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Applied Statistics And Analytics In Python And Chatgpt

Applied Statistics And Analytics In Python And Chatgpt

Applied Statistics And Analytics In Python And Chatgpt

Published 1/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.49 GB | Duration: 3h 33m



Use Statistics and Hypothesis Testing to Find Insights. Develop Regression Models and Turn Data into Strategic Actions.

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.

Requirements
No prior experience is required.
Beginners are most welcome.
Basic computer literacy.
Interest in data analysis and statistics.

Description
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: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.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.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.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.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.

Overview
Section 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

Section 2: What is Statistical Data Analysis?

Lecture 5 Understanding the concept of statistical data analysis

Lecture 6 Confidence level, Significance level and P-value

Lecture 7 Understanding complete workflow in statistical analysis

Section 3: Cleaning Data for Statistical Data Analysis

Lecture 8 Importing data file into Jupyter Notebook

Lecture 9 Dealing with missing or nan values

Lecture 10 Dealing with inconsistent or mistaken data

Lecture 11 Managing and assigning correct data types

Lecture 12 Identifying and removing duplicate values

Section 4: Manipulating Data for Statistical Data Analysis

Lecture 13 Arranging and sorting dataset by variables

Lecture 14 Conditional filtering (e.g., and, or, not etc.)

Lecture 15 Merging datasets and adding new variables

Lecture 16 Concatenating datasets and adding extra data

Section 5: Transforming Data into Normal Distribution

Lecture 17 Test the normal distribution for numeric data

Lecture 18 Square root transformation for normality

Lecture 19 Logarithmic transformation for normality

Lecture 20 Box-cox transformation for normality

Lecture 21 Yeo-jhonson transformation for normality

Section 6: Statistical Analysis and Hypothesis Testing

Lecture 22 Frequency and Percentage analysis

Lecture 23 Descriptive analysis (Mean, deviation, median, etc.)

Lecture 24 One Sample T-Test: Measure difference as a whole

Lecture 25 Independent Sample T-Test: Measure difference in two groups

Lecture 26 Oneway ANOVA: Measure difference in two or more groups

Lecture 27 Chi-square Test for Independence: Association between nominal data

Lecture 28 Pearson Correlation: Relationship between numeric data

Lecture 29 Regression Analysis: Measure the influence

Section 7: Tips, Tricks and Resources

Lecture 0 ChatGPT for Fastest Python Programming and Debugging

Lecture 30 Other Resources

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

HOMEPAGE


  https://www.udemy.com/course/applied-statistics-and-analytics-in-python-and-chatgpt/ 


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