Overview

Managing a business or organization in today’s world is more science than art. At the core of that science is data, and the ability to unleash its power and extract value from it is critical to any company’s success. Data science and machine learning have transformed entire industries and continue to do so. The data revolution has led to a spike in the demand for data scientists and machine learning practitioners that shows no signs of slowing down.

The Postgraduate Diploma in Applied Data Science is designed to help participants master data science, from the critical foundations of statistics and probability to working hands-on with machine learning models using Python, the world’s most popular programming language. Analytical models are more powerful when they are built with the right statistics, and this comprehensive diploma can help you learn the key statistics and probability concepts to build effective models, enhance your data interpretation skills and make well-informed decisions.

Emeritus and Columbia Engineering Executive Education

Columbia Engineering Executive Education is collaborating with online education provider Emeritus Institute of Management (Emeritus) to offer executive education courses.

An Emeritus Postgraduate Diploma contains multiple Emeritus Certificate courses created in collaboration with Columbia Engineering Executive Education, and may also include courses created independently by Emeritus. Upon successful completion, learners will be awarded a Postgraduate Diploma by Emeritus.

You can read more about the collaboration here.

GET PROGRAM INFO

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  • Starts on

    September 30, 2020

  • Duration

    9 Months, Online

    (6-8 hours per week)
  • Program Fees

    US$ 3,000*

Curriculum & Faculty

syllabus

    Python Basics - How to Translate Procedures into Code

  • Python data types (basic and Boolean), conditional statements, functions, assignment operations
  • Intermediate Python - Data Structures for Analysis

  • Lists, dictionaries, mutability, and iterations with examples on data structures
  • Relational Databases - Where Big Data Is Typically Stored

  • Basics of databases and normalization
  • SQL - Ubiquitous Database Format/Language

  • Using SQL for Python, SQL workbench, working with multiple tables
  • Data Extraction - Getting Data from the Internet - Part 1

  • Extracting data from the web using JSON, Google API, and XML
  • Data Extraction - Getting Data from the Internet - Part 2

  • Using the Beautiful Soup mechanism to extract data, the Epicurious example

    Statistical Distributions - The Shape of Data

  • Types of distributions: Normal (examples), Poisson, Geometric, Exponential, Lognormal, and Bernoulli
  • Sampling - When You Can't or Won't Have ALL the Data

  • Size and sampling techniques, central limit theorem and motivation, sample size distribution (fixed sample size), polling techniques (given sample size, given target accuracy), estimating proportions
  • Hypothesis Testing - Answering Questions About Your Data

  • Calculating and interpreting confidence levels, t-tables and t-multipliers, determining P-values and A/B testing example
  • Data Analysis and Visualization - using Python's NumPy for analysis

  • Introduction to using Numpy and Pandas for data visualization, Pandas datareader, time-series analysis, risk return analysis, regression
  • Data analysis and visualization - using Python's Pandas for Data Wrangling

  • Data cleaning and data visualization using Pandas, using the groupby function to organize data

    Machine Learning - Basic Regression and Classification

  • Machine learning using wines dataset and rocks and mines dataset, classification metrics, classification metrics using rocks and mines dataset
  • Linear Regression

  • Introduction to linear regression, using dummy variables in regression, measuring outputs of regression, making predictions with regression, collinearity, overfitting and how to prevent it
  • Logistic Regression

  • Introduction to regression, classification problems, and building a logistic regression model, and practice
  • Machine Learning - Decision Trees and Clustering

  • Understanding decision trees – example and visualization, regression trees (using the wines dataset), classification trees (rocks and mines dataset)
  • Ensemble Methods

  • Decision trees, bagging and boosting concepts, feature importance, and hyperparameter tuning
  • Naïve Bayes Classifiers

  • Discrete and conditional probabilities, Baye’s theorem, spam filtering, and practice
  • Neural Networks

  • Neural networks in keras, the perceptron, real-life examples: movie review classification and predicting housing prices
  • K-means Clustering

  • Unsupervised models, k-means clustering models and examples, Gaussian mixtures and examples
  • Dimensionality Reduction

  • Data projections, dimensionality reduction (DR), other DR techniques, principal component analysis
  • Text Mining - Automatic Understanding of Text

  • Text mining techniques: sentiment analysis, complexity analysis, and named entity analysis, text summarization, and topic modelling techniques
  • Time Series Analysis

  • Datetime and introduction to time series, exploring time series, descriptive statistics, partial autocorrelation, autoregressive models, the ARIMA model
Acting in the role of consultant, test the efficacy of an office supply company’s telemarketing campaigns for a select audience and help them leverage the test results to their advantage.

