Applied Statistics And Analytics In Python And Chatgpt

Free Download 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
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