Course presentation

In this course, we are going to focus on Exploratory Data Analysis techniques for machine learning projects.


What is EDA?

Exploratory Data Analysis (EDA) is the first approach to a dataset before applying any transformation or model. It's able to make us analyze a dataset just as it is, graphically exploring the correlation between the features and their predictive power. In fact, EDA makes use of data visualization concepts to extract the information hidden inside data.


Why is EDA useful?

EDA helps us explore the information hidden inside a dataset before applying any model or algorithm. So, it's bias-free. Moreover, it lets us figure out whether our features have a predictive power or not, so if the machine learning project we are working on has chances to be successful. Without EDA, we may give the wrong data to a model without reaching any success.


Is EDA a good skill to have?

Definitely yes. The results of a good EDA can be a deliverable by themselves and can let managers understand information before spending time and money on models and algorithms. EDA makes data scientists do their original job: extract business information from data.


What will I learn with this course?

Completing this course you will learn the basic principles of Exploratory Data Analysis, including pair plots, histograms, conditional histograms, some powerful Python libraries and other visualization tools. Every lesson is made by a practical example in Python programming language using Jupyter notebooks.

What people say about this course


Karunamoorthy S

⭐⭐⭐⭐⭐

A very good EDA lecture with short and sweet examples for beginners


Veronica (translated from Italian)

⭐⭐⭐⭐⭐

Very clear in the explanation, correct and understandable English, lessons wisely studied in content and form, which do not neglect the details. Advised!


Martin Repa

⭐⭐⭐⭐⭐

Simple, understandable, perfect


Mayra Dalence Limachi (translated from Spanish)

⭐⭐⭐⭐⭐

It is an ideal course to familiarize yourself with the python programming language. Exceed my expectations, I recommend it!

Course contents


  Introduction
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  Univariate analysis
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  Multivariate analysis
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  Some useful libraries
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  General guidelines
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  End of course
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Join the course

Who is this course for?


This course has been made for:

  • aspiring, junior and senior data scientists
  • machine learning engineers
  • data analysts
  • researchers
  • students
  • everybody who is interested in machine learning and data science


Prerequisites


In order to benefit from the course, it's useful to have this previous knowledge:

  • Python programming language
  • Numpy library
  • Pandas library


What will I get once I join the course?


After you buy the course, you'll have complete access to the video lessons and you'll be able to start discussions with the teacher and the other students using the comment section under every lesson.

Your Instructor


My name is Gianluca Malato, I'm Italian and have a Master's Degree cum laude in Theoretical Physics of disordered systems at "La Sapienza" University of Rome.

I'm a Data Scientist who has been working for years in the banking and insurance sector. I have extensive experience in software programming and project management and I have been dealing with data analysis and machine learning in the corporate environment for several years.

I am also skilled in data analysis (e.g. relational databases and SQL language), numerical algorithms (e.g. ODE integration, optimization algorithtms) and simulation (e.g. Monte Carlo techniques).

I've written many articles about Machine Learning, R and Python and I've been a Top Writer on Medium.com in Artificial Intelligence category.

Frequently Asked Questions

Does the course have a start and a finish date?

No. Once you enroll, you can follow the recorded video lessons when you want.


How can I pay for the course?

You can pay with a credit card using Teachable's payment gateway or with Paypal.


How can I follow the lessons?

Once you pay for your enrollment, you can access the recorded video lessons of the course when you want from your computer using this website. These videos are given in streaming, so you'll need to connect to this website and have an Internet connection in order to watch them. After you create your account and log in, you can use the My Courses link in top of every page to see all the courses you have enrolled in.


What language will be used?

During this course, the spoken and written language is English.


What if I'm not satisfied?

If you are not satisfied with the course, we apply a 30-day refund policy. Just contact us within 30 days from the date of purchase to get a full refund.

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