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Statistics for Data Analysis Using Python

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Statistics for Data Analysis Using Python, available at $84.99, has an average rating of 4.54, with 138 lectures, 34 quizzes, based on 1210 reviews, and has 9056 subscribers.

You will learn about Learn Python from the basics with no prior knowledge required, making this course accessible to everyone. Understand statistics from the ground up, with no prior knowledge needed, ensuring a solid foundation in both Python and statistics. Start with basic statistical concepts and progressively apply these concepts using Python for a comprehensive learning experience. Enjoy a balanced combination of theory and practice, enhancing your understanding and application of statistical methods. Master descriptive statistics, including mean, mode, median, standard deviation, variance, and interquartile range, using Python. Dive into inferential statistics with one and two-sample z-tests, t-tests, Chi-Square tests, F-tests, ANOVA, and more, gaining practical skills. Explore various probability distributions, such as normal, binomial, and Poisson, and learn to implement these in Python. Understand how to visualize data effectively using Python libraries, creating insightful graphs and charts. Enhance your resume with valuable skills in Python and statistics, making you a competitive candidate in data-driven fields. Gain confidence in your ability to perform statistical analyses and interpret results using Python, boosting your career prospects. This course is ideal for individuals who are Anyone who want to use statistics to make fact based decisions. or Anyone who wants to learn Python for career in data science. or Anyone who thinks Statistics is confusing and wants to learn it in plain and simple language. It is particularly useful for Anyone who want to use statistics to make fact based decisions. or Anyone who wants to learn Python for career in data science. or Anyone who thinks Statistics is confusing and wants to learn it in plain and simple language.

Enroll now: Statistics for Data Analysis Using Python

Summary

Title: Statistics for Data Analysis Using Python

Price: $84.99

Average Rating: 4.54

Number of Lectures: 138

Number of Quizzes: 34

Number of Published Lectures: 137

Number of Published Quizzes: 31

Number of Curriculum Items: 172

Number of Published Curriculum Objects: 168

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn Python from the basics with no prior knowledge required, making this course accessible to everyone.
  • Understand statistics from the ground up, with no prior knowledge needed, ensuring a solid foundation in both Python and statistics.
  • Start with basic statistical concepts and progressively apply these concepts using Python for a comprehensive learning experience.
  • Enjoy a balanced combination of theory and practice, enhancing your understanding and application of statistical methods.
  • Master descriptive statistics, including mean, mode, median, standard deviation, variance, and interquartile range, using Python.
  • Dive into inferential statistics with one and two-sample z-tests, t-tests, Chi-Square tests, F-tests, ANOVA, and more, gaining practical skills.
  • Explore various probability distributions, such as normal, binomial, and Poisson, and learn to implement these in Python.
  • Understand how to visualize data effectively using Python libraries, creating insightful graphs and charts.
  • Enhance your resume with valuable skills in Python and statistics, making you a competitive candidate in data-driven fields.
  • Gain confidence in your ability to perform statistical analyses and interpret results using Python, boosting your career prospects.

Who Should Attend

  • Anyone who want to use statistics to make fact based decisions.
  • Anyone who wants to learn Python for career in data science.
  • Anyone who thinks Statistics is confusing and wants to learn it in plain and simple language.

Target Audiences

  • Anyone who want to use statistics to make fact based decisions.
  • Anyone who wants to learn Python for career in data science.
  • Anyone who thinks Statistics is confusing and wants to learn it in plain and simple language.

Perform simple or complex statistical calculations using Python! – You don’t need to be a programmer for this 🙂

You are not expected to have any prior knowledge of Python. I will start with the basics. Coding exercises are provided to test your learnings.

The course not only explains, how to conduct statistical tests using Python but also explains in detail, how to perform these using a calculator (as if, it was the 1960s). This will help you in gaining the real intuition behind these tests.

Learn statistics, and apply these concepts in your workplace using Python.

The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concepts. Various examples and data-sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use Python to perform these calculations.

The following areas of statistics are covered:

Descriptive Statistics– Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation.

Data Visualization – Commonly used plots such as Histogram, Box and Whisker Plot and Scatter Plot, using the Matplotlib.pyplot and Seaborn libraries.

Probability – Basic Concepts, Permutations, Combinations

Population and Sampling – Basic concepts

Probability Distributions – Normal, Binomial and Poisson Distributions

Hypothesis Testing– One Sample and Two Samples – z Test, t-Test, F Test and Chi-Square Test

ANOVA – Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using Python.

