Data Analysis With Python 2022

Statistics introduction applied to data science. Focus on Exploratory Data Analysis (EDA).

What you'll learn
Descriptive Statistics.
Pivot Table.
HeatMap.
Histograms.
Box-Plot.
Regression and Correlation.
Anova.
Chi-Square.
Introduction to Time Series.
And much more.

Requirements
Basic training in mathematics and use of a web browser.
Knowledge of the Python language is desirable but not essential.

Description
Do you need help with statistics?. In this course we will learn the basic statistical techniques to perform an Exploratory Data Analysis in a professional way. Data analysis is a broad and multidisciplinary concept. With this course, you will learn to take your first steps in the world of data analysis. It combines both theory and practice.The course begins by explaining basic concepts about data and its properties. Univariate measures as measures of central tendency and dispersion. And it ends with more advanced applications like regression, correlation, analysis of variance, and other important statistical techniques.You can review the first lessons that I have published totally free for you and you can evaluate the content of the course in detail.We use Python Jupyter Notebooks as a technology tool of support. Knowledge of the Python language is desirable, but not essential, since during the course the necessary knowledge to carry out the labs and exercises will be provided.If you need improve your statistics ability, this course is for you.if you are interested in learning or improving your skills in data analysis, this course is for you.If you are a student interested in learning data analysis, this course is for you too.This course, have six modules, and six laboratories for practices.Module one. We will look at the most basic topics of the course.Module two. We will see some data types that we will use in python language.Module three. We will see some of the main properties of quantitative data.Module four. We will see what data preprocessing is, using the python language.Module five. We will begin with basics, of exploratory data analysis.Module six. We will see more advanced topics, of exploratory data analysis.

Overview
Section 1: Summary

Lecture 1 Summary

Section 2: Module 1

Lecture 2 Basic Concepts

Lecture 3 Anaconda Individual Edition Installation

Lecture 4 Lecture 4: Install Anaconda Suite on Windows 10

Section 3: Module 2

Lecture 5 Python data types - Part 1

Lecture 6 Python data types - Lab 1

Lecture 7 Python data types - Part 2

Lecture 8 Python data types - Lab 2

Lecture 9 Python data types - Part 3

Lecture 10 Python data types - Lab 3

Section 4: Module 3

Lecture 11 Quantitative Data Properties

Lecture 12 Quantitative Data Properties - Lab 4

Section 5: Module 4

Lecture 13 Pre-Processing Data in Python

Section 6: Module 5

Lecture 14 Exploratory Data Analysis. Part one

Section 7: Module 6

Lecture 15 Exploratory Data Analysis. Part Two

Section 8: Final Test

Section 9: Bonus One - Chi Square

Lecture 16 Chi-Square Test

Section 10: Bonus Two - Time Series

Lecture 17 What are Time Series?

Lecture 18 Time Series - Date and Time

Lecture 19 Transformation, Indexing and Resampling

Lecture 20 Time Series - Basic Calculations

Lecture 21 Time Series - Decomposition

Students and professionals who wish to acquire or improve their skills in data analysis through statistical techniques.,Python developers who want to improve their skills using statistical techniques.,Data analysts.,Beginning python developers interested in data science.

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