ANALYTICS |
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Descriptive, Predictive and Prescriptive Analytics Explained Click here |
The Analytics Journey An IBM view of the structured data analysis landscape: descriptive, predictive and prescriptive analytics. Click here |
16 Data Mining Techniques: The Complete List Click here |
7 Fundamental Steps to Complete a Data Analytics Project Click here |
Mean, Median, Mode, Range Click here |
REGRESSION |
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Linear Regression, Clearly Explained Click here |
‘Interactive’ Regression Click here |
Ordinary Least Squares Regression Click here |
Regression Models Click here |
TYPE I & II ERRORS |
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Type I & Type II Errors | Differences, Examples, Visualizations Click here |
Introduction to Type I and Type II errors (VIDEO) Click here |
Null and Alternative Hypotheses Definitions & Examples Click here |
PYTHON |
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Python for OSINT Investigations Click here |
GGPLOT2 (tidyverse) |
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Introduction to data visualization with ggplot2 Click here |
Data visualization with ggplot2 CHEAT SHEET Click here |
Introduction to Graphing Click here |
PROBABILITY |
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Probability Explained Click here |
Monty Hall Problem (@Numberphile) Click here |
Standard deviation (BBC Bitesize) Click here |
R PROGRAM LANGUAGE |
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R Studio Click here |
The Big Book of R Click here |
Markdown Basic Elements Click here |
Understanding R programming over Excel for Data Analysis Click here |
R Basics: Quick and Easy Click here |
R Markdown: The Definitive Guide Click here |
R for Graduate Students Y. Wendy Huynh Click here |
Introduction to Hypothesis Testing in R – Learn every concept from Scratch! Click here |
SQL |
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SQLBolt – Learn SQL with simple, interactive exercises. Click here |
SQL Exercises, Practice, Solution Click here |
SQL Cheat Sheet Click here |
SQL Commands Cheat Sheet – How to Learn SQL in 10 Minutes Click here |