Statistics for Data Analysis Using Python from Udemy

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.

What's inside

Learning objectives

Python from basics - no prior knowledge required
Statistics from basics - no prior knowledge required
You will first learn the basic statistical concepts, followed by application of these concepts using python. this course is a nice combination of theory and practice.

Inferential statistics - one and two sample z, t, chi square, f tests, anova and more.
Descriptive statistics - mean, mode, median, standard deviation, variance and interquartile range
Probability distributions - normal, binomial and poisson

Python from basics - no prior knowledge required
Statistics from basics - no prior knowledge required
You will first learn the basic statistical concepts, followed by application of these concepts using python. this course is a nice combination of theory and practice.
Inferential statistics - one and two sample z, t, chi square, f tests, anova and more.
Descriptive statistics - mean, mode, median, standard deviation, variance and interquartile range
Probability distributions - normal, binomial and poisson

Syllabus

Introduction

Installing Anaconda

Getting started with Jupyter Notebook

Download Section 1 Resources and the Course Slides

Getting started with Python

Variables and Data Types

An Introduction to Coding Excercises and Course Resources

Introduction to coding exercises

Solution: Introduction to coding exercises

Working with a List - Part 1

Select an element from the list

Solution: Select an element from the list

Working with a List - Part 2

A review of lists

Solution: A review of lists

Working with a Dictionary

Working with a Tuple

Working with a Set

Logical Operators

Descriptive Statistics

Download Section 2 Resources

[Theory] Measurement of Central Tendency

[Theory] Measurement of Dispersion - Part 1

[Theory] Measurement of Dispersion - Part 2

Quiz: Descriptive Statistics

Descriptive Statistics Using Python

Find the mean, mode, median and standard deviation

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

Find the Inter Quartile Range

Solution: Find the Inter Quartile Range

NumPy Package, Probability Distributions and an Introduction to SciPy Package

Download Section 3 Resources

NumPy - Part 1

Two Dimensional Array

Solution: Two Dimensional Array

Creating a Numpy array

Solution: Creating a Numpy array

NumPy - Part 2

Filtering from an Array

Solution: Filtering from an Array

Select a subsection of an Array

Solution: Select a subsection of an Array

NumPy - Part 3

Divide the Array elements by 10

Solution: Divide the array elements by 10

[Theory] Basics of Probability - Part 1

[Theory] Basics of Probability - Part 2

[Theory] Basics of Probability - Part 3

Quiz: Basics of Probability

Generating Random Numbers to Simulate the Probability

A Sample Probability Question

Generate Five Random Numbers

Solution: Generate Five Random Numbers

[Theory] Probability Distributions - Introduction

[Theory] Binomial Distribution

Quiz: Binomial Distribution

Binomial Distribution Using NumPy

Introducing SciPy Package for Binomial Distributions

Flipping a Coin

Solution: Flipping a Coin

Let's Flip Again

Solution: Let's Flip Again

Number of defectives in a selection

Solution: Number of defectives in a selection

[Theory] Poisson Distribution

[Theory] Poisson Distributions - An Example

Poisson Distribution Using NumPy

Poisson Distribution Using SciPy

Receiving Phone Calls

Solution: Receiving Phone Calls

Probability of more than 6 calls

Solution: Probability of more than 6 calls

[Theory] Normal Distribution - Part 1

[Theory] Normal Distribution - Part 2

Quiz: Normal Distrubution

Normal Distribution Using NumPy

Normal Distribution Using SciPy

Area of curve between two values of z

Solution: Area of curve between two values of z

Descriptive Statistics Using NumPy

Mean of Rows

Solution: Mean of Rows

Pandas Package

Download Section 4 Resources

Pandas Series

Pandas DataFrame

Create a DataFrame

Solution: Create a DataFrame

Reading a .csv File (Importing External Data)

Importing a CSV file

Solution: Importing a CSV file

DataFrame - Dealing with Columns

DataFrame - Dealing with Rows

What is the Temperature on Monday?

