Master Python Data Analysis and Modelling Essentials

Master Python Data Analysis and Modelling Essentials, available at $49.99, has an average rating of 4.6, with 37 lectures, based on 12 reviews, and has 87 subscribers.

You will learn about 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 This course is ideal for individuals who are Business analysts or Data analytics professionals or Statisticians or Engineers and scientists for data analysis, modelling and machine learning or Anyone who wants to learn data analysis and modelling with Python for his/her projects It is particularly useful for Business analysts or Data analytics professionals or Statisticians or Engineers and scientists for data analysis, modelling and machine learning or Anyone who wants to learn data analysis and modelling with Python for his/her projects.

Enroll now: Master Python Data Analysis and Modelling Essentials

Summary

Title: Master Python Data Analysis and Modelling Essentials

Price: $49.99

Average Rating: 4.6

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $94.99

Quality Status: approved

Status: Live

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

Who Should Attend

Business analysts

Data analytics professionals

Statisticians

Engineers and scientists for data analysis, modelling and machine learning

Anyone who wants to learn data analysis and modelling with Python for his/her projects

Target Audiences

Business analysts

Data analytics professionals

Statisticians

Engineers and scientists for data analysis, modelling and machine learning

Anyone who wants to learn data analysis and modelling with Python for his/her projects

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Course Contents

Lecture 2: Introduction to Data Analysis and Modelling

Lecture 3: How to Use and Download the Source Notebook of the Course

Lecture 4: How to Receive Instructor Announcements on Time

Chapter 2: Setting up Python Environment

Lecture 1: Installing Anaconda Python

Lecture 2: Required Python Packages

Lecture 3: Installing Required Packages

Lecture 4: Creating and Accessing Working Directory

Chapter 3: Data Exploration

Lecture 1: An Explaination How to Dowload the Data for the Next Lecture

Lecture 2: Reading and Writing Data

Lecture 3: Accessing Basic Information of DataFrame

Lecture 4: Renaming Columns of DataFrame

Lecture 5: Slicing DataFrame

Lecture 6: Sorting DataFrame

Lecture 7: Filtering DataFrame

Lecture 8: Grouping DataFrame

Lecture 9: Calculating Summary Statistics of DataFrame

Chapter 4: Data Preparation

Lecture 1: Detecting Missing Values

Lecture 2: Imputing Missing Values

Lecture 3: Detecting Outliers

Lecture 4: Treating Outliers

Lecture 5: Correlation Analysis and Feature Selection

Lecture 6: Encoding Categorical Values

Lecture 7: Data Splitting

Lecture 8: Data Normalization

Chapter 5: Classic Statistical Linear Regression Models

Lecture 1: Statistical Modelling Process

Lecture 2: Data Normalization in Classic Statistical Regression

Lecture 3: Model Estimation and Result Interpretation

Lecture 4: Multicollinearity

Lecture 5: Model Improvement

Lecture 6: Model Evaluation

Lecture 7: Model Result Visualization

Chapter 6: Machine Learning Linear Regression Models

Lecture 1: Machine Learning Modelling Process

Lecture 2: Model Trainning

Lecture 3: Model Evaluation

Lecture 4: Model Improvement

Lecture 5: Model Result Visualization

Instructors

Dr. Shouke Wei
Professor

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2 stars: 0 votes

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4 stars: 4 votes

5 stars: 8 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!