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Análisis y Visualización de Datos con Python

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Beginner

El plan de estudios del programa abarca una colección de cursos fundamentales que proporcionan los conocimientos básicos y las habilidades esenciales para un viaje exitoso en el campo de la ciencia de datos. Estos cursos abarcan el estudio exhaustivo de los conceptos clave, herramientas y metodologías integrales para el ámbito de análisis de datos y modelado. Mostrar más

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What you will learn

You will learn to master the NumPy library for high-performance numerical computing
You will gain proficiency in manipulating data using the Pandas library
You will understand the mathematical foundations essential for data analysis and modeling
You will explore the basics of probability theory and its application in data analysis
You will learn to visualize data using Python’s Matplotlib library
You will create sophisticated data visualizations with Seaborn
You will develop the skills to perform complex data transformations and aggregations
You will learn to clean, preprocess, and analyze large datasets efficiently
You will be able to apply statistical methods to derive insights from data

147 chapters
18 hours
135 tasks

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Learning track content

Module 1 / Ultimate NumPy

First of all, we will discuss the applications of NumPy and why it is popular. Afterwards you will learn various possible options to create different arrays in NumPy.

It's now time to learn how to use index notation to retrieve specific elements or subsets of data from arrays. You will also learn how to use indices to retrieve data based on certain conditions and handle missing values.

There are quite few of common operations on arrays which you may want to perform. Luckily, NumPy has built-in functions and methods for this purpose which you will learn here.

It's time to discover how to efficiently use mathematical operations on NumPy arrays and apply them in some real-world problems.

Module 2 / Primeros Pasos con Pandas

En esta sección, exploraremos los fundamentos de las estructuras Series y DataFrame. También aprenderá acerca de las distinciones entre estos dos tipos de estructuras.

Los datos pueden obtenerse en varios formatos, como CSV, JSON, SQL, HTML y más. Con Pandas, no está limitado a un único formato - puede trabajar con datos a través de una multitud de tipos de archivo. En este capítulo, nos centraremos específicamente en los formatos CSV y TXT.

Aquí aprenderá a procesar datos sin procesar eliminando la información superflua y gestionando los valores nulos de un conjunto de datos.

Module 3 / Advanced Techniques in pandas

This section will teach you how to output specific columns by their titles or indices. Also, you will get acquainted with the ways you can select rows by indices.

Here, you will learn how to extract data that has specific conditions. Also, you will learn how to combine them and even create your own.

In this section, you will expand your knowledge on setting different data conditions. You will learn to check if your data is in a defined list of values or between two values. You will also learn how to find the largest and smallest values.

This section is one of the most fascinating of the course. Here, you will learn how to group data in different ways. It will help you work as a data analyst to find out information on specific data groups.

This section is one of the most significant for a data analyst because if the data contains missing data values in the incorrect format, it will be impossible to work with. Thus, you will learn how to deal with such inappropriate values here.

Module 4 / Probability Theory Basics

We will start our way of learning probability theory by considering some basic definitions and rules: what is a stochastic experiment and random event, what is independence and incompatibility of events in the context of probability theory, what is the probability and how can we calculate probabilities of different elementary events.

In real-life tasks, we often have to deal with complex relationships and, as a result, calculate probabilities of several events or events that depend on each other. Let's consider how we can do this using probability theory.

To solve many real problems in probability theory, special models have been created that describe a particular situation. Let's consider some of the most used models that can be used to describe some discrete results of stochastic experiments.

What if the result of a stochastic experiment cannot be described by a discrete value? For this, models that work with continuous values are used. Consider the most popular of these models.

Often we are faced with the task of checking the dependence of the results of different stochastic experiments on each other. Moreover, it is necessary not only to assess the presence of dependencies but also to somehow quantify the degree of dependencies. To solve these problems, we can use covariance and correlation.

Module 5 / Ultimate Visualization with Python

First, we will discuss plotting applications, Matplotlib library, and its advantages. Afterwards you will create your first simple plot using Matplotlib.

Now you are ready to create various commonly used in practice plots. Without further ado, let's dive in right now!

Plotting and visualization is not only about creating a plot or several plots, it is also about making them clear, understandable and pleasant to look at. Time to learn plots customization in maplotlib, let's do it!

Statistics is essential in working with data, hence why it is extremely important to know how to create various statistical plots. Now it's time for us to explore the most common ones.

Seaborn is another powerful plotting library in Python built on top of matplotlib. Now that you are familiar with matplotlib, it's time to dive into visualization with seaborn.

Requirements

A computer with a browser - all browsers are supported.
Your enthusiasm to enhance your tech skills.
Everything else needed to start learning and practicing is already included in this course.

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