Application Assignments

Data extraction & web scraping using Beautiful Soup

Data extraction & web scraping using Beautiful Soup

Data distribution & sampling using office supply data

Data distribution & sampling using office supply data

Hypothesis testing

Hypothesis testing

Data analysis & visualization using lending club data

Data analysis & visualization using lending club data

Linear regression using house pricing data

Linear regression using house pricing data

Machine learning classification using handwritten digits data

Machine learning classification using handwritten digits data

Decision trees using Abalone data

Decision trees using Abalone data

Text mining using Amazon review data

Text mining using Amazon review data

Unsupervised ML using Iris data

Unsupervised ML using Iris data

Time series analysis using Amazon stock prices

Time series analysis using Amazon stock prices

Faculty

Hardeep Johar
Hardeep Johar

Senior Lecturer Of Industrial Engineering And Operations Research

Vineet Goyal
Vineet Goyal

Associate Professor Industrial Engineering And Operations Research

Course Leaders

*Course Leaders are subject to change

Phil Capobres

Course Leader, Emeritus

Industry Leaders

In addition to Course Leaders, industry experts focusing on data science share their knowledge and experience through periodic guest lectures.

Learning Experience

Benefits to the Learner

Enhance Your Career capital

Enhance Your Career capital

  • Professional acceleration through our enriched leadership toolkit.
  • Learn while you earn.
  • Get noticed. Get ahead.
  • Understand how to manage your career & personal brand.
Enhance Your Social Capital

Enhance Your Social Capital

  • Make new, life-long connections with experienced business people from a wide variety of cultures, industries, and backgrounds.
  • Inclusion in the Emeritus Network
  • Invitation to Emeritus alumni events globally including career panels, CXO speaker series, and industry interactions.
Manage Your Brand Capital

Manage Your Brand Capital

  • A Global Business Education on your resume
  • Top 10 percent of the class achieves the status of Emeritus Scholars determined by the overall diploma GPA
Deepen Your Intellectual Capital

Deepen Your Intellectual Capital

  • World class curriculum and teaching by faculty from Columbia Engineering Executive Education.
  • Peer-to-peer learning through learning circles, classroom discussions, and project reviews.
  • Selective entrance criterion ensures you learn with the best.

CERTIFICATE

Postgraduate Diploma in Applied Data Science (E-Learning) - Certificate Click to view certificate

ADMISSION & FEES

PREREQUISITES

The diploma requires an undergraduate knowledge of statistics, (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation etc.) linear algebra and probability. You would be required to possess a knowledge of programming concepts like variables, loops, functions, OOP etc.

 

Some hands on knowledge with Python Language and Jupyter Notebook IDE will be necessary. All assignments/application projects will be done in Jupyter Notebooks using the Python programming language. Emeritus offers a complimentary Python for Data Analytics certificate course to meet this prerequisite. Participants who successfully complete this certificate course will receive a certificate of completion from Emeritus Institute of Management.

PAYMENT

  • You can pay for the course either with an international debit or credit card, or bank wire transfer. On clicking the apply now button, you will be directed to the application form and the payment page.
  • We provide deferrals and refunds in specific cases. The deferrals and refund policy is available here.
  • You will be provided a course login within 48 hours of making a payment.

APPLICATION PROCESS

Please provide your work experience and your current employer via the application.

 

Application Requirements

  • Minimum three years of professional work experience
  • Employment history (CV/resume)
  • University transcripts
  • All candidates who have received their bachelor’s or other degree or diploma from an education institution where English is NOT the primary language of instruction are required to demonstrate English language proficiency through ANY ONE of the following methods
    – Obtain a TOEFL minimum score of 550 for the paper based test or its equivalent
    – Obtain an IELTS minimum score of 6.0 Obtain a Pearson Versant Test minimum score of 59
    – Obtain a Certificate of Completion for a Certificate course offered by the Emeritus Institute of Management
    – Submit a document which shows that the candidate has, for the last 24 months or more, worked in ANY ONE of these countries: Antigua and Barbuda, Australia, The Bahamas, Barbados, Belize, Canada, Dominica, Grenada, Guyana, India, Ireland, Jamaica, New Zealand, Singapore, South Africa, St Kitts and Nevis, St Lucia, St Vincent and the Grenadines, Trinidad and Tobago, United Kingdom, United States of America
  • A completed Application Form
  • Proof of diploma/degree in any field of study (your highest qualification should be submitted)

DIPLOMA FEE

$3000 Payable in 2 equal installments

NON-REFUNDABLE APPLICATION FEE

$50

NEXT COMMENCEMENT

September 30, 2020

APPLICATION DEADLINE

September 28, 2020

FAQs

For any questions regarding Emeritus, the learning experience, admission & fees, grading & evaluation please visit COMMON FAQs