What you'll learn
The basics of descriptive statistics (which allows you to summarize information)
Data visualization (which allows data to be represented graphically)
Traps to avoid (when analyzing data and which it is easy to fall into)
Data analysis in the business environment
Requirements
There are no prerequisites for this course.
Description
If you want to master the fundamentals of Data Analysis, this course is perfect for you!To interpret a massive amount of data that nobody understands, you need to know the basics of data analysis.My name is Louis, and I have been a data analyst for several years now.In this course, we will review all the fundamental concepts to analyze data.This course is designed for three categories of people:Firstly, data analysts who are already working in companies and would like to further master the fundamentals.Secondly, students who have taken statistics courses and would like to explore more practical cases.And finally, the curious ones who are interested in a deep understanding of data.In this course, we will cover:The basics of descriptive statistics (which allows us to summarize information).Data visualization (which allows us to represent data graphically).Pitfalls to avoid when analyzing data and common mistakes to be aware of.Finally, we will talk about data analysis in business.For those without technical skills, don't worry, this course is designed to be accessible to all levels, without requiring any prior knowledge of programming or the use of specific tools.The objective of this course is for you to truly understand the fundamentals of data analysis.You will be guided through your learning in a dynamic environment.This course aims to be clear and well-structured. You will find:Theoretical concepts explained progressively.Many examples to illustrate the theory.Regular reminders to make connections between sections.If you want to master the fundamentals of data analysis, this course is perfect for you!Are you ready to begin? See you very soon!
Overview
Section 1: Introduction
Lecture 1 Structure of the course
Section 2: The basics of Descriptive Statistics
Lecture 2 Definition of Data Analysis
Lecture 3 Data Tables, Database and Views
Lecture 4 Definition of Descriptive Statistics
Lecture 5 Vocabulary of Descriptive Statistics
Lecture 6 Frequency and Percentage
Lecture 7 The mode
Lecture 8 The mean
Lecture 9 The median
Lecture 10 The quartiles
Lecture 11 The range and the interquartile range
Lecture 12 The variance and the standard deviation
Lecture 13 Measures of skewness
Lecture 14 Measures of kurtosis
Lecture 15 The correlation
Section 3: Data Visualization
Lecture 16 Definition of Data Visualization
Lecture 17 Bar charts and histograms
Lecture 18 Box plots
Lecture 19 Line charts
Lecture 20 Stacked area charts
Lecture 21 Pie charts
Lecture 22 Scatter plots
Lecture 23 Sankey diagrams
Lecture 24 The correlation matrix
Lecture 25 Other examples of data visualization
Section 4: Pitfalls to avoid
Lecture 26 How to lie with Statistics
Lecture 27 Correlation does not imply causation
Lecture 28 The dangers of the mean
Lecture 29 The trap of seasonality
Lecture 30 The misleading axis
Lecture 31 Sample size too small
Lecture 32 The selection bias
Lecture 33 The trap of one dimensional analysis
Lecture 34 The Simpson's paradox
Section 5: Data Analysis in Business
Lecture 35 The corporate Data Analysis "jargon"
Lecture 36 The best tools for data analysis
Lecture 37 AB testing
Lecture 38 Qualitative study
Lecture 39 Do I need to be good at maths ? at stats ? at coding ? at AI ?
Data analysts who already work in a company and who would like to master the fundamentals more,Students who have taken statistics courses and would like to go further by exploring more practical cases,The curious who are interested in an in-depth understanding of data

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