R for Data Science: Statistics Full Course for Beginners (2+ Hours)
Welcome to this comprehensive Statistics Full Course for Data Science using R Programming! In this 2.5-hour masterclass, you will learn everything from the fundamentals of data types to advanced inferential statistics and hypothesis testing.
Whether you are a beginner looking to enter the world of Data Science or a professional wanting to sharpen your R programming skills, this course provides a hands-on approach with real-world datasets, including stock prices and case studies.
What You Will Learn:
Data Fundamentals: Qualitative vs. Quantitative Data, Population, and Sampling.
Descriptive Statistics: Mean (Arithmetic, Geometric, Harmonic), Median, Mode, Quartiles, and Outliers.
Data Visualization: Mastering Bar Plots, Pie Charts, Histograms, and Box Plots in R.
Statistical Variation: Variance, Standard Deviation, Correlation, and Covariance.
Probability Distributions: Understanding Uniform and Normal Distributions.
Inferential Statistics: Hypothesis Testing, P-Values, T-Tests, and Chi-Square Tests.
Download the dataset used in the video: http://softlect.com/datasets/murders.csv
Download the dataset used in the video: http://softlect.com/datasets/GEStock.csv
Download the dataset used in the video: http://softlect.com/datasets/IBMStock...
Download the dataset used in the video: http://softlect.com/datasets/CocaCola...
Download the dataset used in the video: http://softlect.com/datasets/ZominosC...
Download the dataset used in the video: http://softlect.com/datasets/ZominosS...
Timestamps:
[00:00] Course Introduction: Why Data Analysis?
[03:15] Obtaining Data: Population and Sampling Strategies
[05:53] Classifying Data: Qualitative vs. Quantitative
[06:52] Overview of Statistical Measures (Mean, Regression, Skewness)
[09:13] Descriptive vs. Inferential Statistics
[10:51] Handling Qualitative Data in R (Factors & Vectors)
[16:22] Visualizing Qualitative Data (Bar Plots & Pie Charts)
[22:35] Handling Quantitative Data in R
[25:49] Visualizing Quantitative Data (Histograms, Box Plots, Strip Charts)
[31:53] Case Study: Analyzing Real-time Stock Prices
[38:12] Computing Mean types: Arithmetic, Geometric, and Harmonic
[44:24] Applications: Growth Rates & Financial Returns
[51:50] Measures of Central Tendency: Median and Mode
[56:43] Detecting and Handling Outliers
[01:01:09] Quartiles, Quantiles, and Data Distribution
[01:06:05] Measuring Variation: Variance & Standard Deviation
[01:13:55] Correlation and Covariance (Stock Price Relation)
[01:28:13] Bivariate Data Analysis (Two Variables)
[01:41:21] Multivariate Data Analysis in R
[01:50:01] Probability Distributions (Normal & Uniform)
[02:06:35] Statistical Hypothesis Testing & P-Value Significance
[02:12:40] Confidence Levels and Confidence Intervals
[02:15:01] T-Tests: One-Sample, Two-Sample, and Paired Tests
[02:24:42] Chi-Square Test for Independence
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