What you'll 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.
Perform simple or complex statistical calculations using Python! - You don't need to be a programmer for this :)
You are not expected to have any prior knowledge of Python. I will start with the basics. Coding exercises are provided to test your learnings.
The course not only explains, how to conduct statistical tests using Python but also explains in detail, how to perform these using a calculator (as if, it was the 1960s). This will help you in gaining the real intuition behind these tests.
Learn statistics, and apply these concepts in your workplace using Python.
The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concepts. Various examples and data-sets are used to explain the application.
I will explain the basic theory first, and then I will show you how to use Python to perform these calculations.
The following areas of statistics are covered:
Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation.
Data Visualization - Commonly used plots such as Histogram, Box and Whisker Plot and Scatter Plot, using the Matplotlib.pyplot and Seaborn libraries.
Probability - Basic Concepts, Permutations, Combinations
Population and Sampling - Basic concepts
Probability Distributions - Normal, Binomial and Poisson Distributions
Hypothesis Testing - One Sample and Two Samples - z Test, t-Test, F Test and Chi-Square Test
ANOVA - Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using Python.
The Goodness of Fit and the Contingency Tables.
