The MIIA plan to promote, facilitate and/or host local training courses across the African continent. We intend to work with any company or organization that provides quality Machine Intelligence and Data Science related courses, either on-premise or on-line via Massive Open Online Courses (MOOCs). There are plenty online resources available for Machine Intelligence and Data Science practitioners such DeepLearning.net, Data Science Central, KDnuggets, FastML, Galvanize, Quora - Data Science, Data Science Masters, Data Science Academy, DeepLearning.University, and many more as listed on NGDATA, KDnuggets - best data science online courses, Data Science on Class Central as well as training sources listed on the following github sites (it is recommended to clone these git sources to stay in sync with the latest updates):

- https://github.com/josephmisiti/awesome-machine-learning
- https://github.com/okulbilisim/awesome-datascience

If you know of any courses in your local area that we should promote on the MIIA website or via our real-time community messaging platform (MIIA on Slack), please let us know either via our #events channel on Slack or an email to info@machineintelligenceafrica.org.

**MOOC list from Awesome Data Science**

- Google Making Sense of Data
- Coursera Introduction to Data Science
- Data Science - 9 Steps Courses, A Specialization on Coursera
- Data Mining - 5 Steps Courses, A Specialization on Coursera
- Machine Learning – 5 Steps Courses, A Specialization on Coursera
- CS 109 Data Science
- Schoolofdata
- OpenIntro
- Data science MOOC
- CS 171 Visualization
- Process Mining: Data science in Action
- Oxford Deep Learning
- Oxford Deep Learning - video
- Oxford Machine Learning
- UBC Machine Learning - video
- Data Science Specialization
- Coursera Big Data Specialization
- Data Science and Analytics in Context by Edx
- Big Data University by IBM
- Udacity - Deep Learning

**Visualization and Graphic Design courses**

- See also Online Courses Review's graphic design page which provides an overview of the wide range of graphic design courses available including reviews of the course materials. This gives people plenty of options if Data Visualization and D3.js isn’t quite right for them.

Launch your Data Science career with this introductory course. Build a solid foundation in R and start exploring data-related careers.

Courses: https://www.experfy.com/training/courses

Corporate Training: https://www.experfy.com/training/business

Algorithmic Trading Strategies

Adopting Hadoop for the Enterprise: From Strategy to Roadmap

Getting Started with Apache Cassandra

Gain Competitive Advantage using Microsoft Azure DataPlatform and Cortana Analytics

Big Data - What Every Manager Needs to Know

Mastering Data Visualization Using Tableau: From Basic to Advanced

Supply Chain Optimization Analyst Training

Experfy's Data Science Certification taught by industry experts at Harvard, Columbia, Cisco, Nokia and State Farms. Experfy instructors are industry thought leaders who provide you with in-depth training in introductory topics like statistics to advanced ones like machine learning. This certification has 5 courses

Probability and Statistics - Harvard faculty teaches you how to apply statistical methods to explore, summarize, make inferences from complex data and develop quantitative models to assist business decision making.

Data Wrangling in R - Instructor is the founder of Analytics Incubation Center at Cisco and has 15 years of analytics development experience. This courses teaches you from start to finish real-world data preparation for further analysis using R.

Econometric Analysis: Methods and Applications - Quantitative and econometric analysis focused on practical applications taught by faculty member at the Department of International and Public Affairs at Columbia University

Classification Models - How to use classification algorithms to solve real world problems . Instructor is lead data scientist at one of the largest software companies in the world, author of a best-seller and an adjunct professor at University of Toronto

Clustering and Association Rule Mining - Learn concepts of Cluster Analysis and study most popular set of Clustering algorithms with end-to-end examples in R. Taught by Machine Learning Scientist with 9+ years of hands-on experience in predictive analytics domain at companies like Target, Symphony-IRI and Genpact.