Solution: What is the Temperature on Monday

Data Visualization Using Matplotlib.pyplot and Seaborn Libraries

Download Section 5 Resources

Histogram using matplotlib.pyplot

Box Plot using matplotlib.pyplot

Line and Scatter Plots using matplotlib.pyplot

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches the fundamentals of statistics and data science, which is standard in industry and academia

Develops skills in Python, which are core skills for data scientists

Emphasizes hands-on experience through coding exercises, which helps learners retain knowledge

Provides a strong foundation for learners with no prior knowledge of Python or statistics

Covers various statistical techniques and probability distributions, giving learners a comprehensive understanding of data analysis

Appropriate for learners in various fields, including business, science, and social sciences, due to its focus on practical applications

Career center

Learners who complete Statistics for Data Analysis Using Python will develop knowledge and skills that may be useful to these careers:

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can help you build a strong foundation in statistics, which is essential for Quantitative Analysts. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as a Quantitative Analyst.

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Statistician

Statisticians collect, analyze, interpret, and present data. They use statistical methods to develop models and theories that can be used to make predictions or decisions. This course can help you build a strong foundation in statistics, which is essential for Statisticians. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as a Statistician.

See salaries and explore the career path for Statistician

Data Analyst

Data Analysts use statistical techniques to analyze data, identify patterns and trends, and help businesses make informed decisions. This course can help you build a strong foundation in statistics and data analysis, which are essential skills for Data Analysts. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as a Data Analyst.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use statistical techniques to analyze data, identify patterns and trends, and help businesses make informed decisions. This course can help you build a strong foundation in statistics, which is essential for Data Scientists. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as a Data Scientist.

See salaries and explore the career path for Data Scientist

Business Analyst

Business Analysts use statistical techniques to analyze data, identify patterns and trends, and help businesses make informed decisions. This course can help you build a strong foundation in statistics, which is essential for Business Analysts. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as a Business Analyst.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models. They use statistical techniques to train and evaluate models, and they work with data scientists to interpret results. This course can help you build a strong foundation in statistics, which is essential for Machine Learning Engineers. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Insurance Underwriter

Insurance Underwriters use statistical techniques to assess risk and uncertainty. They work with insurance companies to determine the likelihood of an event occurring and to set insurance premiums. This course can help you build a strong foundation in statistics, which is essential for Insurance Underwriters. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as an Insurance Underwriter.

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Operations Research Analyst

Operations Research Analysts use statistical techniques to analyze data and identify ways to improve efficiency and productivity. They work with businesses and organizations to develop and implement solutions to complex problems. This course can help you build a strong foundation in statistics, which is essential for Operations Research Analysts. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as an Operations Research Analyst.

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Biostatistician

Biostatisticians use statistical techniques to analyze biological data. They work with scientists and medical researchers to design and conduct studies, and they analyze data to help answer research questions. This course can help you build a strong foundation in statistics, which is essential for Biostatisticians. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as a Biostatistician.

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Market Researcher

Market Researchers use statistical techniques to collect and analyze data about consumer behavior. They use this data to help businesses make informed decisions about marketing and product development. This course can help you build a strong foundation in statistics, which is essential for Market Researchers. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as a Market Researcher.

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Actuary

Actuaries use statistical techniques to assess risk and uncertainty. They work with insurance companies, pension funds, and other financial institutions to develop and manage risk management programs. This course can help you build a strong foundation in statistics, which is essential for Actuaries. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as an Actuary.

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Risk Manager

Risk Managers use statistical techniques to assess risk and uncertainty. They work with businesses and organizations to develop and implement risk management programs. This course can help you build a strong foundation in statistics, which is essential for Risk Managers. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as a Risk Manager.

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Epidemiologist

Epidemiologists investigate the causes and patterns of disease. They use statistical techniques to analyze data and identify risk factors. This course can help you build a strong foundation in statistics, which is essential for Epidemiologists. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as an Epidemiologist.

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Data Engineer

Data Engineers design and build data pipelines that collect, store, and process data. They use statistical techniques to ensure that data is accurate and reliable. This course can help you build a strong foundation in statistics, which is essential for Data Engineers. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as a Data Engineer.

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Financial Analyst

Financial Analysts use statistical techniques to analyze financial data and make investment decisions. This course can help you build a strong foundation in statistics, which is essential for Financial Analysts. You will learn how to use Python, a popular programming language for data analysis, and how to perform statistical tests and interpret results. This course will provide you with the skills you need to succeed as a Financial Analyst.

See salaries and explore the career path for Financial Analyst