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

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.

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[sudhansubal]

00:10 Understanding the math required to become a data professional
03:02 Progression from data analyst to data scientist
09:40 Understanding matrices, calculus, and discrete mathematics for machine learning.
13:32 Understanding measures of central tendency
19:28 Outliers influence the average, affecting the central tendency.
22:27 Understanding median and mode in statistics
28:09 Understanding mean, median, and mode in data analysis
30:37 Understanding standard deviation and variance in statistics
36:11 Understanding matrix multiplication.
39:02 Understanding differentiation and partial differentiation
44:15 Combinations and permutations are important in data science
47:00 Introduction to statistics and descriptive statistics
52:31 Descriptive statistics summarize complete data using key metrics.
55:15 Understanding the process of finding the average number of study hours for 12th class students in India.
1:00:20 Inferential statistics helps in approximating population average
1:02:54 Inferential statistics helps to approximate population parameters and conduct hypothesis testing.
1:09:22 Understanding numerical and categorical columns
1:12:26 Nominal and Ordinal Variables
1:18:30 Data can have endless possibilities within a range.
1:21:25 Descriptive statistics summarizes data in few statistics parameters
1:27:01 Average is the sum of all observations divided by the number of observations.
1:29:32 Observations on data distribution and outliers
1:35:14 Median is the exact center point of the data.
1:38:01 Mean and median describe the central point of the data, impacted by outliers
1:43:36 Measures of variation quantify how values are scattered around the center.
1:46:16 Scatter plot representation of student marks for two subjects
1:52:25 Explaining basic statistical measures
1:55:27 Range is not a reliable measure due to outliers.
2:01:08 Finding the average distance from the center in statistics.
2:03:45 Calculating variance involves taking the average of distance squared.
2:09:20 Understanding standard deviation and its implications.
2:12:03 Standard deviation cannot be used to compare variations among different series.
2:17:57 Using coefficient of variation to compare variation
2:20:46 Understanding variations and stability in product sales
2:25:50 Descriptive statistics help compare and analyze data to make informed decisions.
2:28:32 Impact of company announcements on stock prices
2:33:54 Random experiments and sample space
2:36:33 Probability is the ratio of favorable outcomes to the total outcomes in the sample space.
2:41:46 Probability depends on equally likely sample space
2:44:32 Rules of probability: Probability of each outcome in a sample space adds up to 1.
2:49:52 Probability of Union for Disjoint Events
2:52:24 Probability of union if not disjoint
2:57:50 Probability of Jack or heart
3:00:42 Calculating probability using set theory and rules
3:06:00 Basic principles of probability and types of events
3:08:33 The outcome of the first event does not influence the probability of the second event.
3:13:39 Gambler's fallacy and the law of large numbers in statistics
3:16:11 Understanding dependent events in probability.
3:21:59 Probability of dependent events
3:24:48 Calculating probabilities and understanding random variables in statistics
3:31:01 Random variables are either discrete or continuous
3:34:05 Random variables can be discrete or continuous
3:39:32 Understanding the probability of a random variable
3:42:17 Probability and expected value of winning and losing in a game.
3:47:41 Analyzing outcomes of a coin toss experiment and plotting the distribution.
3:50:31 Understanding probability mass function (PMF) for discrete random variables
3:55:55 Continuous random variables have endless possibilities.
3:58:37 Understanding probability density function (PDF)
4:03:45 The Bernoulli experiment has a binary outcome and is conducted for one trial.
4:06:44 Binomial experiment is a series of Bernoulli experiments with binary outcomes
4:12:25 Finding the probability of getting two orange balls in three trials.
4:14:56 Calculating the probability for a binomial experiment
4:20:17 Understanding binomial experiments and probabilities
4:22:57 Calculation of binomial probability of an experiment
4:28:12 Normal distribution is important in statistics and machine learning
4:30:49 Understanding standard normal distribution and its properties
4:35:47 Scaling data is about converting data into a common scale for comparison.
4:38:21 Understanding scaled data using min-max scaling
4:44:03 Understanding normalization and standardization in statistics
4:47:10 Z scaling standardizes data with mean 0 and standard deviation 1.
4:52:23 Understanding the distribution and standard normality
4:54:46 Understanding properties of normal distribution
5:00:01 Properties of Standard Deviation
5:02:57 Understanding standard normal distribution and probability calculations
5:08:33 Calculating probability for a data range
5:11:30 Using Z table to find probability above a score
5:16:56 Minimum marks to be in top 5%
5:19:48 Understanding skewness in distributions
5:25:01 Understanding normal distribution and its properties.
5:27:39 Finding the average internet recharge of complete Indians in last year is a challenging task.
5:33:29 Sampling is the process of collecting a smaller sample from a larger population.
5:36:22 Simple random sampling and central limit theorem explained.
5:41:32 Construct 95% confidence interval estimate for the average sample mean
5:44:09 Using Z-values to estimate population parameter range
5:49:35 Estimating the average rent in New York City using sample data
5:52:19 Introduction to hypothesis testing
5:57:42 Hypothesis testing involves formulating, testing, and making conclusions based on a problem statement.
6:00:36 Formulating null and alternate hypothesis for testing
6:06:20 Accepting alternate means being 100% sure of guilt, ignoring uncertainty
6:08:53 Testing if average amount is greater than 530 ml
6:14:08 Testing null and alternate hypothesis with examples
6:16:24 Introduction to Hypothesis Testing and Types of Tests
6:21:45 Using significance level 0.05 to test difference in milk content.
6:24:35 Testing the significance of a new average compared to the old average.
6:29:58 Calculating lower and upper values for a confidence interval
6:32:45 Determining acceptance and rejection of null hypothesis based on sample mean
6:38:00 Calculating lower and upper critical values using Z-score
6:40:41 Understanding one-tail tests
6:46:19 When to use a T Test
6:48:57 Different types of T Tests in statistics
6:54:27 T critical is calculated using the T table
6:57:19 Determining T critical value using degree of freedom
7:03:00 Hypothesis testing and T estimate calculation
7:05:50 Paired sample T Test compares before-and-after data of the same group, while independent sample T Test compares data from two different groups.
7:11:00 Finding T critical value for two-tail test
7:13:43 Independent sample T Test helps determine if two techniques are different.
7:19:29 Understanding type 1 and type 2 errors in hypothesis testing
7:22:30 Identifying type one and type two errors in hypothesis testing
7:28:21 Government has to decide on managing budget impacts or public satisfaction.
7:30:51 Understanding type 1 and type 2 errors in hypothesis testing
7:36:12 Alpha value impacts type one and type two errors

