This course was created with the
course builder. Create your online course today.
Start now
Create your course
with
Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Data Science and Business Analytics with Python
Course Lectures
1. Introduction (3:23)
2. Class Project (1:35)
3. What is Data Science (4:44)
4. Tool Overview (4:15)
5. How To Find Help (14:17)
6. Data Loading (0:21)
7. Loading Excel and CSV files (6:20)
8. Loading Data from SQL (5:11)
9. Loading Any Data File (5:59)
10. Dealing with Huge Data (10:15)
11. Combining Multiple Data Sources (4:15)
12. Data Cleaning (0:54)
13. Dealing with Missing Data (8:06)
14. Scaling and Binning Numerical Data (12:27)
15. Validating Data with Schemas (10:10)
16. Encoding Categorical Data (6:39)
17. Exploratory Data Analysis (6:39)
18. Visual Data Exploration (10:20)
19. Descriptive Statistics (10:20)
20. Dividing Data into Subsets (12:31)
21. Finding and Understanding Relations in the Data (5:51)
22. Machine Learning (1:08)
23. Linear Regression for Price Prediction (14:30)
24. Decision Trees and Random Forests (6:59)
25. Machine Learning Classification (9:45)
26. Data Clustering for Deeper Insights (8:16)
27. Validation of Machine Learning Models (10:02)
28. ML Interpretability (16:23)
29. Intro to Machine Learning Fairness (7:47)
30. Visuals & Reports (16:23)
31. Visualization Basics (16:23)
32. Visualizing Geospatial Information (5:30)
33. Exporting Data and Visualizations (6:42)
34. Creating Presentations directly in Jupyter (2:38)
35. Generating PDF Reports from Jupyter (3:48)
36. Conclusion and Congratulations! (2:04)
Exercises
Notebooks
27. Validation of Machine Learning Models
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock