Statistics for Data Analysis Using Python

Learn Python from Basics • Descriptive, Inferential Statistics • Plots for Data Visualization • Data Science

4.65 (1400 reviews)

Udemy

platform

English

language

Data & Analytics

What you will learn

Learn Python from the basics with no prior knowledge required, making this course accessible to everyone.

Understand statistics from the ground up, with no prior knowledge needed, ensuring a solid foundation in both Python and statistics.

Start with basic statistical concepts and progressively apply these concepts using Python for a comprehensive learning experience.

Enjoy a balanced combination of theory and practice, enhancing your understanding and application of statistical methods.

Master descriptive statistics, including mean, mode, median, standard deviation, variance, and interquartile range, using Python.

Dive into inferential statistics with one and two-sample z-tests, t-tests, Chi-Square tests, F-tests, ANOVA, and more, gaining practical skills.

Explore various probability distributions, such as normal, binomial, and Poisson, and learn to implement these in Python.

Understand how to visualize data effectively using Python libraries, creating insightful graphs and charts.

Enhance your resume with valuable skills in Python and statistics, making you a competitive candidate in data-driven fields.

Gain confidence in your ability to perform statistical analyses and interpret results using Python, boosting your career prospects.

Course Gallery

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Comidoc Review

Our Verdict

This Statistics for Data Analysis Using Python course serves as an excellent resource for learners seeking solid foundations in both theoretical and practical aspects of statistics. The course incorporates real-world examples and covers a wide array of statistical tests while gradually guiding students through their implementation using the Python programming language. Although starting slowly, the course progresses to more complex topics, ensuring students are well-prepared for data analysis tasks ahead. However, be prepared for heavy theoretical content, with little focus on Python development or advanced regression analysis methods.

What We Liked

The course strikes an optimal balance between theoretical statistics concepts and their practical implementation using Python.
Complex statistical methods, such as ANOVA, are broken down into easy-to-understand steps with clear explanations and examples.
Real-world scenarios are used to demonstrate the importance of data preparation for advanced statistical tests.
Instructor provides a GitHub repository for future reference, covering topics like one & two-sample z-tests, t-tests, Chi-Square tests, F-tests, ANOVA, and probability distributions.

Potential Drawbacks

The course starts with very basic content which may be too simple for some learners, such as downloading Anaconda.
The heavy emphasis on theory might not appeal to students interested in Python development specifically.
There is no coverage of regression analysis or non-parametric statistical tests in this course.
Some users might find the instructor's thick accent challenging to understand at first, although adaptation occurs over time.