Python + ChatGPT 3.5 for A-Z Statistical Data Analysis

Python + ChatGPT 3.5 for A-Z Statistical Data Analysis, available at $24.99, has an average rating of 5, with 35 lectures, 25 quizzes, based on 21 reviews, and has 1055 subscribers.

You will learn about Learn how to understand data and hone your skills in inferential, descriptive, and hypothesis testing statistics. Discover how to use descriptive statistical measures, such as mean, median, variance, and standard deviation, to summarize and understand data. Python tools for cleaning, modifying, and analyzing real-world data include pandas, numpy, seaborn, matplotlib, scipy, and scikit-learn. Establish a methodical procedure for data analysis that includes conversion, cleaning, and the use of statistical techniques to guarantee quality and accuracy. Learn how to set up, run, and comprehend one-sample, independent sample, crosstabulation, association tests, and one-way ANOVA for hypothesis testing. Gaining a rudimentary understanding of regression analysis will enable you to foresee and model variable relationships—a critical skill for making informed deci Use python to show complex, interactive statistical visualizations including box plots, KDE plots, clustered bar charts, histograms, heatmaps, and bar plots. Full?explanation on?each Python code that is used to solve statistical challenges. This will make the use of statistical analysis more clear. This course is ideal for individuals who are People who want to work in data analysis and want an easy-to-understand introduction to the world of numbers or People who work in business intelligence and want to make decisions based on data can or People who use data on the job to make assumptions, estimates, or guesses using statistics or Students who want to learn strong, useful skills through unique, hands-on projects and demos It is particularly useful for People who want to work in data analysis and want an easy-to-understand introduction to the world of numbers or People who work in business intelligence and want to make decisions based on data can or People who use data on the job to make assumptions, estimates, or guesses using statistics or Students who want to learn strong, useful skills through unique, hands-on projects and demos.

Enroll now: Python + ChatGPT 3.5 for A-Z Statistical Data Analysis

Summary

Title: Python + ChatGPT 3.5 for A-Z Statistical Data Analysis

Price: $24.99

Average Rating: 5

Number of Lectures: 35

Number of Quizzes: 25

Number of Published Lectures: 35

Number of Published Quizzes: 25

Number of Curriculum Items: 60

Number of Published Curriculum Objects: 60

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn how to understand data and hone your skills in inferential, descriptive, and hypothesis testing statistics.

Discover how to use descriptive statistical measures, such as mean, median, variance, and standard deviation, to summarize and understand data.

Python tools for cleaning, modifying, and analyzing real-world data include pandas, numpy, seaborn, matplotlib, scipy, and scikit-learn.

Establish a methodical procedure for data analysis that includes conversion, cleaning, and the use of statistical techniques to guarantee quality and accuracy.

Learn how to set up, run, and comprehend one-sample, independent sample, crosstabulation, association tests, and one-way ANOVA for hypothesis testing.

Gaining a rudimentary understanding of regression analysis will enable you to foresee and model variable relationships—a critical skill for making informed deci

Use python to show complex, interactive statistical visualizations including box plots, KDE plots, clustered bar charts, histograms, heatmaps, and bar plots.

Full?explanation on?each Python code that is used to solve statistical challenges. This will make the use of statistical analysis more clear.

Who Should Attend

People who want to work in data analysis and want an easy-to-understand introduction to the world of numbers

People who work in business intelligence and want to make decisions based on data can

People who use data on the job to make assumptions, estimates, or guesses using statistics

Students who want to learn strong, useful skills through unique, hands-on projects and demos

Target Audiences

People who want to work in data analysis and want an easy-to-understand introduction to the world of numbers

People who work in business intelligence and want to make decisions based on data can

People who use data on the job to make assumptions, estimates, or guesses using statistics

Students who want to learn strong, useful skills through unique, hands-on projects and demos

Unlock the power of data through the Applied Statistics and Analytics course, where you will embark on a comprehensive journey of statistical analysis and data interpretation using Python and ChatGPT. This course is designed to equip you with essential skills in hypothesis testing, descriptive statistics, inferential statistics, and regression analysis, empowering you to transform raw data into strategic insights.

