In this video, Sumit Shukla (DSML Educator), is explaining everything about Statistics for Data Science. This video covers everything from beginners to advanced level. Check out free masterclasses by industry-leading experts: https://www.scaler.com/events?utm_source=Youtube&utm_medium=osocial&utm_campaign=brand_scaler_events_osocial_youtube_statistics-full-course-by-sumit-shukla-dec-23&utm_content=YTDescription
Topics Covered
00:00:00 - Introduction
00:14:25 - Measures of Central Tendency
00:25:35 - Measures of Dispersion
00:41:57 - Combinations
00:45:08 - Permutations
01:21:40 - Descriptive Statistics
01:47:58 - Measures of Variables
02:45:00 - Rules of Probability
03:58:37 - Probability Density Function (PDF)
4:20:17 - Binomial Experiments and Probabilities
4:44:03 - Normalization and Standardization
5:00:01 - Standard Deviation
5:25:01 - Normal distribution and its properties
5:52:19 - Introduction to Hypothesis Testing
6:16:24 - Hypothesis Testing and Types of Tests
6:48:57 - Different types of T-tests in statistics
What is statistics?
Statistics is a branch of mathematics and science that involves collecting, organizing, analyzing, interpreting, and presenting data. It is used to gain insights, make informed decisions, and draw conclusions about various phenomena.
What is probability?
Probability is a measure of the likelihood of an event occurring. It is expressed as a number between 0 and 1, with 0 indicating impossibility and 1 indicating certainty. Probability theory is used to quantify uncertainty and randomness in various fields, such as mathematics, science, and statistics.
What is hypothesis testing?
Hypothesis testing is a statistical method used to make inferences about population parameters based on a sample of data. It involves formulating a null hypothesis and an alternative hypothesis, collecting data, and using statistical tests to determine if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.
What are the types of tests?
There are various types of tests in statistics, including:
a. Hypothesis Tests: Used to make inferences about population parameters.
b. T-Tests: Assess differences between means of two groups.
c. Chi-Square Tests: Analyze the association between categorical variables.
d. ANOVA (Analysis of Variance): Compares means of multiple groups.
e. Regression Analysis: Examines relationships between variables.
f. Non-parametric Tests: Statistically analyze data when assumptions of parametric tests are not met.
What is a random variable?
A random variable is a variable in probability theory that can take on different values with certain probabilities. It represents the outcomes of a random process or experiment. Random variables can be discrete (with countable outcomes) or continuous (with an infinite number of possible values).
What is distribution?
In statistics, a distribution refers to the set of all possible values and their associated probabilities or frequencies for a random variable. It describes how the values of a random variable are spread or distributed. Common probability distributions include the normal distribution, binomial distribution, and Poisson distribution, each with specific characteristics and applications.
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