Data Analysis with Python
What you’ll learn
Regression and Correlation.
Introduction to Time Series.
And much more.
Basic training in mathematics and use of a web browser.
Knowledge of the Python language is desirable but not essential.
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.
Section 1: Summary
Lecture 1 Summary
Lecture 2 Other interesting courses
Lecture 3 Time Series Analysis Presentation
Section 2: Module 1
Lecture 4 Basic Concepts
Lecture 5 Anaconda Individual Edition Installation
Lecture 6 Lecture 4: Install Anaconda Suite on Windows 10
Section 3: Module 2
Lecture 7 Python data types – Part 1
Lecture 8 Python data types – Lab 1
Lecture 9 Python data types – Part 2
Lecture 10 Python data types – Lab 2
Lecture 11 Python data types – Part 3
Lecture 12 Python data types – Lab 3
Section 4: Module 3
Lecture 13 Quantitative Data Properties
Lecture 14 Quantitative Data Properties – Lab 4
Section 5: Module 4
Lecture 15 Pre-Processing Data in Python
Section 6: Module 5
Lecture 16 Exploratory Data Analysis. Part one
Section 7: Module 6
Lecture 17 Exploratory Data Analysis. Part Two
Section 8: Final Test
Section 9: Bonus One – Chi Square
Lecture 18 Chi-Square Test
Section 10: Bonus Two – Time Series
Lecture 19 What are Time Series?
Lecture 20 Time Series – Date and Time
Lecture 21 Transformation, Indexing and Resampling
Lecture 22 Time Series – Basic Calculations
Lecture 23 Time Series – Decomposition
Lecture 24 Next Level
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.
Udemy | English | 2h 7m | 722.98 MB
Created by: Christian Cisne