Statistics Full Course For Beginners | Statistics For Data Science | Machine Learning @SCALER

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: Topics Covered 00:00:00-Introduction 00:00:00-How much Math you need to become a Data Professional? 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 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. #datascience #softwareengineering #scaler ______________________________________________________________________________ About SCALER: A transformative tech school, creating talent with impeccable skills. Upskill and Create Impact. Learn more about Scaler: 📌 Follow us on Social and be a part of an amazing tech community📌 👉 Meet like-minded coder folks on Discord - 👉 Tweets you cannot afford to miss out on - 👉 Check out student success stories, expert opinions, and live classes on Linkedin - 👉 Explore value-packed reels, carousels and get access to exclusive updates on Instagram - 📢 Be a part of our one of a kind telegram community: 🔔 Hit that bell icon to get notified of all our new videos 🔔 If you liked this video, please don’t forget to like and comment. Never miss out on our exclusive videos to help boost your coding career! Subscribe to Scaler now!