Master Python Data Analysis and Modelling Essentials

A Real-World Project using Jupyter notebook, Numpy, SciPy, Pandas, Matplotlib, Statmodels, Scikit-learn, and many more

4.60 (12 reviews)
Udemy
platform
English
language
Data Science
category
instructor
79
students
7.5 hours
content
Apr 2022
last update
$49.99
regular price

What you will learn

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

Description

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. After this course, you will own the skills to:

(1) to explore data using Python Pandas library

(2) to rename the data column using different methods

(3) to detect the missing values and outliers in dataset through different methods

(4) to use different methods to fill in the missings and treat the outliers

(5) to make correlation analysis and select the features based on the analysis

(6) to encode the categorical variables with different methods

(7) to split dataset for model training and testing

(8) to normalize data with scaling methods

(9) to develop classic statistical regression models and machine learning regression models

(10) to fit the model, improve the model, evaluate the model and visualize the modelling results, and many more

Content

Introduction

Introduction to Course Contents
Introduction to Data Analysis and Modelling

Setting up Python Environment

Installing Anaconda Python
Required Python Packages
Installing Required Packages
Creating and Accessing Working Directory

Data Exploration

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

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

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

Machine Learning Modelling Process
Model Trainning
Model Evaluation
Model Improvement
Model Result Visualization

Screenshots

Master Python Data Analysis and Modelling Essentials - Screenshot_01Master Python Data Analysis and Modelling Essentials - Screenshot_02Master Python Data Analysis and Modelling Essentials - Screenshot_03Master Python Data Analysis and Modelling Essentials - Screenshot_04

Reviews

Feiya
January 27, 2022
This is an excellent lecture about the detailed process of data analysis and modelling step by step. I am particpating Professor Wei's Jupyter Notebook course. They have similar teaching stype, offering so many detailed methods, and teaching things step by step using concrete and easy understanding examples. Excellent, I like it very much.

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4392322
udemy ID
11/11/2021
course created date
2/1/2022
course indexed date
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