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

Traffic lights

Read about what's good

what should give you pause

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

Reviews summary

Foundational statistics and python for data analysis

According to learners, "Statistics for Data Analysis Using Python" is a strong foundational course designed for beginners in both statistics and Python. Students particularly appreciate the clear explanations of complex statistical concepts, often reinforced by manual calculations for building intuition. The course effectively provides practical application through hands-on coding exercises and projects, utilizing key libraries such as NumPy, Pandas, Matplotlib, and Seaborn. While offering broad coverage of essential topics, some learners might find the pacing slow if they already possess prior knowledge in one of the subject areas.

Pacing varies; broad coverage, not deep specialization.

"If you already have some Python knowledge, the initial sections on basics might feel a bit slow for you."

"While it covers a wide range of topics, don't expect it to dive into highly advanced statistical methods."

"For a complete beginner, the course can be demanding but highly rewarding if you dedicate enough time."

Instructor explains complex topics clearly and effectively.

"The instructor did an excellent job of breaking down complex statistical ideas into easily digestible parts."

"I found the explanations very easy to follow, even for challenging concepts like Hypothesis Testing and ANOVA."

"The clear and concise teaching style kept me motivated throughout the course."

Offers valuable hands-on coding experience.

"The coding exercises were essential for immediate application and reinforcing my understanding of the concepts."

"I enjoyed working through the practical examples with NumPy and Pandas; it made the learning process very interactive."

"Setting up Jupyter Notebook and engaging with the coding challenges made Python feel much more approachable."

Balances theoretical understanding with practical application.

"I highly appreciated the manual calculation explanations; they gave me a deep intuition for the statistical tests."

"The course successfully combines theoretical concepts with practical application using Python, which is exactly what I needed."

"Learning the 'why' behind the statistics alongside the 'how' using Python was a major plus for me."

Establishes a strong base in both statistics and Python.

"As someone new to both statistics and Python, I found this course incredibly helpful in building a solid foundation."

"It really helped me grasp the basics effectively before diving into practical applications."

"The course delivered on its promise of starting from scratch, making it accessible even for absolute beginners."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Statistics for Data Analysis Using Python with these activities:

Review your notes from other statistics courses

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If you have taken other statistics courses, reviewing your notes will help you to refresh your memory and to build on your prior knowledge.

Browse courses on Probability

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Gather your notes from any previous statistics courses you have taken.
Review the notes and identify the key concepts that you need to remember.

Read the book "Probability and Statistics for Engineers and Scientists"

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This book provides a comprehensive overview of the fundamental concepts of probability and statistics, which will be essential for understanding the course материала.

View Probability & Statistics for Engineers &... on Amazon

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Read the chapters on probability and descriptive statistics.
Complete the practice problems at the end of each chapter.

Solve practice problems from the textbook

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Solving practice problems will help you to solidify your understanding of the concepts covered in the course.

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Choose a few practice problems from each chapter.
Solve the problems on your own.
Check your answers against the solutions provided in the textbook.

Four other activities

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Watch video tutorials on statistical concepts

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Watching video tutorials can help you to visualize the concepts covered in the course and to see how they are applied in real-world scenarios.

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Search for video tutorials on the topics that you are struggling with.
Watch the tutorials and take notes.

Form a study group with other students

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Studying with other students can help you to learn the material more effectively and to identify areas where you need additional support.

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Find a few other students who are taking the same course.
Meet regularly to discuss the material and to work on practice problems together.

Create a cheat sheet of statistical formulas

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Creating a cheat sheet will help you to memorize the most important statistical formulas.

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Compile a list of all the statistical formulas that you need to know for the course.
Create a one-page cheat sheet that includes the formulas and their descriptions.

Collect data and perform a statistical analysis

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Collecting data and performing a statistical analysis will help you to apply the concepts covered in the course to a real-world problem.

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Decide on a research question that you would like to answer.
Design a study to collect data that will help you to answer your research question.
Collect the data.
Perform a statistical analysis of the data.
Write a report on your findings.

Career center

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

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

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

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.

See salaries and explore the career path for Quantitative Analyst

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

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Epidemiologist

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.

See salaries and explore the career path for Biostatistician

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.

See salaries and explore the career path for Market Researcher

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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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.

See salaries and explore the career path for Risk Manager

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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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.

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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.

See salaries and explore the career path for Data Engineer