[vedpatil]

Best video lecture on stats, especially for those weak in subject. Thank you, scaler team and instructor.

[monsuratafolabi]

You are an amazing teacher. I might be needing this for grad school. Thank you so much

[jasdeepcheema]

Thank you so much for this valuable knowledge, you taught better than My college professor where I paid nearly $2000 for this course. It was beneficial, and easy to understand for a complete beginner like me. Thank you again :)

[ANGAYARKANNI_A]

Thank you Sumit Shukla sir. wonderful effort

[Himanshusingh-subj]

I hope this would be long so i can revise my complete syllabus of stats

[s.ponmani]

Hello sir, you are teaching the concept and ways are good, it is helpful to revise the topic. Could you tell best book to learn machine learning from scratch to clear the interview?

[manminder]

while solving problem of two-tailed hypothesis testing (6:41:05) where you dealt with average income of a sample of 140 persons, how does $34, 325 fall in the range of $34, 917.76 & $36, 082.24? it should be on the left of lower limit 34, 917.76 as 34, 325 is less than 34, 917.76. Please clarify

[JinniSong]

Best lec statics i watched a lot of lec but never had this clear understanding after studying

[varunjithava]

at 6:40:44 we have to reject null because Range (34917, 36082) and sample mean is 34325

[sweetie]

you did a great job in this video .very simple and explanatory.kudos

[ishdeepkaur]

Data analyst k liye kya itna hi statistics use hota hai?

[shubhamprasadkuiri]

But 34325 is out of the range (LV and UV i.e 34917 - 36082)...hence, the null must be rejected.

[priyamanik]

diff - is calculated Xi - mean not the other way around.

[sunoo_v]

at 5:19:43 where we find out the min marks to be in top 5% we have mean and st deviation. In order to be in top 5% we have to calculate the X value at 95% which is 2 SD then 527 + 2(112) = 751. 751 should be the min marks right ? Can anyone explain on this

[Kids_zone]

Please can you tell me which laptop should I buy for data science and ML

[ashutoshanshu]

Is this helpful for gate DA paper sir ?

[mannumannu]

32:41 what is degree of freedom? Please tell

[nazmulhudaroni]

Topics Covered:
Mathematical Foundations:

Matrices and Matrix Multiplication (9:40, 36:11)
Differentiation and Partial Differentiation (39:02)
Combinations and Permutations (44:15)
Descriptive and Inferential Statistics:

Central Tendency Measures (Mean, Median, Mode) (13:32, 22:27, 28:09, 35:14, 1:27:01)
Outliers and Their Influence (19:28, 1:29:32)
Standard Deviation and Variance (30:37, 2:09:20)
Coefficient of Variation (2:17:57)
Inferential Statistics and Hypothesis Testing (1:00:20, 1:02:54, 5:52:19)
Sampling and Central Limit Theorem (5:33:29, 5:36:22)
Probability and Distributions:

Basic Probability Concepts (2:33:54, 2:36:33)
Probability Rules and Events (2:41:46, 2:44:32, 3:00:42)
Random Variables (3:24:48, 3:31:01)
Probability Distributions (Bernoulli, Binomial, Normal, etc.) (4:03:45, 4:06:44, 4:28:12, 4:54:46)
Bayes’ Theorem (not explicitly mentioned, but usually related to topics around conditional probability)
Data Visualization and Interpretation:

Scatter Plots and Visual Representation of Data (1:46:16)
Understanding Skewness in Distributions (5:19:48)
Hypothesis Testing:

Formulating and Testing Hypotheses (5:52:19, 6:00:36)
Null and Alternate Hypothesis (6:06:20)
Z-Test and T-Test (6:16:24, 6:40:41, 6:46:19)
Type I and Type II Errors (7:19:29, 7:22:30)

[umermirza]

Please also make courses in linear algebra and calculus for data science in hindi