Statistics 2023 AZ For Data Science with Both Python R
What you’ll learn
Statistics
Data Analysis
Business Analytics
Regression Analysis
Descriptive Statistics
Inferential Statistics
Hypothesis Testing
T-Test
Chi Square Test
AnOVa
Linear Regression
Logistic Regression
Machine Learning
Data Science
Requirements
Knowledge Of Basic Python and R
Motivation to Learn
Description
Data Science and Analytics is a highly rewarding career that allows you to solve some of the world’s most interesting problems and Statistics the base for all the analysis and Machine Learning models. This makes statistics a necessary part of the learning curve. Analytics without Statistics is baseless and can anytime go in the wrong direction.For a majority of Analytics professionals and Beginners, Statistics comes as the most intimidating, doubtful topic, which is the reason why we have created this course for those looking forward to learn Statistics and apply various statistical methods for analysis with the most elaborate explanations and examples!This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.This course provides Full-fledged knowledge of Statistics, we cover it all.Our exotic journey will include the concepts of:1. What’s and Why’s of Statistics – Understanding the need for Statistics, difference between Population and Samples, various Sampling Techniques.2. Descriptive Statistics will include the Measures Of central tendency – Mean, Median, Mode and the Measures of Variability – Variance, SD, IQR, Bessel’s Correction3. Further you will learn about the Shapes Of distribution – Bell Curve, Kurtosis, Skewness.4. You will learn about various types of variables, their interactions like Correlation, Covariance, Collinearity, Multicollinearity, feature creation and selection.5. As part of Inferential statistics, you will learn various Estimation Techniques, Properties of Normal Curve, Central Limit Theorem calculation and representation of Z Score and Confidence Intervals.6. In Hypothesis Testing you will learn how to formulate a Null Hypothesis and the corresponding Alternate Hypothesis.7. You will learn how to choose and perform various hypothesis tests like Z – test, One Sample T Test, Independent T Test, Paired T Test, Chi Square – Goodness Of Fit, Chi-Square Test for Independence, ANOVA8. In regression Analysis you will learn about end-to-end variable creation selection data transformation, model building and Evaluation process for both Linear and Logistic Regression.9. In-depth explanation for Statistical Methods with all the real-life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course.10. All explanations provided in a simple language to make it easy to understand and work on in future.11. Hands-on practice on more than 15 different Datasets to give you a quick start and learning advantage of working on different datasets and problems.
Overview
Section 1: Introduction to Course
Lecture 1 Introduction
Section 2: Descriptive Statistics Explained
Lecture 2 Introduction to Statistics_Population & Sampling
Lecture 3 Measure Of Central Tendencies Mean Median Mode
Lecture 4 Measure Of Variability – Variance Standard Deviation IQR
Lecture 5 Data Diatributions Correlation & Covariance
Lecture 6 Practice Questions: Descriptive Statistics
Section 3: Intro to Inferential Statistics
Lecture 7 Intro to Inferential Statistics
Lecture 8 Variable Types
Section 4: Inferential Statistics: Central Limit Theorem,Z-Score,Confidence Interval
Lecture 9 Central Limit Theorem
Lecture 10 Z-Score
Lecture 11 Confidence Interval
Lecture 12 CI examples
Section 5: Hypothesis Testing
Lecture 13 Hypothesis Testing Introduction
Lecture 14 Hypothesis Testing Theory Explained
Lecture 15 Type of Errors and Significant Difference
Section 6: T-test Family
Lecture 16 One Sample, Independent, Paired T Test
Section 7: Chi-Square Tests
Lecture 17 Chi Square test of Goodness of Fit
Lecture 18 Chi Square test of Independance
Section 8: ANOVA
Lecture 19 ANOVA
Lecture 20 Which test to pick?
Lecture 21 Statistics Using Graphpad
Section 9: Practice Questions in Python: Descriptive and Inferential Statistics
Lecture 22 Z-Score questions
Lecture 23 T-tests questions
Lecture 24 Chi Test, Anova, Cov, Correlation questions
Section 10: Statistics using Python – Case Studies
Lecture 25 House Prices Dataset – Case Study -1
Lecture 26 City Payroll Dataset – Case Study -2
Section 11: Descriptive Statistics Using R -Practice
Lecture 27 Descriptive Statistics using R Practice Questions
Section 12: Inferential Statistics Using R – Practice
Lecture 28 Inferential Statistics Using R Practice Questions
Section 13: Statistics using R – Case Studies
Lecture 29 Census Income Dataset – Case Study -1
Section 14: Linear Regression Analysis using Python
Lecture 30 Regression Analysis Explained – Linear Regression
Lecture 31 Linear Regression Cost, Gradient and Cross Validation
Lecture 32 Linear Regression from scratch
Lecture 33 Linear Regression Regularization
Section 15: Logistic Regression Analysis using Python
Lecture 34 Logistic Regression Introduction
Lecture 35 06_Logistic Regression_Mathematics
Lecture 36 07 Logistic Regression Metrics
Lecture 37 Logistic Regression Implementation
Section 16: Linear Regression Analysis using R
Lecture 38 Linear Regression Analysis using R
Section 17: Logistic Regression Analysis using R
Lecture 39 Logistic Regression Analysis using R
Beginner,Intermediate,Advanced
Course Information:
Udemy | English | 15h 18m | 7.96 GB
Created by: MG Analytics
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