Course Content
Introduction
You will understand the contents of the course and be clear about the main process of data analysis and modelling.
0/4
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.
0/4
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.
0/9
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.
0/8
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.
0/7
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.
0/5
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
    About Lesson

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

    Previous
    Next
    0% Complete
    Scroll to Top