Applied Statistics in Python and ChatGPT

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StatElite Academy

3:32:09

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1 - Setting up Python Jupyter Notebook and ChatGPT

1 - Install Python and Jupyter Notebook.html

1 - Mac.pdf

1 - Windows.pdf

2 - Instructions-of-setting-up-ChatGPT.pdf

2 - Setting Up ChatGPT for SMART Analysis.html

3 - Download dataset for practice quizzes.html

3 - Extra-Data.xlsx

3 - Extra-Variables.xlsx

3 - sales.xlsx

4 - Instructions for Quizzes IMPORTANT.html

2 - What is Statistical Data Analysis

5 - Understanding the concept of statistical data analysis.mp4

08:09

6 - Confidence level Significance level and Pvalue.mp4

05:05

7 - Understanding complete workflow in statistical analysis.mp4

07:40

3 - Cleaning Data for Statistical Data Analysis

2 - Data Cleaning in Python.html

8 - Importing data file into Jupyter Notebook.mp4

07:42

9 - Dealing with missing or nan values.mp4

12:43

10 - Dealing with inconsistent or mistaken data.mp4

11:17

11 - Managing and assigning correct data types.mp4

07:43

12 - Identifying and removing duplicate values.mp4

04:22

4 - Manipulating Data for Statistical Data Analysis

3 - Data Manipulation in Python.html

13 - Arranging and sorting dataset by variables.mp4

05:17

14 - Conditional filtering eg and or not etc.mp4

10:34

15 - Merging datasets and adding new variables.mp4

03:47

16 - Concatenating datasets and adding extra data.mp4

03:47

5 - Transforming Data into Normal Distribution

4 - Data Transformation.html

17 - Test the normal distribution for numeric data.mp4

08:29

18 - Square root transformation for normality.mp4

06:15

19 - Logarithmic transformation for normality.mp4

05:57

20 - Boxcox transformation for normality.mp4

05:45

21 - Yeojhonson transformation for normality.mp4

05:14

6 - Statistical Analysis and Hypothesis Testing

5 - Statistical Data Analysis in Python.html

22 - Frequency and Percentage analysis.mp4

16:09

23 - Descriptive analysis Mean deviation median etc.mp4

11:54

24 - One Sample TTest Measure difference as a whole.mp4

09:44

25 - Independent Sample TTest Measure difference in two groups.mp4

06:46

26 - Oneway ANOVA Measure difference in two or more groups.mp4

10:32

27 - Chisquare Test for Independence Association between nominal data.mp4

12:45

28 - Pearson Correlation Relationship between numeric data.mp4

10:05

29 - Regression Analysis Measure the influence.mp4

14:28

7 - Tips Tricks and Resources

30 - Complete.pdf

30 - Confidence.pdf

30 - Other Resources.html

30 - Steps.pdf

30 - Various.pdf

Description

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.

Who is this for?

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

What You Need to Know?

No prior experience is required.

Beginners are most welcome.

Basic computer literacy.

Interest in data analysis and statistics.

More details

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.
Who this course is for:
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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At StatElite Academy, our mission is to democratize access to statistical knowledge and empower individuals to excel in the realm of data analysis and interpretation. We believe that statistics is not merely a tool for academics or professionals in specific fields but a vital skill set for anyone navigating today's data-driven world. Through comprehensive courses and hands-on training, we aim to break down complex statistical concepts into easily understandable modules, fostering a community of lifelong learners equipped with the analytical prowess to tackle real-world problems.Our vision is to cultivate a global network of statistically literate individuals who not only possess the technical proficiency to analyze data but also the creative insight to derive meaningful insights. By emphasizing practical applications and real-world scenarios, we strive to bridge the gap between theory and practice, enabling our students to thrive in diverse industries and embark on successful freelance careers. At StatElite Academy, we envision a future where statistical literacy is not just a skill but a cornerstone of informed decision-making and innovation.

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