# Data Science: Learn Computational Statistics with Python

Python is the programming language of choice for scientific computing, offering a powerful environment for statistical data analysis.

Python is excellent for Data Science with a plethora of useful statistical and mathematical resources for data analysts/scientists.

Python provides a built-in library for descriptive statistics and there are third-party libraries like NumPy, pandas, SciPy that help acquire, organize, and process information for Statistical Analysis.

In this article, we want to suggest resources to learn Essential Statistics for Data Science with Python.

These resources provide the statistical background you need to get started in data science with Python programming, including probability, random distributions, confidence intervals, hypothesis testing, ANOVA, and building regression models for prediction.

If you, however, prefer learning from the books, we recommend two in particular.

Where most instructors teach collecting and cleaning data in Python, these Data Science Educators have gone further, providing guidance and technical mentor support on how to perform a proper Statistical Analysis with Python.

Let's take a closer look...

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## Best Courses to Learn Computational Statistics with Python – 2022 Updated

These highest-rated courses are designed for beginners and cover the basics and intermediate topics of Python and Statistics for Data Science.

### 1. Introduction to Statistics in Python

This Introductory-level statistics course in Python for beginners is developed by DataCamp and taught by Adel Nehme.

Through the guided lectures, you’ll gain lifelong skills in Python for Data Science.

This course to grow Statistical skills is perfect to learn to calculate averages, use scatterplots to show the relationship between numeric values, and calculate the correlation using Python.

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### 2. Introduction to Computational Statistics for Data Scientists

The high-quality specialization aims to teach the basics of Computational Statistics to perform inference to Data Science learners.

The life long skills you gain in this course will bootstrap also your abilities to have a tight grip on the basics of Bayesian modelling and inference.

Along with the functional knowledge, you will gain a conceptual understanding of the techniques used to perform Bayesian inference in practice.

### 3. Probability and Statistics with Python

This beginner-level learning path in Python is offered by Dataquest to help learners understand the fundamentals of probability for statistical analysis.

You will learn important data science skills in Python using Statistical libraries for data analysis, data cleaning, data visualization, etc.

This course is excellent for beginners to learn advanced statistical concepts for more powerful data analysis using conditional probability, Bayes' theorem, Naive Bayes, Hypothesis testing, etc.

By the end, you'll be equipped with the Statistical Literacy to use Python for Data Science.

### 4. Statistics Fundamentals with Python (Skill-Track)

This skill track provides a comprehensive path to gain powerful skills in Data Analysis, Exploratory Data Analysis, Inference, Formal Statistical Modeling, interpretation, and communication.

The courses included in this skill track will help you tremendously in applying statistical knowledge and techniques to business contexts and working on complex data science projects.

This skill track provides the statistical foundations required for machine learning and data science.

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GO TO — Statistics Fundamentals with Python (Skill-Track)

### 5. Statistics with Python Specialization

This highest-rated specialization offered by The University of Michigan is suitable for beginners to master Statistical Inference, Data Visualization, and Modeling in Python.

You will learn Data Analysis, Confidence Interval, Statistical Hypothesis Testing, Bayesian Statistics and Statistical Regression.

By the end, you will have gained skills in Python Programming, Data Visualization, Statistical Model, Statistical inference methods.

### 7. Statistics and Data Science – MicroMasters

This highly recommended Micro Master's program offered by MIT via edX provides expert instructions to help learners grasp the foundations of data science, statistics, and machine learning.

You’ll learn through the guided lectures, exercises and projects to analyze and make data-driven predictions through probabilistic modelling and statistical inference.

This comprehensive Data Science for Statistics Certification program is very suitable for learners interested in Big Data.

You’ll gain employable skills to build machine learning algorithms on your own to extract meaningful information even from seemingly unstructured data.

You’ll learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks.

Upon successful completion, you’ll be prepared for job titles such as Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, and Data Engineer.

### 8. Statistical Thinking in Python (Part 1)

This high-quality “Statistical Thinking in Python” course aims to help you build the foundation you need in Python to think statistically.

The modules covered in this course include:

• Graphical Exploratory Data Analysis
• Quantitative Exploratory Data Analysis
• Thinking Probabilistically–Discrete Variables
• Thinking Probabilistically–Continuous Variables

### 9. Statistical Thinking in Python (Part 2)

In Part 2, you will dig deeper to become highly prepared to execute key tasks in statistical inference; parameter estimation, and hypothesis testing using real-world data in Python.

The modules in this course thoroughly cover the following topics:

• Parameter estimation by optimization.
• Bootstrap confidence intervals.
• Introduction to hypothesis testing.
• Hypothesis test examples.

### 10. Practicing Statistics Interview Questions in Python

This course prepares learners for the Statistical interview required for Data Science in Python by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

By the end, you’ll clearly understand how to work with a diverse collection of datasets, including web-based experiment results and Australian weather data.

Closing Notes

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