Course11010451345 lectures
Advanced R Programming for Data Analytics in Business
Prof. Abhinava Tripathi · IIT Kanpur
About
Over the next few decades, Data Science (DS), Machine-Learning (ML), and AI (Artificial Intelligence) will play a crucial role in several aspects of business decision-making and management information systems. Leaders in organizations need to capitalize on data analytics to gain a competitive advantage in the modern business landscape. The application of cutting-edge data analytics techniques implemented with R programming (and RStudio, a powerful IDE(Integrated Development Environment) will prepare the learners for business analytics workflow and make them job ready for mid-to-senior managerial positions in various business and industry settings. In this course, you will use advanced data analytics tools to explore, clean, wrangle, visualize, and process business data to generate useful insights and make inferences from raw and unstructured data. The course will also introduce the learners to use cases from business, finance, and management areas and problem sets that require advanced techniques for processing the data and communicating the results, and providing managerial implications. This course has been carefully designed to cater to not only business, finance, and management professionals but also those from other industries and academics that significantly rely on data-driven decision-making. The operating environment for all types of organizations (engineering and management) has become extremely dynamic and data-driven and continues to evolve at an extremely fast pace, with technological innovations at the heart of this change. Against this backdrop, DS, ML, and AI are providing new opportunities for all market participants, i.e., business leaders, policymakers, regulators, and governments. The objective of this course is to help the learners understand and apply these modern DS, ML, and AI techniques in the business, finance, and management industry. This includes solving real-life business, finance, and management problems to improve organizational…
Lectures
Week 1
- Lesson 1: Fundamentals of R
- Lesson 2: Data cleaning
- Lesson 3: Data Visualization- Part 1
- Lesson 4: Data Visualization- Part 2
Week 2
- Lesson 5: Introduction to Probability Theory
- Lesson 6: Conditional Probabilities and Bayes Theorem
- Lesson 7: Random Variables and Probability Distributions
- Lesson 8: Binomial Distribution
- Lesson 9: Continuous Random Variables and Normal Distribution
Week 3
- Lesson 10: Descriptive Analytics: Measures of Central Tendency
- Lesson 11: Descriptive Analytics: Measures of Variability and Shape
- Lesson 12: Statistical Inference: Sampling
- Lesson 13: Statistical Inference: Central Limit Theorem and Confidence Intervals
- Lesson 14: Statistical Inference: Hypothesis testing
Week 4
- Lesson 15: Introduction to R
- Lesson 16: Inferential statistics: R Implementation
- Lesson 17: Statistical Inference: Sampling
- Lesson 18: Hypothesis Testing: R Implementation
Week 5
- Lesson 19: Regression Modelling-Part 1
- Lesson 20: Regression Modelling-Part 2
- Lesson 21: Regression Algorithm: Application (Part 1)
- Lesson 22: Regression Algorithm: Application (Part 2)
Week 6
- Lesson 23: Classification Algorithms: Logit/Probit Regression (Part 1)
- Lesson 24: Classification Algorithms: Logit/Probit Regression (Part 2)
- Lesson 25: Classification Algorithms: Application (Part 1)
- Lesson 26: Classification Algorithms: Application (Part 2)
Week 7
Week 8
- Lesson 29: Introduction to Panel Data Modelling
- Lesson 30: Panel data application and implementation with R
Week 9
- Lesson 31: Advanced Time-Series Models
- Lesson 32: Introduction to ARMA process
- Lesson 33: Forecasting with ARMA models
- Lesson 34: Non-stationarity, Cointegration, and Error correction Models
- Lesson 35: Return and Volatility Modelling and Forecasting
Week 10
- Lesson 36: Maximum Likelihood Estimation (MLE)
- Lesson 37: Quantile Regression - Part A
- Lesson 38: Quantile Regression - Part B
- Lesson 39: Solar Panel Marketing Case Study
Week 11
- Lesson 40: PCA and Clustering- Part 1
- Lesson 41: PCA and clustering- Part 2
- Lesson 42: Tutorial- HR Analytics Part 1
- Lesson 43: Tutorial - HR Analytics- Part 2
Week 12
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Kanpur
- Department
- Management
- Teacher
- Prof. Abhinava Tripathi
- Level
- Undergraduate
- Language
- English
- Subjects
- Business and management
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed