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Course overview
What will i learn?
- Introduction to Python for Data Science
- Pandas, Seaborn and Matplotlibs
- Data Cleaning Preprocessing
- EDA Case Study
- Linear Regression, Logistic Regression, Decision Tress, Ensemble
- Kmeans & Hierarchical
Requirements
- Some programming experience
- Admin permissions to download files
Curriculum for this course
32 Lessons
23:59:54 Hours
Overview
1 Lessons
00:04:25 Hours
-
Course Overview
00:04:25
Introduction to Python for Data Science & Numpy
3 Lessons
01:41:59 Hours
-
Introduction to Python for Data Science
00:47:24
-
Numpy Part1
00:31:05
-
Numpy Part2
00:23:30
Pandas
3 Lessons
02:00:57 Hours
-
Pandas Part 1
00:37:17
-
Pandas Part 2
00:44:05
-
Pandas Part 3
00:39:35
Seaborn and Matplotlib
4 Lessons
02:04:08 Hours
-
Seaborn
00:38:25
-
Matplotlib Part1
00:41:13
-
Matplotlib Part2
00:31:49
-
Matplotlib Part3
00:12:41
Business Statistics
3 Lessons
01:19:37 Hours
-
Business Statistics Part 1
00:28:21
-
Business Statistics Part 2
00:32:29
-
Business Statistics Part 3
00:18:47
Data Cleaning Preprocessing
3 Lessons
01:38:57 Hours
-
Data Cleaning Preprocessing - Part1
00:32:01
-
Data Cleaning Preprocessing - Part 2
00:32:28
-
Data Cleaning Preprocessing - Part 3
00:34:28
EDA Case Study
5 Lessons
03:07:00 Hours
-
Part1
00:29:02
-
Part2
00:33:11
-
Part3
00:43:41
-
Part 4
00:38:08
-
Part 5
00:42:58
Linear Regression, Logistic Regression, Decision Tress, Ensemble
6 Lessons
06:26:40 Hours
-
Linear Regression
00:40:58
-
Logistic Regression
01:59:37
-
Decision Trees Part 1
00:24:34
-
Decision Trees Part 2
01:31:53
-
Ensemble Part1
00:40:11
-
Ensemble Part2
01:09:27
Kmeans & Hierarchical
2 Lessons
03:00:16 Hours
-
Kmeans
01:47:12
-
Hierarchical
01:13:04
Case Study
2 Lessons
02:35:55 Hours
-
Part1
01:24:17
-
Part 2
01:11:38
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