Course Content
Introduction
You will understand the contents of the course and be clear about the main process of data analysis and modelling.
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Course Introduction
04:22
Introduction to Course Contents
08:56
Download Courses Notes
04:56
Introduction to Data Analysis and Modelling
05:41
Setting up Python Environment
You will be able to setup Python environment to starting data anlysis and modelling.
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Installing Anaconda Python
24:28
Required Python Packages
03:34
Installing Required Packages
10:45
Creating and Accessing Working Directory
08:27
Data Exploration
You will be able to read local and online datasets into Pandas DataFrame, and make basic data exploration.
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Download the Data
02:37
Reading and Writing Data
14:28
Accessing Basic Information of DataFrame
10:15
Renaming Columns of DataFrame
13:30
Slicing DataFrame
19:39
Sorting DataFrame
07:45
Filtering DataFrame
17:02
Grouping DataFrame
08:21
Calculating Summary Statistics of DataFrame
15:21
Data Preparation
You will be able to clean the data and preprocess the dara for further analysis and creating models.
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Detecting Missing Values
15:50
Imputing Missing Values
20:12
Detecting Outliers
24:28
Treating Outliers
17:23
Correlation Analysis and Feature Selection
30:04
Encoding Categorical Values
26:46
Data Splitting
10:08
Data Normalization
29:51
Classic Statistical Linear Regression Models
You will be able to develop a classic Statistical Linear Regression Models, interprete the results, improve it, evaluate it and visualizae the results.
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Statistical Modelling Process
04:20
Data Normalization in Classic Statistical Regression
05:33
Model Estimation and Result Interpretation
11:21
Multicollinearity
09:36
Model Improvement
12:12
Model Evaluation
08:00
Model Result Visualization
07:18
Machine Learning Linear Regression Models
Your will be able to develop Machine Learning Linear Regression Models, and you can train a model, evaluate it, improve it and visualize the results.
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Machine Learning Modelling Process
01:47
Model Trainning
05:08
Model Evaluation
03:15
Model Improvement
12:11
Model Result Visualization
13:44
Master Python Data Analysis and Modelling Essentials

    This lecture is an introduction to the course contents. After this lecture, you will be clear course structure and what the course covers.

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