Statistics plays an invaluable role across countless industries and disciplines. Mastering statistical concepts equips you with crucial skills for data analysis and quantitative research Fortunately, with the right strategies and resources, you can learn statistics on your own through self-guided education.

Follow this beginner’s guide to teaching yourself statistics step-by-step:

## Understand the Fundamentals

Like any subject building statistical proficiency starts with grasping the core fundamentals. Make sure you understand these foundational concepts first

**Types of data:**Numerical, categorical, ordinal**Measures of central tendency:**Mean, median, mode**Measures of variability:**Range, variance, standard deviation**Sampling:**Populations, samples, statistical significance**Common distributions:**Normal, binomial, Poisson**Confidence intervals:**Margin of error, confidence levels**Hypothesis testing:**Null hypothesis, p-values, t-tests**Correlation vs causation:**Association vs determination

Solidifying this baseline knowledge establishes the necessary groundwork. All other statistical techniques build on these basics

## Learn by Doing with Datasets

Practice applying fundamental concepts with real datasets. Resources like Kaggle offer thousands of free datasets on every topic imaginable.

Import datasets into statistical software like R or Python. Then manipulate and analyze the data by:

- Calculating summary statistics like means and standard deviations
- Creating visualizations like histograms and scatterplots
- Performing hypothesis tests to compare groups
- Using regression to predict outcomes
- Applying statistical techniques covered in introductory resources

Hands-on practice builds skills and cements theoretical knowledge.

## Find a Statistics Textbook

For systematic learning, work through an introductory statistics textbook. Entry-level books cover topics like:

- Data collection: Sampling, experimental design
- Descriptive statistics: Tables, graphs, summary measures
- Probability: Random variables, distributions
- Statistical inference: Estimation, hypothesis testing
- Correlation and regression
- ANOVA analysis
- Nonparametric tests

Search for a textbook with a teaching style that matches your learning preferences. Then read chapters sequentially while practicing techniques with datasets.

## Take Statistics Courses

Enroll in beginner statistics courses to benefit from structured lessons and expert instruction.

Options include:

**Local colleges:**Audit an introductory course as a non-degree student**Community centers:**Sign up for adult education classes**Online learning platforms:**Take statistics courses from sites like edX, Coursera, Udemy**MOOCs:**Enroll in free Massive Open Online Courses

Traditional courses provide graded assignments, accountability, and teacher support.

## Learn Specific Statistical Tests

When you grasp the basics, delve deeper into specific statistical tests like:

- ANOVA – Compares means across multiple groups
- Chi-square – Tests relationships between categorical variables
- Simple linear regression – Models linear associations between variables
- Logistic regression – Predicts categorical outcomes
- Factor analysis – Reduces variables into fewer factors
- SEM – Models networks of relationships

Select tests relevant to your field and goals. Use datasets to practice running and interpreting each one.

## Understand Statistical Software

Data analysis relies heavily on software. Get hands-on with programs like:

- R – Open-source programming for statistical computing
- Python – Flexible data analysis programming language
- SAS – Traditional statistics software for beginners
- Stata – General-purpose statistical software
- SPSS – User-friendly GUI for analyzing datasets
- Excel – Basic analysis with widespread access

Learn to input, manipulate, analyze, and visualize data using the right tools.

## Join an Online Community

Connect with fellow beginners in online statistics communities. Sites like Reddit offer forums to:

- Ask questions when stuck on concepts
- Discuss learning strategies and resources
- Share project ideas and data visualizations
- Review each other’s code and analyses
- Talk through thorny statistical dilemmas

Peer support keeps you motivated and engaged.

## Read Examples of Real-World Applications

Statistics comes alive when you see it applied to real-world issues. Look for case studies about:

- Published academic studies across every field
- Statistical techniques used at top technology firms
- Data journalism stories at outlets like FiveThirtyEight
- Business analytics guiding decisions at leading companies

Exposure to real-world use cases highlights the practical value of statistics.

## Practice Presenting Data Visually

Learn to communicate statistical insights through compelling charts, graphs, and diagrams. Tools like Tableau let you build interactive data visualizations.

