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Introduction to Statistics with Python

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Introduction to Statistics with Python, available at $59.99, has an average rating of 3.67, with 59 lectures, 12 quizzes, based on 3 reviews, and has 125 subscribers.

You will learn about Basic statistical analysis How to utilize the Jupyter Notebook Basic of the Python language for statistical analysis Data visualization This course is ideal for individuals who are Beginning Python users or People interested in data science or Students looking to learn statistics It is particularly useful for Beginning Python users or People interested in data science or Students looking to learn statistics.

Enroll now: Introduction to Statistics with Python

Summary

Title: Introduction to Statistics with Python

Price: $59.99

Average Rating: 3.67

Number of Lectures: 59

Number of Quizzes: 12

Number of Published Lectures: 59

Number of Published Quizzes: 12

Number of Curriculum Items: 71

Number of Published Curriculum Objects: 71

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Basic statistical analysis
  • How to utilize the Jupyter Notebook
  • Basic of the Python language for statistical analysis
  • Data visualization

Who Should Attend

  • Beginning Python users
  • People interested in data science
  • Students looking to learn statistics

Target Audiences

  • Beginning Python users
  • People interested in data science
  • Students looking to learn statistics

This course provides a basic introduction to statistics and the use of Python a popular programming language. During the course, we look at many fundamental ideas in statistics within the framework of analysis in Jupyter Notebook a commonly used integrated development environment for Python. Topics include both descriptive and inferential statistics. Concepts related to data visualization will also be discussed to empower you in your journey as a data analyst.  Among the concepts shared include mean, median, mode, standard deviation, histograms, confidence intervals,  t-test, correlation, regression, ANOVA, chi-square, and more. All of these ideas can be learned from the comfort of your own home without the hassle of having to go to class. Quizzes are also available to assess your progress along with the actual Python that is used in the videos.

For students and those who want exposure to statistical analysis while also obtaining a basic insight into some tools involved in data science, this is a course for you. With the growth in demand for quantitative skills with the rise of Big Data, developing mastery of statistics and analytical tools such as Jupyter Notebook is becoming a norm.

Come and be a part of the analytics experience by enrolling in a course that will prepare you for the demands of the mid 20th century.

Course Curriculum

Chapter 1: What is statistics

Lecture 1: Lesson 1.1 What is statistics

Lecture 2: Lesson 1.2 Types of Variables

Lecture 3: Lesson 1.3 More on Variables

Lecture 4: Lesson 1.4 Statistical Notation

Chapter 2: How do You Use Python

Lecture 1: 2.1 Installing Python

Lecture 2: Lesson 2.2 Python Basics

Lecture 3: Lesson 2.3 Functions

Lecture 4: Lesson 2.4 Loading Data

Chapter 3: How do You Visualize Numbers?

Lecture 1: Instructions for loading CSV files

Lecture 2: Lesson 3.1 Frequency Tables with Categorical Variables

Lecture 3: Lesson 3.2 Frequency Tables with Categorical Variables in Python

Lecture 4: Lesson 3.3 Frequency Tables with Continuous Variables

Lecture 5: Lesson 3.4 Scatter Plot & Histogram

Chapter 4: What are Measures of Central Tendency?

Lecture 1: Instructions for loading CSV files

Lecture 2: Lesson 4.1 Mean, Median, Mode

Lecture 3: Lesson 4.2 Mean, Median, Mode in Python

Lecture 4: Lesson 4.3 Mean vs Median vs Mode OPTIONAL

Chapter 5: What are Measures of Dispersion?

Lecture 1: Instructions for loading CSV files

Lecture 2: Lesson 5.1 Range and Finding Range in Python

Lecture 3: Lesson 5.2 Variance & Standard Deviation

Lecture 4: Lesson 5.3 Find Variance & Standard Deviation in Python

Lecture 5: Lesson 5.4 Quartiles

Lecture 6: Lesson 5.5 Find Quartiles and make Box plots in Python

Lecture 7: Lesson 5.6 Kurtosis & Skew

Lecture 8: Lesson 5.7 Find Kurtosis and Skew in Python

Chapter 6: What is Probability?

Lecture 1: Lesson 6.1-Probability

Lecture 2: Lesson 6-2 Bayesian Probability OPTIONAL

Lecture 3: Lesson 6-3 Review OPTIONAL

Chapter 7: What is Normal Distribution?

Lecture 1: Lesson 7.1 Normal Distribution

Lecture 2: Lesson 7.2 Standard Normal Distribution

Lecture 3: Lesson 7.3 Normal, Standard, & Sampling Distributions OPTIONAL

Chapter 8: What are Confidence Intervals?

Lecture 1: Lesson 8.1 Confidence Intervals Defined

Lecture 2: Lesson 8.2 Finding Confidence Intervals in Python

Lecture 3: Lesson 8.3 Confidence Intervals for Proportions

Lecture 4: Lesson 8.4 Finding Confidence Intervals for Proportions in Python

Chapter 9: What is Hypothesis Testing?

Lecture 1: Lesson 9.1 Hypotheses

Lecture 2: Lesson 9.2 One Sample t-test

Lecture 3: Lesson 9.3 One Sample t-test in Python

Lecture 4: Lesson 9.4 t-test for Proportion

Lecture 5: Lesson 9.5 t-test for Proportion in Python

Chapter 10: What is Two Sample Hypothesis Testing?

Lecture 1: Lesson 10.1 T-test for Two Means

Lecture 2: Lesson 10.2 T-test for Two Means in Python

Lecture 3: Lesson 10.3 Paired T-Test

Lecture 4: Lesson 10.4 Paired T-Test in Python

Lecture 5: Lesson 10.5 Two-Sample Test of Proportions

Lecture 6: Lesson 10.6 Two-Sample Test of Proportions in Python

Chapter 11: What is Analysis of Variance?

Lecture 1: Lesson 11.1 ANOVA

Lecture 2: Lesson 11.2 Other Forms of ANOVA OPTIONAL

Lecture 3: Lesson 11.3 ANOVA Under the Hood OPTIONAL

Chapter 12: What is Correlation and Regression?

Lecture 1: Lesson 12.1 Scatter Plots & Correlation

Lecture 2: Lesson 12.2 Find Correlation in Python

Lecture 3: Lesson 12.3 Simple Linear Regression

Lecture 4: Lesson 12.4 Calculate Simple Linear Regression in Python

Lecture 5: Lesson 12.5 Multiple Regression

Lecture 6: Lesson 12.6 Calculate Multiple Regression in Python

Chapter 13: What is Chi-Square?

Lecture 1: Lesson 13.1 Chi Square Goodness of Fit

Lecture 2: Lesson 13.2 Chi-Square Goodness of Fit in Python

Lecture 3: Lesson 13.3 Chi-Square Test of Independence in Python

Lecture 4: Conclusion

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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!

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