Professional Certificate in Data Science and Business Analytics
Course Information
No schedule.
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Overview
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Learning Outcomes
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Who Should Attend
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Testimonials
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Trainer's Profile
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Course Outline
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.
You will learn:
- Develop your knowledge of the most popular analytics tools and technologies such as Tableau, Python, MySql and Excel
- Develop the capacity to use analytics and data science to solve business problemson your own with 200+ hours of hands-on Learning and 50+ hour of live sessions
- Learn about Data Science uses and ramifications in various Industries like BFSI,
- Retail, E-commerce and Healthcare.
- Learn how to extract key business insights from data and convey them to stakeholders in a clear and concise manner.
- Create models that forecast future trends and utilise them to guide business decisions.
- Apply the cutting edge ML algorithms to develop solutions for real-life business problems.
- Design the AI strategy for your vertical and evaluate the various factors involved in its implementation.
Who is this program for?:
- Software Engineers who wish to enter DS
- Mid Level IT professionals
- Non Technical Business Analysts
- Marketing/ Healthcare/ Domain based professionals who wish to apply analytics
- Early career technologists with understanding of math and basic logic for programming
- The program design and curriculum assumes an undergraduate education has been earned by the learner.
- High school graduates/ Associate degree holders can pursue this program.
PK KannanDean's Chair in Marketing SciencePhD in Management, Purdue University; Marketing
Lauren RhueAssistant Professor;Decision, Operations and Information Technologies;Ph.D in Information Systems, New York Univerisity
Preparatory content (0 Weeks)Data Science Toolkit (10 Weeks)
- Introduction to the program
- 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 (2 weeks)
- 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
- Introduction to Linear Regression in Explanatory & Inferencing Setting
- 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
- Assignment
- Unsupervised Learning: Clustering