Big Data for Business (888102)
About
Course Syllabus
Materials
1. Overview of Big Data for Business
2. Data
3. Big Data & Application in Business
4. Business Problems & Data Science Solutions
5. Basic Tools for Data Mining
6. Data Preparation
7. Data Visualization
8. Statistics and Probability: Probability Foundations
8.1 Statistics and Probability: Applied Statistics
8.2 Statistics and Probability: Solving Business Problems with Descriptive Statistics
9. Introduction to Machine Learning
10. Supervised Learning
10.1 Supervised Learning: Regression
10.2 Supervised Learning: Classification
10.3 Supervised Learning: knn
10.4 Supervised Learning: Evaluation
11. Unsupervised Learning: K-mean
11.1 Unsupervised Learning: Hierarchical Clustering
11.2 Unsupervised Learning: DBSCAN
11.3 Unsupervised Learning: Association Rule
12. Text Mining
12.1 Text Mining: End-to-End NLP Pipeline: From Data to Deployment
Extra: Essential Microsoft Excel Keyboard Shortcuts
Extra: Best Practices for Data Visualization
Labs
Labs: How to Transform Data to Structural Data
Labs: Text Data Cleaning
Labs: If
Labs: Sum
Labs: Filter
Labs: PivotTable
Labs: Data Viz
Labs: Prob and Stat
Labs (Extra): Data Visualization with Jamovi
Pre-test
Data
Big Data
Business Problems and Data Science Solutions
Data Wrangling
Data Visualization
Statistics and Probabilities
Introduction to Machine Learning
Supervised Learning: Regression
Supervised Learning: Logistic Regression
Supervised Learning: Decision Tree
Shares
Statistics and Probability: Applied Statistics
Full Screen