Udemy

Python + ChatGPT 3.5 for A-Z Statistical Data Analysis

Enroll Now
  • 1,081 Students
  • Updated 8/2024
4.6
(25 Ratings)
CTgoodjobs selects quality courses to enhance professionals' competitiveness. By purchasing courses through links on our site, we may receive an affiliate commission.

Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
3 Hour(s) 40 Minute(s)
Language
English
Taught by
Analytix AI
Rating
4.6
(25 Ratings)
1 views

Course Overview

Python + ChatGPT 3.5 for A-Z Statistical Data Analysis

Learn to Use Python with ChatGPT 3.5 to Perform Statistical Tests and Data Analysis from Cleaning to Insight Generation.

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:

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

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

  2. Python Tools for Data Analysis:

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

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

  3. Hypothesis Testing Mastery:

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

  4. Regression Analysis Essentials:

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

  5. Python for Statistical Visualization:

    • 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 Content

  • 8 section(s)
  • 35 lecture(s)
  • Section 1 Setting up Python, Jupyter Notebook and ChatGPT
  • Section 2 What is Statistical Data Analysis?
  • Section 3 Cleaning Data for Statistical Data Analysis
  • Section 4 Manipulating Data for Statistical Data Analysis
  • Section 5 Transforming Data into Normal Distribution
  • Section 6 Statistical Analysis and Hypothesis Testing
  • Section 7 Your Next Journey of Learning
  • Section 8 Tips, Tricks and Resources

What You’ll 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.

Reviews

  • W
    William Miller
    5.0

    Applied Statistics in Python course helped me improve my data analysis skills significantly.

  • M
    Melissa White
    5.0

    This course on Python statistics is a must-try for anyone interested in statistical analysis.

  • S
    Stephanie Scott
    5.0

    Applied Statistics in Python course helped me understand real-life applications better.

  • B
    Brian King
    5.0

    ChatGPT was a helpful companion throughout the course, making learning enjoyable.

Start FollowingSee all

We use cookies to enhance your experience on our website. Please read and confirm your agreement to our Privacy Policy and Terms and Conditions before continue to browse our website.

Read and Agreed