, Postgres, Git, Github and Jira.
Data Preparation
By the end of this unit, you should be able to:
 Evaluate the integrity of data by making decisions on data quality issues
 Perform the extraction, querying, and aggregation of data for analysis in multiple projects through common techniques and tools
 Understand mechanisms for missing data, outliers and analytic implications.
Tools: Python, Pandas and Matplotlib.
Moringa School Online Data Science Core
Core is the stage at which you learn the advanced data science concepts and tools.
After this stage, you will be a competent data scientist who can create, supervise, audit, and execute complex projects.
Curriculum
Statistics for Data Science
Week 1: Data Visualisation and Presentation
 Principles of Constructing Data Visualizations
 Storytelling with Data
 Introduction to Visualization Tools: Matplotlib, Seaborn and Tableau
 Visualization with Tableau
Week 2: Descriptive Statistics
 Introduction to Descriptive Statistics
 Univariate Analysis
 Bivariate Analysis
 Multivariate Analysis  Reduction Techniques
Week 3: Sampling, Distributions and Time Series

Introduction to Sampling Techniques
 Simple Random Sampling
 Stratified Sampling
 Cluster Sampling
 Systematic Sampling

Introduction to Statistical Distributions
 The Normal Distribution
 Student's t Distribution
 ChiSquare Distribution
 The Poisson Distribution
 The Binomial Distribution
 The Bernoulli Distribution
 Prior and Posterior Distributions

Introduction Time Series
 Components of Time Series
 Smoothing and Differencing
 AutoRegressive Moving Average (ARIMA) Models
Week 4: Hypothesis Testing & Introduction to Bayesian Statistics

Hypothesis Testing
 Introduction to Hypothesis Testing
 Hypothesis Formulation
 Tests
 PValues
 Types of Errors
 Hypothesis Testing: Results Interpretation
 Hypothesis Testing: Case Studies

Introduction to Bayesian Statistics
 Bayes Theorem
 Bayesian Estimation
 Point Estimation
 Interval Estimation
 Maximum Likelihood Estimation
Week 5: Project Week: Statistics for Data Science
 During this week students work in group projects to apply the concepts learned throughout the previous module using reallife data sets
Supervised Learning with Python
Week 6: Introduction to Supervised Learning
 Crafting good datasets
 Linear Regression
 Polynomial Regression
 Logistic Regression
 Hyperparameter Tuning
 Ethical Implications of Supervised Learning
Week 7: Advanced Regression
 Quantile Regression
 Ridge Regression
 Lasso Regression
 Elastic Net Regression
Week 8: Decision Trees
 Introduction to Decision Trees

Optimizing Decision Trees
 Random Forests
 Bagging and Boosting
 Nodes
 Introduction to Support Vector Machine
 Advanced Support Vector Machine
Week 9: The KNearest Neighbours (KNN)
 Introduction to KNN
 Model performance  KNN
 The Naive Bayes Classifier
 Model performance  Naive Bayes
Week 10: Neural Networks
 Introduction to Neural Networks
 Activation Function
 Optimization of Neural Networks
 Regressors and Neural Networks
 Recurrent Networks
 Convolution Neural Networks
Week 11: Project Week: Supervised Learning with Python
 During this week students work in group projects to apply the concepts learned throughout the previous module using reallife data sets
R for Data Science
Week 12: Fundamentals of R in Data Science
 Operations, Lists, and Vectors
 Matrices
 Factors, Dataframes, Datatables and Tibbles
 Handling Missing Data with R
 Handling Outliers with R
 Handling Duplicate Data with R
 Formatting Data with R
 Handling Obvious Inconsistencies with R
 Univariate analysis in R
 Bivariate and Multivariate Analysis in R

Supervised Learning in R
 Regression
 KNN
 Decision Trees
Week 13: Unsupervised Learning with R
 Kmeans Clustering
 Hierarchical Clustering
 Dimensionality Reduction with PCA
 tDistributed Stochastic Neighbour Embedding (tSNE)
Week 14: Project Week: R for Data Science
 During this week students work in group projects to apply the concepts learned throughout the previous module using reallife data sets.
Week 15: Content Review Week: Data Science Core
 During this week difficult concepts from the previous modules are revisited and any pending roadblocks are addressed.
Week 16  18: Project period
 Employer centred Ideation (students work in groups on their capstone project).

Professional development workshops such as:
 Mock interviews
 CV & cover letter building
 LinkedIn etc.
Internet access and attendance
 Students must attend 70% of classes
 Moringa School supports students to get internet for online learning
 Learners must checkin in the morning then check out in the evening
Applications due by August 17th, 2020
Apply now
Testimonials
The program exceeded my expectations in terms of what I wanted to gain in a day.
For example, I have been able to understand R and some bit of supervised machine learning. Teacher Kamande was very helpful in terms of sharing his knowledge. I would recommend other guys to pursue this course.
Johnbosco Mulei
Service Management Analyst at Safaricom PLC
I developed an interest in Data Science and I started looking at options on where I could do it. Moringa School came to mind because I had heard of so much good reviews about their programs.
My initial expectation was that I would be here to learn more on exploratory data analysis and understand more about machine learning using R.
They were all met. I am looking forward to joining the fulltime program.
Carol Wambui
Statistician from Kenyatta University
FAQs
Q.
Do you guarantee employment upon completion of this course?
A.
No.
What we guarantee is careerready skills.
Our courses are practical and relevant to the market. That is why many of our graduates find jobs.
Moringa School offers support through training and informing graduates of job opportunities. Graduates then apply and some get these jobs.
Other graduates have found jobs without our direct support while others have ventured into freelancing and entrepreneurship.
Moringa School is proud to have helped all these brilliant young people achieve their career goals.
Moringa School graduates have gone on to succeed in various careers such as freelancing and entrepreneurship. Others have gone on to work in reputable companies such as;
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