 Selfpaced, online learning
 Intermediate skill level
 Personalized support
 COS certificate
 15 ECTS credits
Next learners start 15 March 2018
Enroll today!The Applied Data Science: Machine Learning program will give you handson experience in one of the hottest areas of data science. You will learn tools for predictive modeling and analytics, harnessing the power of neural networks and deep learning techniques across a variety of types of data sets.
Each of the four courses in this program will let you demonstrate your newlyacquired skills through a course project. ECTS credits will be awarded to learners who successfully complete all four courses and course projects as well as a final capstone project.
These course details are subject to change; please refer to the program outline at the time of registration.
Courses

1 — Introduction to Data Analysis with Python
 Getting Started
 Exploring our First Data Set
 The Jupyter Notebook
 A First Look at NumPy
 A First Look at Pandas
 The Basics of Data Visualization
 Probability for Data Science
 Linear Algebra for Data science
 Course Project

2 — Applied Data Analysis
 Getting the Data
 Cleaning the Data
 Manipulating the Data
 Working with Textual Data
 Working with Timeseries Data
 Databases in Python
 Statistical Data Analysis
 Course Project

3 — Applied Machine Learning 1
 Introduction to Machine Learning
 Fitting a first model
 Cost functions and outliers
 Linear regressions
 Gradient descent
 Feature engineering
 Regularization
 Course Project

4 — Applied Machine Learning 2
 Knearest neighbors
 Biasvariance tradeoff
 Logistic regressions
 Decision trees and SVMs
 Clustering and dimensionality reduction
 Introduction to deep learning
 Convolutional neural networks
 Chatbots by Swisscom
 Course Project

Capstone Project
Prerequisites
This course is taught at the intermediate level. You should have the following skills and abilities prior to registering for this course:
 English at B2 level
 Basic understanding of algebra, geometry, calculus (derivatives), probability and statistics
 Familiarity with computer environments (what is a program, file system, file formats, terminal, programming language library)
 Prior experience with any programming language