Welcome to the Statistics course! Throughout this course, you will learn essential statistical tools that are widely used in data analysis and decision-making processes.
This course consists of the following modules:
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Data Preprocessing: Explore the steps involved in data preprocessing.
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Frequency Distribution: Learn how to create and analyze frequency distributions to summarize categorical and numerical data.
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Measure of Central Tendency: Understand various measures of central tendency, such as mean, median, and mode, to describe the central value of a dataset.
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Measure of Shape: Explore different measures of shape, like skewness and kurtosis, to understand the distribution of data.
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Measure of Dispersion: Learn about measures of dispersion, such as range, variance, and standard deviation, to assess the spread of data.
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Inferential Statistics: Dive into inferential statistics, covering topics like sampling techniques, the central limit theorem, confidence intervals, hypothesis testing (Z-test and T-test), Chi-Square test, one-way ANOVA, and two-way ANOVA.
Before you begin the course, ensure you have the following:
- Basic understanding of mathematics and algebra.
- Familiarity with programming fundamentals, preferably using Python.
If you have any questions or encounter any challenges while working through the course content or code files, feel free to reach out to us through linkedin[www.linkedin.com/in/sakshi-singhal-bb0b3213a].