The Goodness of Fit and the Contingency Tables.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Installing Anaconda

Lecture 2: Getting started with Jupyter Notebook

Lecture 3: Download Section 1 Resources and the Course Slides

Lecture 4: Getting started with Python

Lecture 5: Variables and Data Types

Lecture 6: An Introduction to Coding Excercises and Course Resources

Lecture 7: Solution: Introduction to coding exercises

Lecture 8: Working with a List – Part 1

Lecture 9: Solution: Select an element from the list

Lecture 10: Working with a List – Part 2

Lecture 11: Solution: A review of lists

Lecture 12: Working with a Dictionary

Lecture 13: Working with a Tuple

Lecture 14: Working with a Set

Lecture 15: Logical Operators

Chapter 2: Descriptive Statistics

Lecture 1: Download Section 2 Resources

Lecture 2: [Theory] Measurement of Central Tendency

Lecture 3: [Theory] Measurement of Dispersion – Part 1

Lecture 4: [Theory] Measurement of Dispersion – Part 2

Lecture 5: Descriptive Statistics Using Python

Lecture 6: Solution: Find the mean, mode, median and standard deviation

Lecture 7: Solution: Find the Inter Quartile Range

Chapter 3: NumPy Package, Probability Distributions and an Introduction to SciPy Package

Lecture 1: Download Section 3 Resources

Lecture 2: NumPy – Part 1

Lecture 3: Solution: Two Dimensional Array

Lecture 4: Solution: Creating a Numpy array

Lecture 5: NumPy – Part 2

Lecture 6: Solution: Filtering from an Array

Lecture 7: Solution: Select a subsection of an Array

Lecture 8: NumPy – Part 3

Lecture 9: Solution: Divide the array elements by 10

Lecture 10: [Theory] Basics of Probability – Part 1

Lecture 11: [Theory] Basics of Probability – Part 2

Lecture 12: [Theory] Basics of Probability – Part 3

Lecture 13: Generating Random Numbers to Simulate the Probability

Lecture 14: A Sample Probability Question

Lecture 15: Solution: Generate Five Random Numbers

Lecture 16: [Theory] Probability Distributions – Introduction

Lecture 17: [Theory] Binomial Distribution

Lecture 18: Binomial Distribution Using NumPy

Lecture 19: Introducing SciPy Package for Binomial Distributions

Lecture 20: Solution: Flipping a Coin

Lecture 21: Solution: Let's Flip Again

Lecture 22: Solution: Number of defectives in a selection

Lecture 23: [Theory] Poisson Distribution

Lecture 24: [Theory] Poisson Distributions – An Example

Lecture 25: Poisson Distribution Using NumPy

Lecture 26: Poisson Distribution Using SciPy

Lecture 27: Solution: Receiving Phone Calls

Lecture 28: Solution: Probability of more than 6 calls

Lecture 29: [Theory] Normal Distribution – Part 1

Lecture 30: [Theory] Normal Distribution – Part 2

Lecture 31: Normal Distribution Using NumPy

Lecture 32: Normal Distribution Using SciPy

Lecture 33: Solution: Area of curve between two values of z

Lecture 34: Descriptive Statistics Using NumPy

Lecture 35: Solution: Mean of Rows

Chapter 4: Pandas Package

Lecture 1: Download Section 4 Resources

Lecture 2: Pandas Series

Lecture 3: Pandas DataFrame

Lecture 4: Solution: Create a DataFrame

Lecture 5: Reading a .csv File (Importing External Data)

Lecture 6: Solution: Importing a CSV file

Lecture 7: DataFrame – Dealing with Columns

Lecture 8: DataFrame – Dealing with Rows

Lecture 9: Solution: What is the Temperature on Monday

Chapter 5: Data Visualization Using Matplotlib.pyplot and Seaborn Libraries

Lecture 1: Download Section 5 Resources

Lecture 2: Histogram using matplotlib.pyplot

Lecture 3: Box Plot using matplotlib.pyplot

Lecture 4: Line and Scatter Plots using matplotlib.pyplot

Instructors

Rating Distribution

  • 1 stars: 7 votes
  • 2 stars: 11 votes
  • 3 stars: 76 votes
  • 4 stars: 387 votes
  • 5 stars: 729 votes

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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