Data Mining¶
This course covers the concepts regarding Data Analysis.
Why Data Mining¶
- Scalability
- Handling high-dimensional data
- Complex & Heterogeneous Data
- Handling Poor Quality Data
- Data ownership and Distribution
Applications of this course¶
To fill in areas where traditional data analysis methods cannot be applied
- Optimize business operations
- Understand customers
- Computer-Aided Diagnosis
- Image
- Segmentation
- Captioning
- Object Detection
Sources¶
Kaggle, UCI ML repository
References¶
- Data Mining | Dr. Angel Arul Jothi
- From Data to Decisions: Measurement, Uncertainty, Analysis and Modeling | Chris Mack | University of Texas
- How to write a good scientific paper | Chris Mack | University of Texas
- Statistics literacy for non-statisticians | Mike x Cohen
- Big Data Analytics | Caltech
- MIT 14.310x Data Analysis for Social Scientists, Spring 2023
- IIT Roorkee July 2018 | Data Analytics with Python
- ORIE 5355 -- People, Data, Systems -- Fall 2021 -- Cornell Tech
- Mining Massive Datasets | Stanford University
- Quantitative Social Science Methods | Harvard
- Data Mining | University of Utah
- Visualization for Data Science | University of Utah
- Winter 2017, STAT 442 / 842 Data Visualization
- Data Visualization | IIT Madras
- Introduction to Bigdata | IIT Madras
- Empirical Methods CMU
- Fall 2022
- Spring 2021
- Introduction to Data Analysis, Design of Experiment, and Machine Learning | Ashraf Alam | Purdue University
- Data Analytics | Gary Holness | Clark University
- Intro to Data Science | Gary Holness | Clark University
- Statistical Research Methods | Mikko Rönkkö