Key Learning Objectives:

1. Foundational Statistical Concepts:

2. Develop a solid understanding of hypothesis testing, descriptive statistics, and inferential statistics.

3. Learn to interpret data by applying statistical metrics such as mean, median, variance, and standard deviation.

4. Python Tools for Data Analysis:

5. Acquire proficiency in utilizing Python tools like pandas, numpy, seaborn, matplotlib, scipy, and scikit-learn for cleaning, altering, and analyzing real-world data.

6. Establish a systematic data analysis process encompassing data cleaning, transformation, and the application of statistical approaches to ensure accuracy and quality.

7. Hypothesis Testing Mastery:

8. Gain hands-on experience in organizing, conducting, and understanding various hypothesis tests, including one-sample, independent sample, crosstabulation, association tests, and one-way ANOVA.

9. Regression Analysis Essentials:

10. Learn the fundamentals of regression analysis to model and forecast variable relationships, enabling you to make informed and strategic decisions based on data insights.

11. Python for Statistical Visualization:

12. Harness the power of Python for creating complex and interactive statistical visualizations. Explore visualization techniques such as clustered bar charts, histograms, box plots, KDE plots, heatmaps, and bar plots to present data clearly and persuasively.

By the end of this course, you will not only be proficient in statistical analysis using Python but also capable of transforming data into actionable insights, making you an invaluable asset in the data-driven decision-making landscape. Join us on this transformative journey into the world of Applied Statistics and Analytics, where data speaks, and you have the skills to listen.

Course Curriculum

Chapter 1: Setting up Python, Jupyter Notebook and ChatGPT

Lecture 1: Install Python and Jupyter Notebook

Lecture 2: Setting Up ChatGPT for SMART Analysis

Lecture 3: Download dataset for practice quizzes

Lecture 4: Instructions for Quizzes: IMPORTANT

Lecture 5: Connect with my youtube channel

Lecture 6: Get special handbooks

Chapter 2: What is Statistical Data Analysis?

Lecture 1: Understanding the concept of statistical data analysis

Lecture 2: Confidence level, Significance level and P-value

Lecture 3: Understanding complete workflow in statistical analysis

Chapter 3: Cleaning Data for Statistical Data Analysis

Lecture 1: Importing data file into Jupyter Notebook

Lecture 2: Dealing with missing or nan values

Lecture 3: Dealing with inconsistent or mistaken data

Lecture 4: Managing and assigning correct data types

Lecture 5: Identifying and removing duplicate values

Chapter 4: Manipulating Data for Statistical Data Analysis

Lecture 1: Arranging and sorting dataset by variables

Lecture 2: Conditional filtering (e.g., and, or, not etc.)

Lecture 3: Merging datasets and adding new variables

Lecture 4: Concatenating datasets and adding extra data

Chapter 5: Transforming Data into Normal Distribution

Lecture 1: Test the normal distribution for numeric data

Lecture 2: Square root transformation for normality

Lecture 3: Logarithmic transformation for normality

Lecture 4: Box-cox transformation for normality

Lecture 5: Yeo-jhonson transformation for normality

Chapter 6: Statistical Analysis and Hypothesis Testing

Lecture 1: Frequency and Percentage analysis

Lecture 2: Descriptive analysis (Mean, deviation, median, etc.)

Lecture 3: One Sample T-Test: Measure difference as a whole

Lecture 4: Independent Sample T-Test: Measure difference in two groups

Lecture 5: Oneway ANOVA: Measure difference in two or more groups

Lecture 6: Chi-square Test for Independence: Association between nominal data

Lecture 7: Pearson Correlation: Relationship between numeric data

Lecture 8: Regression Analysis: Measure the influence

Lecture 9: Utilize Python in real-world data analysis application

Chapter 7: Your Next Journey of Learning

Lecture 1: Resources for enhancing data analytics skill

Chapter 8: Tips, Tricks and Resources

Lecture 1: ChatGPT for Fastest Python Programming and Debugging

Lecture 2: Other Resources

Instructors

Analytix AI
Unleashing the Power of Data with AI for Informed Insights.

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5 stars: 21 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!