Machine Learning Foundations: Supervised Learning

**Industry Specific**

Introduction to Applications of Data Science in theHealthcare Industry

Marketing Analytics: Text Analysis & RecommendationSystems

Increase Cross Selling and Upselling of Products andServices

Analytics forthe Internet of Things

Learn how to apply basic analytical methods to IoT Data. Apply the fundamentals of machine learning and statistics to extract value from IoT data. Understand different business use-cases for IoT data. Understand different types of IoT data

Implementingan Internet of Things (IoT) Business

This course is geared towards executives or managers responsible for implementing an IoT business in their companies.This course describes a business approach and walks through all the steps and decisions towards an IoT launch in seven 45 minute fast-paced sessions

This one-day fast paced deep dive into the Internet of Things is designed for business executives who want to understand what the Internet of Things is and the potential impacts it can have on a business

The purpose of this course is to provide a deep understanding of the digital technologies, infrastructure, and social political forces shaping the future of our urban environments. We begin by defining Smart Cites through lectures and case studies and drill down into the technologies shaping new and existing cities.

**Securing Enterprise Internet of Things Implementation**

Being able to pay for each course as you go or all at once makes Coursera’s specializations very attractive. If you either don’t want to spend a lot at once, or if you just want to get a taste of Data Science, Coursera’s paths are great for getting totally new learners off the ground.

Each specialization consists of a handful of courses that are usually taken in order and require some programming experience and working knowledge of mathematics up to algebra.

Although you may take the courses for free, you can also pay to receive certificates for each course you take, which will grant you an overall specialization certificate in the end.

**Institution:** Johns Hopkins University

**Courses Included in Specialization:**

**Institution:** Johns Hopkins University

**Courses Included in Specialization:**

**Institution:** University of Illinois at Urbana-Champaign

**Courses Included in Specialization:**

- Introduction to Data Science
- Process Mining: Data science in Action
- Genomic Data Science and Clustering (Bioinformatics V)
- Big Data Science with the BD2K-LINCS Data Coordination and Integration Center
- Computational Methods for Data Analysis
- Data Analysis and Statistical Inference
- Statistics: Making Sense of Data

**edX**

Like Coursera, edX also has courses bundled together to form a knowledge set, called Xseries. You can take these courses for free, or purchase verified certificates to complete the bundled track. So far, edX only offers one Xseries that is relevant to Data Science.

**Institution:** University of California Berkeley

**Courses Included in Specialization:**

- Data Science and Machine Learning Essentials
- Introduction to Computational Thinking and Data Science
- Introduction to R Programming
- Introduction to Computer Science and Programming Using Python
- Data Analysis: Take It to the MAX()
- Text Mining and Analytics
- Data, Analytics and Learning
- Implementing Real-Time Analytics with Hadoop in Azure HDInsight
- Big Data in Education
- Statistics and R for the Life Sciences
- Explore Statistics with R
- Text Mining and Analytics
- Introduction to Linear Models and Matrix Algebra
- Applications of Linear Algebra Part 1
- Applications of Linear Algebra Part 2
- The Analytics Edge
- CS For All: Introduction to Computer Science and Python Programming

Udacity only has one track, or what they call a Nanodegree, that is relevant to Data Science, and that’s the Data Analyst Nanodegree. The great difference between Udacity’s track and either Coursera’s or edX’s is that you get more interaction from the staff, such as feedback on your project and career advice.

Also note that the Nanodegree programs are not exactly course based, but instead project based. Udacity has a list of courses that it recommends to complete on its platform before embarking on the Nanodegree projects.

To pursue the Nanodegree, you’ll need to set aside $200 per month for 9-12 months, but Udacity provides an amazing benefit where you’ll get half of your tuition back if you graduate in less than 12 months.

