Master Python Data Analysis and Modelling Essentials

About Course

We are living in a data explosive world where data is ubiquitous, and thus it is essential to build data analysis and modelling skills. Based on TIOBE Index, Python has overpassed Java and C and become the most popular programming language of today since October 2021. Python leads the top Data Science and Machine Learning platforms based on KDnuggets poll.

This course uses a real world project and dataset and well known Python libraries to show you how to explore data, find the problems and fix them, and how to develop classic statistical regression models and machine learning regression step by step in an easily understand way. This course is especially suitable for beginner and intermediate levels, but many of the methods are also very helpful for the advanced learners.

Data analysis and modelling process
Setting up Python data analysis and modelling environment
Data exploration
Rename the data columns
Data slicing, sorting, filtering, and grouping data
Missing value detection and imputation
Outlier detection and treatment
Correlation Analysis and feature selection
Splitting data set for model fitting and testing
Data normalization with different methods
Developing a classic statistical linear regression model
Developing a machine linear regression model
interpreting the model results
Improving the models
Evaluating the models
Visualizing the model results

Course Content

Introduction
You will understand the contents of the course and be clear about the main process of data analysis and modelling.

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.

Data Exploration
You will be able to read local and online datasets into Pandas DataFrame, and make basic data exploration.

Data Preparation
You will be able to clean the data and preprocess the dara for further analysis and creating models.

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.

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.

Student Ratings & Reviews

5.0

Total 1 Rating

1 Rating

0 Rating

Sigmund Wei

2 years ago

It is very helpful beginner course for data analysis and modelling.

About Course

What Will You Learn?

Course Content

Introduction You will understand the contents of the course and be clear about the main process of data analysis and modelling.

Course Introduction

Introduction to Course Contents

Download Courses Notes

Introduction to Data Analysis and Modelling

Setting up Python Environment You will be able to setup Python environment to starting data anlysis and modelling.

Installing Anaconda Python

Required Python Packages

Installing Required Packages

Creating and Accessing Working Directory

Data Exploration You will be able to read local and online datasets into Pandas DataFrame, and make basic data exploration.

Download the Data

Reading and Writing Data

Accessing Basic Information of DataFrame

Renaming Columns of DataFrame

Slicing DataFrame

Sorting DataFrame

Filtering DataFrame

Grouping DataFrame

Calculating Summary Statistics of DataFrame

Data Preparation You will be able to clean the data and preprocess the dara for further analysis and creating models.

Detecting Missing Values

Imputing Missing Values

Detecting Outliers

Treating Outliers

Correlation Analysis and Feature Selection

Encoding Categorical Values

Data Splitting

Data Normalization

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.

Statistical Modelling Process

Data Normalization in Classic Statistical Regression

Model Estimation and Result Interpretation

Multicollinearity

Model Improvement

Model Evaluation

Model Result Visualization

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.

Machine Learning Modelling Process

Model Trainning