Become a data-driven leader with a one-of-its-kind certificate program in data science and business analytics. Taught by top academicians & industry experts, this 360 degree program will put you at the forefront of the data science revolution. No prior coding experience required.
This program is offered in partnership with upGrad, a global leader in higher online education with 2 million learners across 100 countries worldwide. With the latest technology, pedagogy, industry partners and world-class faculty, upGrad creates immersive online learning experiences for learners globally.
On successful completion of the Program, you receive a verified certificate from University of Maryland’s Robert H. Smith School of Business.
- Showcase your certificate from a Top 20 U.S. Public university
- This is the 1st certificate in a series of two, to complete the Executive Program in Data Science and Machine Learning.
- Full alumni status from Golden Gate University
- Please select Executive Program in Data Science and Machine Learning from the Data Science & Analytics tab in the Courses dropdown at the top.
STUDY MODEL
Online, weekly live sessions, small group coaching
ACCREDITATION
University of Maryland accredited
DURATION
7 months
ELIGIBILITY
Diploma or Bachelor Degree, some experience of programming languages preferred but not essential, English language proficiency
FEES
USD 3,000
VALUE-ADD
Capstone project in domain of your choice
Data Analysis in Excel
Introduction to DS Landscape
Python Programming Essentials I – Variables, Expressions, and Control Statements
Python Programming Essentials II – Functions and Data Structures
Python Libraries for Data Science – NumPy
Python Libraries for Data Science – Pandas
Python Assignment
Data Analysis using SQL
Practical Data Considerations: Data Cleaning and Preparation
Course Project: Python
Exploratory Data Analysis
Visualization in Python
Visualization using Tableau
Data Storytelling
Visualisation and Storytelling Assignment
Inferential Statistics
Hypothesis Testing
Designing Business Experiments
Course Project: Statistics
Linear Regression in a predictive setting
Introduction to Classification: Logistic Regression
Evaluation methods in Classification Models
Model Selection & Practical Consideration around Modelling + KNN
Decision Tree Models
Introduction to Ensemble Models: Random Forest & Boosting
Assignment
Unsupervised Learning: Clustering
Unsupervised Learning: Association Rules Mining (Market Basket Analysis)
Course Project: Machine Learning
Capstone Project