**Recommended Courses for Data Analyst Nanodegree**

- Intro to Computer Science: Build a Search Engine & a Social Network
- A/B Testing: Online Experiment Design and Analysis
- Data Visualization and D3.js: Communicating with Data
- Intro to Machine Learning: Pattern Recognition for Fun and Profit
- Intro to Hadoop and MapReduce How to Process Big Data
- Real-Time Analytics with Apache Storm: The “Hadoop of Real-Time”
- Intro to Data Science: Learn What It Takes to Become a Data Scientist
- Data Analysis with R: Visually Analyze and Summarize Data Sets
- Intro to Statistics: Making Decisions Based on Data
- Intro to Descriptive Statistics: Mathematics for Understanding Data
- Intro to Inferential Statistics: Making Predictions from Data
- Data Wrangling with MongoDB: Data Manipulation and Retrieval
- Model Building and Validation: Advanced Techniques for Analyzing Data

You can approach learning on Dataquest in two ways: 1. you can choose one of three tracks for a more directed study, or you can pick any particular course and begin learning that topic. Dataquest focuses on teaching Data Science using Python, and the first lesson in each course is free.

**Price:** *$35/month *(for both tracks and courses)

**Steps:**

- Python Introduction
- Data Analysis and Visualization
- Statistics and Linear Algebra
- Machine Learning
- Advanced Python and Computer Science
- Advanced Topics in Data Science

**Steps:**

- Introduction to Python
- Python Applications
- Intermediate Python and Pandas
- Probability and Statistics

Datacamp has four different tutorial blocks that take you through many different chapters.

Curriculum:

- Introduction to R
- Intermediate R
- Data Manipulation with dplyr
- Data Analysis the data.tabl Way
- Data Visualization with ggvis
- Reporting with R Markdown
- A Hands-On Introduction to Statistics with R
- Introduction to Machine Learning
- Big Data Analysis with Revolution R Enterprise
- R for SAS, SPSS and STATA users –
*Elective* - How to work with Quandl and R –
*Elective* - Kaggle: R tutorial on Machine Learning –
*Elective*

- Introduction to R
- Introduction to data
- Probability
- Foundations for inference: Sampling distributions
- Foundations for inference: Confidence intervals
- Inference for numerical data
- Inference for categorical data
- Introduction to linear regression
- Multiple linear regression

- Return calculations
- Random variables and probability distributions
- Bivariate distributions
- Simulating time series data
- Analyzing stock returns
- Constant expected return model
- Introduction to portfolio theory
- Computing efficient portfolios using matrix algebra

O’Reilly offers over 150 hours of exclusive training videos under its data oriented learning paths. Unlike many of the other course routes listed here, O’Reilly’s paths are pure video content, but they have several projects for you to do scattered throughout the lessons. O’Reilly allows anyone to see several of the videos in any path for free, so click on any of the path titles below to check them out.

This path is 24 hours long and takes you from beginner to an advanced level in R. You’ll begin at the very start with installation of R, and go from statistical models, to visualizing data, to machine learning, to working with Microsoft Azure and R together.

**Lessons:**

- Learning to Program with R (~4 hours)
- Introduction to Data Science with R (~8.5 hours)
- Expert Data Wrangling with R (~4 hours)
- Writing Great R Code (~1 hour)
- Data Science with Microsoft Azure and R (~7 hours)

The Machine Learning path is 23 hours long, and will take you through 6 courses, which includes several hours of video training on deep learning, algorithms, and data structures.

**Lessons:**

- An Introduction to Machine Learning with Web Data (~3 hours)
- Advanced Machine Learning (~2 hours)
- Deep Learning (~2 hours)
- Hardcore Data Science NYC 2014 (~5 hours)
- Hardcore Data Science California 2015 (~6 hours)

At 14 hours of training, you’ll not only learn all about visualizing data with D3.js, but also how to effectively communicate what your data is saying.

**Lessons:**

- An Introduction to d3.js: From Scattered to Scatterplot (~3 hours)
- Learning to Visualize Data with D3.js (~4 hours)
- Using Storytelling to Effectively Communicate Data (1.5 hours)
- Effective Data Visualization (~3 hours)
- Intermediate D3.js (~4.5 hours)

The Hadoop video training is 16 hours long, and in it you’ll get a good intro to Apache Hadoop and other technologies in the Hadoop ecosystem, like HDFS, MapReduce, Hive, Pig, and Impala. By the end you’ll understand how to work with Hadoop and large datasets and perform analytical procedures.

