Next class starting:

• January 22 – February 22, 2018

Weekly Schedule

• Every week,
• Monday and Thursday
• 7:00 PM -9:00 PM (US Pacific Standard Time) each day

Please confirm your local time

Training material, lab exercises and recordings will be shared after each session with students.

Course Overview

This Data Science course will help you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes using R. You’ll learn the concepts of Statistics, Time Series, Text Mining and an introduction to Deep Learning. You’ll solve real life case studies on Media, Healthcare, Social Media, Aviation, HR.

About this course

In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Machine Learning platform, or with R, and Python on Azure stack.

What you will learn in this course?

• Explore the data science process
• Probability and statistics in data science
• Data exploration and visualization
• Data ingestion, cleansing, and transformation
• Introduction to machine learning
• The hands-on elements of this course leverage a combination of R, Python, and Machine Learning

What are the pre-requisites?

• Python programming knowledge
• Basic machine learning knowledge (especially supervised learning)
• Basic statistics knowledge (mean, variance, standard deviation, etc.)
• Linear algebra (vectors, matrices, etc.)
• Calculus (differentiation, integration, partial derivatives, etc.)

Course Outline

• Data Preprocessing
• Linear And Logistic Regression Models.
• Decision Trees and Random Forest.
• Naive Bayes and Support Vector Machine.
• K-means and Hierarchical Clustering.
• Natural Language Processing.
• Artificial Neural Networks.
• Convolutional Neural Network.