• views: 139
• date: 12 March 2025
• posted by: voska89

What you'll learn

Better understanding of key concepts and properties in statistics
Understand the connection of key concepts with real life scenarios
Learn basic probability concepts used widely in Data science, Analytics, medicinal researches and other imprtant fields
Learn Descriptive and Inferential Statistics and it's real life application
Learn everything from Probabiity to Hypothesis testing and different analysis

Requirements

Anyone who is eager to learn Statistics with real life applications
Anyone who is looking forward to expand the horizons of understanding of statistics
No prior knowledge of Statistics required

Description

Master the Fundamentals of Statistics and Elevate Your Data Analysis Skills!Are you looking to build a strong foundation in statistics for data science, analytics, and decision-making? This beginner-to-intermediate level course is designed to simplify complex statistical concepts with real-world applications, helping you gain confidence in data analysis, hypothesis testing, and regression modeling.What You'll Learn: Module 1: Introduction to Statistics – Understand the role of statistics in data science types of datakey distinctions between descriptive & inferential statistics.Module 2: Descriptive Statistics – Learn about measures of central tendency (mean, median, mode)Measure of dispersion (range, variance, standard deviation)visualize data using histograms, box plots, and scatter plots.Module 3: Probability Basics – Master probability conceptsconditional probabilityBayes' theorem Module 4: Probability Distributions – Understanding Discrete and Continuous Probability distributions Binomial DistributionPoisson DistributionNormal DistributionExponential DistributionCentral Limit Theorem(CLT)Module 5: Inferential Statistics – Dive into sampling techniquesWhat is confidence intervalshypothesis testing Types of errorst-tests & z-testsUnderstanding chi-square testModule 6: Regression & Correlation – ( Coming Soon)Explore correlation analysis, simple & multiple linear regression, R-squared value, and the line of best fit to make data-driven predictions. Who Is This Course For? Aspiring Data Scientists, Business Analysts, and ResearchersMBA & Analytics Students looking to strengthen their statistical foundationStudents & professionals in Finance, Marketing, and Business Strategy Anyone looking to interpret and analyze data effectively Why Take This Course?Beginner-friendly explanations with real-world examplesNo prior advanced math required—concepts explained intuitivelyHands-on learning with statistical tools and visualization techniquesEssential for careers in data-driven decision-makingBy the end of this course, you'll be able to confidently apply statistical techniques to analyze data, test hypotheses, and build predictive models—a must-have skill set for any data professional!Ready to turn data into actionable insights? Enroll now and master statistics for real-world decision-making!

Overview

Section 1: Introduction to Statistics
Lecture 1 What is Statistics?
Lecture 2 Types of Data
Lecture 3 What is Descriptive Statistics and Inferential Statistics?
Section 2: Descriptive Statistics
Lecture 4 Measures of Central Tendancy - Mean, Median and Mode
Lecture 5 Measures of Dispersion
Lecture 6 Understanding Standard Deviation
Lecture 7 What is Co-efficient of Variation?
Lecture 8 What are Quartiles and Percentiles?
Lecture 9 Data Visualization with Graphs
Section 3: Probability Basics
Lecture 10 Introduction to Probability
Lecture 11 Conditional Probability
Lecture 12 Bayes' Theorem
Section 4: Probability Distributions
Lecture 13 Introduction to Probability Distributions
Lecture 14 Binomial Distribution
Lecture 15 Poisson Distribution
Lecture 16 Normal Distribution
Lecture 17 Exponential Distribution
Lecture 18 What is Central Limit Theorem?
Section 5: Inferential Statistics
Lecture 19 Sampling and Sampling techniques
Lecture 20 What is Confidence Interval?
Lecture 21 What is Hypothesis Testing?
Lecture 22 Types of errors in Hypothesis testing.
Lecture 23 Understanding T tests and Z tests
Lecture 24 Understanding Chi-Square Test
Lecture 25 Chi-Square Example solving on Excel
Students planning to get into Data Science and Analytics,Students of Psychology who are interested in building strong foundation in statistics,Analytics or Data Science professionals who are interested in improving statistical understanding,Beginners of stastitics planning to get into further studies


Homepage:

https://www.udemy.com/course/statistics-for-data-science-business-analytics/

   
   
   

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