Data science is making us smarter and more innovative in so many ways. How does it all work? In this Data Science and Analytics Professional Certificate program you will gain insight into the latest data science tools and their application in finance, health care, product development, sales and more. With real world examples, we will demonstrate how data science can improve corporate decision-making and performance, personalize medicine and advance your career goals.
Taught by a distinguished team of professors at Columbia University’s Data Science Institute, this program is perfect for anyone who wants to understand basic concepts in data science without getting into the weeds of programming. Aimed at organization leaders, business managers, health care professionals and anyone considering a career in data science, this program will steep learners in the fundamentals of statistics, machine learning and algorithms. It will also introduce emerging technologies such as the Internet of Things (IoT) , or wirelessly connected products, and techniques that allow computers to summarize mountains of text, audio and video. Concrete examples provided throughout the program will ensure that learners fully grasp and master key concepts.
Courses under this program: Course 1: Statistical Thinking for Data Science and Analytics
Learn how statistics plays a central role in the data science approach.
Course 2: Machine Learning for Data Science and Analytics
Learn the principles of machine learning and the importance of algorithms.
Course 3: Enabling Technologies for Data Science and Analytics: The Internet of Things
Discover the relationship between Big Data and the Internet of Things (IoT).
The Internet of Things is rapidly growing. It is predicted that more than 25 billion devices will be connected by 2020.
In this data science course, you will learn about the major components of the Internet of Things and how data is acquired from sensors. You will also examine ways of analyzing event data, sentiment analysis, facial recognition software and how data generated from devices can be used to make decisions.
Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications.
This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.
This statistics and data analysis course will pave the statistical foundation for our discussion on data science.
You will learn how data scientists exercise statistical thinking in designing data collection, derive insights from visualizing data, obtain supporting evidence for data-based decisions and construct models for predicting future trends from data.
Tian Zheng, Kathy McKeown, Ansaf Salleb-Aouissi, Cliff Stein, David Blei, Itsik Peer, Mihalis Yannakakis, Peter Orbanz, Fred Jiang, Julia Hirschberg, Michael Collins, Shih-Fu Chang , Zoran Kostic, Andrew Gelman , David Madigan, Lauren Hannah, Eva Ascarza and James Curley
1. Contents presented do not match to what one would expect from "Data Science for Executives", and to what is described in the course syllabus. A large part of the courses is a random collection of contents that are only remotely relevant or connected to data science.
2. Also this course is in no way relevant or suitable for "Executives" as suggested by the course series title. Much too detailed and failing to give a "big picture" overview of the field!
Overall it makes the impression of a random collection of (mediocre) lectures, that were bundled together and named for maximum commercial impact