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Keep shoppingData Analysis with Python
5,464 students
Data Analysis with Python
Statistics introduction applied to data science. Focus on Exploratory Data Analysis (EDA).
Created byChristian Cisne
Last updated 4/2024
English
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.
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Coding Exercises
This course includes our updated coding exercises so you can practice your skills as you learn.
See a demo
Course content
10 sections • 28 lectures • 2h 22m total length
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.
Who this course is for:
- 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.