Description
This is a career-oriented analytics foundation programme brought to you by RVIM-SetConnect ACE (Analytics Centre of Excellence). It equips you with the essential skills required to prepare for a career in business and data analytics. It enables you to master core analytics areas of building models to solve different problems, whether in business, technical or research domains. The programme comprehensively covers predictive analytics, machine learning and prescriptive analytics. A broad range of business applications for these modeling techniques will be presented. It is part of the Foundation and Application layers in SetConnect’s fasTrac™ methodology.
Analytics in Practice
Supported by our industry partner
Students will work on real-life analytics projects in companies. This internship will give them an opportunity to apply the technical skills that they have learnt in the
Key Project phases :
- Define and refine Business problem
- Develop solution approach
- Collect, cleanse and mine data
- Create models as part of the solution
- Implement and execute solution
- Present result using the principles of Quantitative Storytelling
Key skills that students will acquire
- Collaborate with Team members
- Learn methods of communication in all interactions within sponsoring organization
- Maximize benefits of mentorship from SetCONNECT faculty.
- Acquire advocacy and communcations skills through executive presentations
Key Benefits
- Understand how to apply technical skills to business problems
- Evaluate themselves in terms of strengths, weaknesses as well as interest areas
- Get practical, hands-on exposure to companies
- Increase employment opportunities by showcasing their abilities
GLOBAL FACULTY PANEL
Dr Ramesh Rajagopalan
Dr Novin Ghaffari
Mr. Gurudutt Shenoy
Dr Bugra Alkan
SUPPORTED BY RVIM COE
Dr. Purrushottam Bung
Dr. Bikramaditya Ghosh
Dr. Santhosh M
Prof. Dileep
Prof. Vandana Gablani
Prof. Nagasubba Reddy
Prof. Shreya Shankar
Who is it for?
This course is relevant for fresh graduates and working professionals seeking to enter the field of Analytics and Data Science. No prior work experience is required.
What will participants be able to achieve at the end of this programme?
At the end of the programme, you will be able to:
-
Translate business requirements to problems that can be solved computationally
-
Move from descriptive to predictive modelling (reactive to proactive)
-
Develop a data-driven approach from hindsight to insight and thus focus on “What will happen” rather than “What happened”
-
Write scripts using syntax and conventions established in industry best practices ; develop a fully functional programme using industry-relevant Python
-
Prepare for additional courses to build your career in Analytics and Data Science
-
Understand career roadmaps in Analytics and explain analytics proposals to senior management
At the end of the programme, you will be able to:
-
Prepare for potential roles as a business manager, business analyst, business intelligence analyst, data analyst, data technician or operations analyst
-
Learn directly from global faculty who have rich experience and subject matter expertise
-
Become proficient with in-demand skills and open-source technologies
-
Formulate and solve business problems with statistical analysis
-
Leverage our industry partner network and hear directly from top companies
-
Flexible, self-paced learning to suit your individual needs
-
Experiential learning through extensive hands-on projects
Duration
-
Duration: 35 weeks, 180 hours
-
Combination of both synchronous and asynchronous learning sessions
-
Extensive lab and hands-on practical sessions at RVIM labs
-
On an average, the student should plan to spend between 10-12 hours per week
Schedule
-
Last date for application – August 30, 2021
-
Programme commencement – Sept 1, 2021
-
Programme completion – March 31, 2022
Business Statistics
- Distributions
- Probability
- Statistical Research
- Hypothesis Testing
- Regression
- Analysis of Variance
- Chi Square
- Transformations
Python for Data Analytics
- Basic Data Types
- Data Structures
- Control Structures
- Error Handling
- Numerical Python
- Scientific Python
- Python Data Analysis Libraries
- Applications for Data Analytics
Introduction to Predictive Modeling
- Applications of Predictive Analytics
- Unsupervised Learning
- Cluster Analysis for Supervised learning
- Classification
- Logistics Regression
- Unsupervised learning using kNN
- Value realization through Analytics
Data Visualization
- Data Analytics Process and Data Cleaning
- Visualizing Descriptive Statistics through Tableau
- Visual analytics using Tableau
- Dashboard designing and Stories
- R Studio Visual Analytics
- R Studio Maps
- R Studio Regression
Machine Learning
- Machine learning fundamentals
- Non-Linear Regression
- Supervised ML
- Decision Trees
- K-Nearest Neighbor
- Unsupervised ML
- Deep Learning
Prescriptive Modeling
- Linear Optimization Models
- Linear Programming
- Sensitivity Analysis
- Transportation and Assignment Models
- Network Models
- Programme Details
-
Who is it for?
This course is relevant for fresh graduates and working professionals seeking to enter the field of Analytics and Data Science. No prior work experience is required.
What will participants be able to achieve at the end of this programme?
At the end of the programme, you will be able to:
-
Translate business requirements to problems that can be solved computationally
-
Move from descriptive to predictive modelling (reactive to proactive)
-
Develop a data-driven approach from hindsight to insight and thus focus on “What will happen” rather than “What happened”
-
Write scripts using syntax and conventions established in industry best practices ; develop a fully functional programme using industry-relevant Python
-
Prepare for additional courses to build your career in Analytics and Data Science
-
Understand career roadmaps in Analytics and explain analytics proposals to senior management
At the end of the programme, you will be able to:
-
Prepare for potential roles as a business manager, business analyst, business intelligence analyst, data analyst, data technician or operations analyst
-
Learn directly from global faculty who have rich experience and subject matter expertise
-
Become proficient with in-demand skills and open-source technologies
-
Formulate and solve business problems with statistical analysis
-
Leverage our industry partner network and hear directly from top companies
-
Flexible, self-paced learning to suit your individual needs
-
Experiential learning through extensive hands-on projects
-
- Schedule
-
Duration
-
Duration: 35 weeks, 180 hours
-
Combination of both synchronous and asynchronous learning sessions
-
Extensive lab and hands-on practical sessions at RVIM labs
-
On an average, the student should plan to spend between 10-12 hours per week
Schedule
-
Last date for application – August 30, 2021
-
Programme commencement – Sept 1, 2021
-
Programme completion – March 31, 2022
-
- Topics
-
Business Statistics
- Distributions
- Probability
- Statistical Research
- Hypothesis Testing
- Regression
- Analysis of Variance
- Chi Square
- Transformations
Python for Data Analytics
- Basic Data Types
- Data Structures
- Control Structures
- Error Handling
- Numerical Python
- Scientific Python
- Python Data Analysis Libraries
- Applications for Data Analytics
Introduction to Predictive Modeling
- Applications of Predictive Analytics
- Unsupervised Learning
- Cluster Analysis for Supervised learning
- Classification
- Logistics Regression
- Unsupervised learning using kNN
- Value realization through Analytics
Data Visualization
- Data Analytics Process and Data Cleaning
- Visualizing Descriptive Statistics through Tableau
- Visual analytics using Tableau
- Dashboard designing and Stories
- R Studio Visual Analytics
- R Studio Maps
- R Studio Regression
Machine Learning
- Machine learning fundamentals
- Non-Linear Regression
- Supervised ML
- Decision Trees
- K-Nearest Neighbor
- Unsupervised ML
- Deep Learning
Prescriptive Modeling
- Linear Optimization Models
- Linear Programming
- Sensitivity Analysis
- Transportation and Assignment Models
- Network Models
