# 5 Free University Courses to Learn Statistics

Learning statistics can be a much more effective and enjoyable process if you have the right resources. But with too many learning resources, you’ll often feel overwhelmed and not make much progress. So how do you find the right resources that actually help you become proficient in stats?

To help you find the balance, we’ve compiled a list of statistics courses from some of the best universities in the world. Most of these courses are offered on platforms like Coursera and edX. So you can audit them for free and access the course materials.

When you work through such courses, you get a structured learning path and sufficient practice exercises and programming assignments to reinforce your understanding. So let’s get started.

## 1. Introduction to Statistics – Stanford University

If you’re looking for a good first course in statistics, the Introduction to Statistics course from Stanford is a great place to start. In this course, you’ll learn the essential statistical thinking concepts that are necessary to understand and analyze data.

Here’s an overview of the course contents:

• Descriptive statistics
• Probability and probability distributions
• Sampling distributions
• The Central Limit Theorem
• Regression
• Confidence intervals
• Tests of significance
• Resampling
• Analysis of categorical data
• One-Way Analysis of Variance (ANOVA)
• Multiple comparisons

## 2. Statistics with Python – University of Michigan

Once you have a good grasp of statistics foundations, you should also work on developing programming and modeling skills in a programming language, preferably Python or R. If you prefer using Python, the Statistics with Python Specialization offered by the University of Michigan on Coursera, is for you.

This specialization will help you learn how to use Python for data visualization, statistical inference, and modeling. The courses in the specialization offer a blend of lectures covering theoretical foundations as well as Python programming assignments and exercises to help you actively apply what you learn. The following are the courses in this specialization:

• Understanding and Visualizing Data with Python
• Inferential Statistical Analysis with Python
• Fitting Statistical Models to Data with Python

## 3. Statistical Learning with Python – Stanford University

Statistical Learning with Python from Stanford, offered on edX, is a comprehensive course to learn essential statistics for data science along with the different machine learning paradigms and important algorithms. If you prefer using R instead, you can check out the original Statistical Learning with R course which uses R.

Here is an overview of important topics that this course covers:

• Linear regression
• Classification
• Resampling methods
• Linear model selection and regularization
• Moving beyond linearity
• Tree-based methods
• Support vector machines
• Survival analysis and censored data
• Unsupervised learning
• Multiple testing

## 4. Statistical Inference – Johns Hopkins University

Statistical inference helps you infer or draw conclusions about populations and truths from data. The Statistical Inference course offered by Johns Hopkins University on Coursera facilitates an understanding of the different strategies for inference and how they can help you make more informed decisions about data.

The course is organized into the following modules:

• Probability and expected values
• Variability, distributions, and asymptotics
• Intervals, testing, and p-values
• Power, bootstrapping, and permutation tests

## 5. Bayesian Statistics – University Of California, Santa Cruz

The Bayesian Statistics specialization offered by the University of California, Santa Cruz on Coursera focuses on using tools from Bayesian statistics to perform analysis, forecasting, and building models on real-world data. With this specialization, you can add Bayesian statistics, Bayesian inference, and strong R programming skills to your statistics toolbox.

The specialization is designed as a series of four courses followed by a capstone project. The courses are as follows:

• Bayesian Statistics: From Concept to Data Analysis
• Bayesian Statistics: Techniques and Models
• Bayesian Statistics: Mixture Models
• Bayesian Statistics: Time Series Analysis

The capstone project requires you to use both your statistics and R programming skills to analyze real-world dataset and report your findings.

## Wrapping Up

I hope you found this compilation of university courses to learn statistics helpful. We’ve included courses covering a range of topics and difficulty levels—from beginner to more advanced learners.

So whether you’re a data professional looking to solidify your stats foundations or someone majoring in math and stats, these courses should be good learning companions. Happy learning!

## 2 Replies to “5 Free University Courses to Learn Statistics”

1. Sitapati Mukherjee says:

I am 50 years of age. I have 24 years of industrial experience in the field of production, quality assurance in ductile iron industry. I am interested in DataScience, AI & ML. Doing online course related to this. I want be professional in this field. Can you help me reach my goal?

1. James Carmichael says:

Hi Sitapati…Absolutely, I can help guide you on your journey to becoming a professional in Data Science, AI, and ML. Here’s a structured approach to help you reach your goal:

### 1. **Education and Courses**
Continue with your online courses and consider these comprehensive paths:
– **Coursera**:
– **Data Science Specialization** by Johns Hopkins University
– **Machine Learning** by Andrew Ng
– **edX**:
– **MicroMasters® Program in Statistics and Data Science** by MIT
– **Artificial Intelligence** by Columbia University
– **Udacity**:
– **Data Scientist Nanodegree**
– **Artificial Intelligence Nanodegree**

### 2. **Foundational Skills**
Make sure you have a strong understanding of the following:
– **Mathematics and Statistics**: Probability, Linear Algebra, Calculus, and Statistics
– **Programming**: Python is essential. Familiarize yourself with libraries such as NumPy, pandas, scikit-learn, TensorFlow, and Keras.

### 3. **Practical Experience**
– **Projects**: Work on real-world projects to build a portfolio. Use platforms like Kaggle to find datasets and participate in competitions.
– **Internships/Part-time Jobs**: Consider internships or part-time roles in data science to gain practical experience.

### 4. **Networking**
– **Join Communities**: Engage with communities on platforms like LinkedIn, GitHub, and Kaggle.
– **Meetups and Conferences**: Attend data science and AI conferences, webinars, and local meetups to network with professionals.

### 5. **Certifications**
– **IBM Data Science Professional Certificate**
– **Google Professional Machine Learning Engineer**
– **AWS Certified Machine Learning – Specialty**

### 6. **Specialize**
Given your background in production and quality assurance, you might want to specialize in areas like:
– **Predictive Maintenance**: Using ML to predict equipment failures.
– **Quality Control**: Applying AI to improve quality assurance processes.
– **Supply Chain Optimization**: Leveraging data science to optimize supply chain operations.

### 7. **Resume and Job Search**
– **Update Resume**: Highlight your industrial experience and how it complements your data science skills.
– **Job Portals**: Use portals like LinkedIn, Indeed, and Glassdoor to search for data science roles.
– **Professional Organizations**: Join organizations like the Data Science Society or the American Statistical Association.

### 8. **Continuous Learning**
– **Books**:
– **”Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron**
– **”Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville**
– **”Python for Data Analysis” by Wes McKinney**
– **Blogs and Journals**: Follow blogs like Towards Data Science, Analytics Vidhya, and journals like the Journal of Machine Learning Research.

### Personal Plan Example
#### Year 1:
– **Q1-Q2**: Complete foundational courses on Coursera and edX.
– **Q3-Q4**: Work on projects and start building a portfolio. Participate in Kaggle competitions.

#### Year 2:
– **Q1-Q2**: Gain practical experience through internships or part-time roles. Attend networking events.
– **Q3-Q4**: Specialize in a niche area relevant to your background. Obtain a certification.

#### Year 3:
– **Q1-Q2**: Apply for full-time roles in data science, highlighting your unique industrial experience.
– **Q3-Q4**: Continue learning and adapting to new technologies and trends in data science.

By following this structured approach and leveraging your extensive industrial experience, you can successfully transition into a professional role in Data Science, AI, and ML. Good luck!