Aimed to make data clear and intuitive for all audiences. Practice visual storytelling using datasets from your own analyses.

## Tackle Practical Projects

At some point, shift focus from building general skills to applying statistics to practical projects.

Potential ideas:

- Perform an analysis for work and present results to colleagues
- Compete in analytics competitions like those on Kaggle
- Volunteer your skills for a cause that matters to you
- Analyze a dataset related to a personal interest or hobby

Practical experience cements proficiency.

## Earn a Certificate or Nanodegree

Formalize your knowledge by completing a certificate or nanodegree program. Options include:

- Harvard’s Data Science Certificate
- IBM Data Science Professional Certificate
- Google Data Analytics Certificate
- Udacity Nanodegree in Statistics and Probability
- edX Statistics and R Certificate

Graduating from a program demonstrates your new analytics abilities.

The resources above explore different paths for mastering statistics. Some techniques work better for certain learning styles. Try various approaches and see what sticks. Consistent practice is key. With dedication and sustained effort, you can absolutely learn statistics on your own.

What strategies have you found most effective? Do you have additional tips for fellow self-guided learners? Please share your experiences in the comments!

## The Best Way to Learn to Statistics for Data Science

By now, you’ve probably noticed that one common theme in “the self-starter way to learning X” is to skip classroom instruction and **learn by “doing sh*t.”**

Mastering statistics for data science is no exception.

In fact, we’re going to tackle key statistical concepts by programming them with code! Trust us… this will be super fun.

If you do not have formal math training, you’ll find this approach much more intuitive than trying to decipher complicated formulas. It allows you to think through the logical steps of each calculation.

If you do have a formal math background, this approach will help you translate theory into practice and give you some fun programming challenges.

Here are the 3 steps to learning the statistics and probability required for data science:

**Core Statistics Concepts –**Descriptive statistics, distributions, hypothesis testing, and regression.**Bayesian Thinking –**Conditional probability, priors, posteriors, and maximum likelihood.**Intro to Statistical Machine Learning –**Learn basic machine concepts and how statistics fit in.

After completing these 3 steps, you’ll be ready to attack more difficult machine learning problems and common real-world applications of data science.

## Statistics Needed for Data Science

Statistics is a broad field with applications in many industries.

Wikipedia defines it as the study of the collection, analysis, interpretation, presentation, and organization of data. Therefore, it shouldn’t be a surprise that data scientists need to know statistics.

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For example, data analysis requires descriptive statistics and probability theory, at a minimum. These concepts will help you make better business decisions from data.

Key concepts include **probability distributions**, **statistical significance**, **hypothesis testing**, and **regression**.

Furthermore, machine learning requires understanding Bayesian thinking. Bayesian thinking is the process of updating beliefs as additional data is collected, and it’s the engine behind many machine learning models.

Key concepts include **conditional probability**, **priors and posteriors**, and **maximum likelihood**.

If those terms sound like mumbo jumbo to you, don’t worry. This will all make sense once you roll up your sleeves and start learning.

## Statistics with Professor B: How to Study Statistics

**How can I learn statistics on my own?**

Here’s how you can learn statistics on your own in four easy steps: 1. Study the core concepts You can start learning statistics by studying the core concepts of the discipline. These include: Descriptive statistics: A single descriptive statistic is a statistic that summarizes similar features or trends in a collection of similar information.

**How do I learn statistics & probability in data science?**

If you do have a formal math background, this approach will help you translate theory into practice and give you some fun programming challenges. Here are the 3 steps to learning the statistics and probability required for data science: Core Statistics Concepts – Descriptive statistics, distributions, hypothesis testing, and regression.

**What can I learn from Stanford’s ‘Introduction to statistics’ course?**

Stanford’s “Introduction to Statistics” teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts.

**How do you use statistics?**

One way to apply statistics is to analyze data from real-world examples. This could include data from scientific studies, surveys, or even business reports. By using statistical methods, you can identify patterns and trends in the data, and draw conclusions based on your findings.