**Lessons:**

- Learning Apache Hadoop (~7.5 hours)
- Hadoop Fundamentals for Data Scientists (~6 hours)
- Architectural Considerations for Hadoop Applications (~2.5 hours)

This learning path is 19 hours long, and has an excellent intro to Python with lots of examples and exercises. You will also get a tutorial on iPython Notebook, which is an amazing tool to discover if you’ve never used it before. Lastly, you’ll receive a copious amount of content on algorithms and data structures in Python.

**Lessons:**

- Introduction to Python (~3.5 hours)
- Learning iPython Notebook (~3 hours)
- Working with Algorithms in Python (~8.5 hours)
- Python Data Structures (~4 hours)

At 62 hours of video training, the *SQL and Relational Databases* course is the longest learning path that O’Reilly offers. This series is incredibly thorough, and the instructors, one of whom is a cofounder of relational database theory, will take you from a total beginner to an advanced SQL and relational database practitioner.

**Price: ***$1299.99*

**Lessons:**

- Learning SQL (~3.5 hours)
- Learning SQL For Oracle (~9 hours)
- Relational Theory for Computer Professionals (~10 hours)
- SQL: Beyond the Basics (~4 hours)
- Learning Data Modeling (~8 hours)
- Time and Relational Theory (~12 hours)
- Nullology (~1 hour)
- The Closed World Assumption (~1.5 hours)
- An Introduction to Set Theory (~1 hour)
- Nulls, Three-Valued Logic, and Missing Information (1 hour)
- View Updating (~1 hour)
- Normal Forms and All That Jazz Master Class (~10 hours)

Unlike many other paid Data Science course programs, Sliderule offers 1-on-1 mentorship each week. Sliderule doesn’t offer any free options, and is actually more expensive than other options if you take too long to complete it.

The foundations track focuses on R and is geared towards everyone starting from the ground up in Data Science.

**Curriculum:**

- Probability & Statistics
- R Basics
- Exploratory Data Analysis
- Data Visualization
- Data Wrangling
- Analytics Techniques
- Capstone Project

Click here to download Sliderule’s Foundations of Data Science syllabus

The Intensive track is focused on using Python for Data Science and the course setup is more for people that already have backgrounds in mathematics and computer science.

**Curriculum:**

- Programming Tools (Python)
- Data Wrangling
- Data Story
- Inferential Statistics
- Machine Learning
- Capstone Project
- Career Resources

Click here to download Sliderule’s Data Science Intensive syllabus

Data Origami offers screencasts that range in difficulty from beginner to advanced. Since the creator, Cameron Davidson-Pilon is also the author ofthe open source book Bayesian Methods for Hackers, you can expect some very interesting videos on useful statistics for Data Science.

**Screencasts:**

**A/B Testing Conversion Rates****Bayesian Beta-Binomial Model****Bayesian Modelling (Car Arrival Problem)****Create Markov Chains Using Your Chrome Browsing History****Estimating the Hazard Function****Estimating the Survival Function****Sorting Colours using PCA****Intro to PCA****Sampling from Discrete Distributions****Scraping the Web using Pandas****Survival Analysis Bundle Pack****Using Patsy for Categorical Data****Visualizing PCA’s Information Loss****Why Should I Be Interested in Survival Analysis?****Determining Ages using First Name Data**

- Data School – Data science for beginners! | Data Science
- edureka! | Data Science
- Zipfian Academy | Data Science
- David Langer | Data Science with R
- Derek Kane | Data Science
- MarinStatsLectures | Statistics
- LearnR | R programming
- Christoph Scherber | Statistics
- Brandon Foltz | Statistics
- statisticsfun | Statistics
- Java and R Tutorials | R programming
- bigdata simplified | All things big data
- Derek Banas | Playlists on SQL and Python

- A list of colleges and universities offering degrees in data science.
- Data Science Degree @ Berkeley
- Data Science Degree @ UVA
- Data Science Degree @ Wisconsin
- Master of Information @ Rutgers
- MS in Business Analytics @ ASU Online
- Data Science Engineer @ BTH

***** https://github.com/okulbilisim/awesome-datascience