Who should attend
This course is highly effective for professionals looking to fill this talent gap and further the use of data science to solve real-world problems.
Previous batches have come from
- Industries: Banking, Software, Consulting, Education, Telecommunication, Healthcare and Energy industries.
- Countries: United States, India, United Kingdom, Canada, Australia, France, Mexico, Germany.
About the course
Data has been called the new global currency, and its meteoric rise is transforming entire industries—and driving the demand for practitioners who can wield its power. From health care and finance to entertainment, cyber security and beyond, the need for data scientists continues to grow in tandem with opportunities for career advancement within the field.
To help fill this talent gap and further the use of data science to solve real-world problems, Columbia Engineering Executive Education has partnered with Emeritus to create the Applied Data Science course.
Since this course requires an intermediate knowledge of Python, you will spend the first part of this course learning Python for Data Science taught by Emeritus. This will provide you with the comprehensive programming knowledge required to put data to work & derive the maximum benefit from the Applied Data Science course.
WHAT ARE THE LEARNING OUTCOMES?
Explore the theory, languages and concepts of this in-demand field while acquiring the Python programming knowledge you need to solve real-world data challenges. At the end of the course, you will be able to:
- UTILIZE Python programming language to code your own algorithms or analytical models to examine data.
- MANAGE and manipulate a large amount of data using Python packages.
- REVEAL hidden yet important characteristics of any dataset using visual tools already built into Python.
- FORMULATE explanations for past events and determine if it is true using available data.
- DISCOVER hidden trends and draw useful insights using software packages designed for use with Python, like NumPy and Pandas
- UNEARTH quality information from analysis of text-data like Facebook or Twitter posts and comments
- CLASSIFY data points in a larger dataset; for example, assigning genres to one billion songs.
- IDENTIFY relationships between data points to form groups in a larger data set; for example, grouping customers into segments using their previous buying patterns.
Emeritus and Columbia Engineering Executive Education
Columbia Engineering Executive Education is collaborating with online education provider Emeritus to offer executive education courses.
An Emeritus Certificate course created in collaboration with Columbia Engineering Executive Education is based on syllabus approved by Columbia Engineering Executive Education, and contains video content created and recorded by Columbia Engineering Executive Education faculty, combined with assessments, assignments, projects, cases, and exercises delivered by Emeritus. Upon successful completion of the course, learners will be awarded a certificate jointly by Emeritus and Columbia Engineering Executive Education.
PART 1: PYTHON FOR DATA SCIENCE (VIDEO CONTENT AND DELIVERY BY Emeritus)
- Module 1: Introduction to Data Science
- Module 2: Working with Data Types and Operators in Python
- Module 3: Writing Functions in Python
- Module 4: Popular Data Science Packages in Python
- Module 5: Intermediate Python
- Module 6: Data Manipulation and Analysis with Pandas
- Module 7: Data Visualization
- Module 8: Random Variables and Statistical Inferences
- Module 9: Statistical Distributions and Hypothesis Testing
PART 2: APPLIED DATA SCIENCE (VIDEO CONTENT FROM COLUMBIA ENGINEERING AND DELIVERY BY Emeritus)
- Module 1 - Data Analysis & Visualization – Part 1
- Module 2 - Data Analysis & Visualization – Part 2
- Module 3 - Statistical Distributions
- Module 4 - Statistical Sampling
- Module 5 - Hypothesis Testing
- Module 6 - Regression Models in Python
- Module 7 - Evaluating Data Models
- Module 8 - Classification with K-nearest Neighbors
- Module 9 - Decision Tree Models
- Module 10 - Clustering Models
- Module 11 - Text Mining in Python – Part 1
- Module 12 - Text Mining in Python – Part 2
Data Exploration using Lending Club Loan Data
- Use Python’s NumPy library to explore and uncover insights in Lending Club’s loan data.
- Using Python’s powerful Pandas library to wrangle and munch Lending Club’s loan data.
Data Wrangling using CNC Mill Tool Wear Data
- Practice using Python’s data framework to process and manipulate data with the CNC Mill Tool Wear dataset.
- Hone your data wrangling and munching skills using Python’s pandas and NumPy libraries with the CNC Mill Tool Wear dataset.
Hypothesis Testing using Cancer Atlas Data
- Statistically test the impact of health factors in relation to cancer rates from around the globe.
Natural Language Processing (NLP) implementation using Amazon product reviews
- Implement Natural Language Processing (NLP) techniques to automate the understanding of product reviews from Amazon.
Emeritus follows a unique online model. This model has ensured that nearly 90 percent of our learners complete their course.
The first week is orientation week. During this week you will be introduced to the other participants in the class from across the world. You will also learn how to use the learning platform and other learning tools provided.
On other weeks, you have learning goals set for the week. The goals would include watching the video lectures and completing the assignments. All assignments have weekly deadlines. Recorded Video Lectures
The recorded video lectures are by faculty from the collaborating university.
Every few weeks, there are live webinars conducted by Emeritus course leaders. Course leaders are highly-experienced industry practitioners who contextualize the video lectures and assist with questions you may have regarding your assignments. Live webinars are usually conducted between 1 pm and 3 pm UTC on Tuesdays and Wednesdays.
In addition to the live webinars, for some courses, the course leaders conduct Office Hours, which are webinar sessions that are open to all learners. During Office Hours, learners ask questions and course leaders respond. These are usually conducted every alternate week to help participants clarify their doubts pertaining to the content.
The Emeritus Program Support team members will follow up and assist over email and via phone calls with learners who are unable to submit their assignments on time.
Continued Course Access
You will continue to have access to the course videos and learning material for up to 12 months from the course start date. Assignments/Application Projects
Assignments are given out weekly and they are based on the lectures or tutorials provided. They need to be completed and submitted as per the deadline for grading purposes. Extensions may be provided based on a request sent to the support team.
It is an open forum where participants pin their opinions or thoughts regarding the topic under discussion.
Emeritus Program Support Team
If at any point in the course you need tech, content or academic support, you can email program support and you will typically receive a response within 24 working hours or less. Device Support
You can access Emeritus courses on tablets, phones and laptops. You will require a high-speed internet connection. Emeritus Network
On completing the course you join a global community of 5000+ learners on the Emeritus Network. The Network allows you to connect with Emeritus past participants across the world.
BENEFITS TO THE LEARNER
- Global Business Education
- Rigorous and experiential curriculum
- World-renowned faculty
- Globally connected classroom: peer to peer learning circles
- Action learning: learning by doing
- Certificate from Emeritus in collaboration with Columbia Engineering Executive Education
- Build new networks through peer interaction
- Benefit from diverse class profiles
- Professional acceleration through our enriched leadership toolkit
- Learn while you earn
- Get noticed. Get ahead.
Hardeep Johar received an M.A. in Economics from the Birla Institute of Technology and Science and is a Fellow of the Indian Institute of Management Calcutta. He received a Ph.D. in Information Systems from the Stern School of Business, New York University in 1994. Prior to joining Columbia, Joha...
Biography Costis Maglaras is a Professor at the Graduate School of Business at Columbia University in the division of Decision, Risk & Operations. His research focuses on quantitative pricing and revenue management, the economics, design, and operations of service systems, and financial engin...
Professor Vineet Goyal received his Bachelor's degree in Computer Science from Indian Institute of Technology, Delhi in 2003 and his Ph.D. in Algorithms, Combinatorics and Optimization (ACO) from Carnegie Mellon University in 2008. Before coming to Columbia, he spent two years as a Postdoctoral A...
Videos and materials
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.