# Introduction to Statistics with Python

Where Statistics and Programming Meet

This course provides a basic introduction to statistics and the use of Python a popular programming language. During the course, we look at many fundamental ideas in statistics within the framework of analysis in Jupyter Notebook a commonly used integrated development environment for Python. Topics include both descriptive and inferential statistics. Concepts related to data visualization will also be discussed to empower you in your journey as a data analyst.  Among the concepts shared include mean, median, mode, standard deviation, histograms, confidence intervals,  t-test, correlation, regression, ANOVA, chi-square, and more. All of these ideas can be learned from the comfort of your own home without the hassle of having to go to class. Quizzes are also available to assess your progress along with the actual Python that is used in the videos.

What you’ll learn

• Basic statistical analysis.
• How to utilize the Jupyter Notebook.
• Basic of the Python language for statistical analysis.
• Data visualization.

Course Content

• What is statistics –> 4 lectures • 30min.
• How do You Use Python –> 4 lectures • 26min.
• How do You Visualize Numbers? –> 5 lectures • 38min.
• What are Measures of Central Tendency? –> 4 lectures • 20min.
• What are Measures of Dispersion? –> 8 lectures • 57min.
• What is Probability? –> 3 lectures • 22min.
• What is Normal Distribution? –> 3 lectures • 31min.
• What are Confidence Intervals? –> 4 lectures • 36min.
• What is Hypothesis Testing? –> 5 lectures • 46min.
• What is Two Sample Hypothesis Testing? –> 6 lectures • 50min.
• What is Analysis of Variance? –> 3 lectures • 23min.
• What is Correlation and Regression? –> 6 lectures • 57min.
• What is Chi-Square? –> 4 lectures • 27min.

Requirements

• No prior coding experience required.
• No prior statistical experience required.

This course provides a basic introduction to statistics and the use of Python a popular programming language. During the course, we look at many fundamental ideas in statistics within the framework of analysis in Jupyter Notebook a commonly used integrated development environment for Python. Topics include both descriptive and inferential statistics. Concepts related to data visualization will also be discussed to empower you in your journey as a data analyst.  Among the concepts shared include mean, median, mode, standard deviation, histograms, confidence intervals,  t-test, correlation, regression, ANOVA, chi-square, and more. All of these ideas can be learned from the comfort of your own home without the hassle of having to go to class. Quizzes are also available to assess your progress along with the actual Python that is used in the videos.

For students and those who want exposure to statistical analysis while also obtaining a basic insight into some tools involved in data science, this is a course for you. With the growth in demand for quantitative skills with the rise of Big Data, developing mastery of statistics and analytical tools such as Jupyter Notebook is becoming a norm.

Come and be a part of the analytics experience by enrolling in a course that will prepare you for the demands of the mid 20th century.

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