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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- 3 stars: 0 votes
- 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!
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