Aprenda los conceptos básicos de Python
Conceptos básicos de Python
Python es una gran herramienta que podemos usar para interactuar con nuestra computadora como un mouse. Cuando trabajamos con un mouse, necesitamos aprender la posición fÃsica de las diferentes opciones y caracterÃsticas para usar bien el programa. . Python es muy similar, en DataSimple creemos que debemos centrarnos en aprender a usar Python no como un cientÃfico informático sino como un cientÃfico de datos.
​
Aprender Python es una forma tan simple que solo estudiar funciones y bucles, la parte de hacer clic con el mouse, puede dificultar la transición al trabajo y la manipulación de datos, especialmente para aquellos que ya se sienten cómodos con las hojas de cálculo.
​
En los programas educativos de DataSimple aprendemos desde el principio desde la perspectiva de los datos. Trabajar con datos de la clase uno. Esto permite a los estudiantes una experiencia de aprendizaje más rápida y cómoda.
​
Todos los programas son compatibles con cuestionarios, proyectos de seguimiento guiados y presentaciones que cubren cada concepto para que aprenda y adquiera experiencia práctica con Python.
Python Data Analysis Bootcamp
BootCamp Content Map
DataSimple
Python Data Analysis
Bootcamp
Conceptos básicos de datos de Python 1
Tipos de datos
Introducción a los pandas
En nuestra primera lección de conceptos básicos de Python, cubrimos los conceptos básicos necesarios para trabajar con código Python. Los tipos de datos se vuelven increÃblemente importantes en el nivel de interacción con una computadora.
Data Analysis Guided Project 1
In our first follow-along guided project we explore the basics of working with data in Pandas and the concept of sampling.
Python Data Analysis BootCamp 2
Data Analysis - Understanding Distributions Class 2
Understanding our distribution is foundational to our understanding of our and is where we begin our bootcamp. Here we understand the types of distributions, how we define real world distributions and what insights we are trying to extract from each.
Data Analysis Guided Project 2
In our 2nd Python data analysis guided project
Python Data Analysis BootCamp 3
Data Anaysis Bivariate Analysis Class 3
​Correlation doesn’t imply causation and this is true but is very much a real world concept. In the world of our understanding the relationships in our data are of the utmost importance. The is especially true for the relationships with our target. Understand how we derive correlation including it’s imperfections and what it doesn’t tell you.
Data Analysis Guided Project 3
In our 3rd Python data analysis guided project
Python Data Analysis BootCamp 4
Data Analysis Hell Week 1 - Pandas Class 4
This week we take a deep dive into the many plotting and data visualation tools available in Pandas. Although we don’t have access to some things that are available in Seaborn and Plotly, Pandas plotting is very quick. And so often we want to use Pandas plot to quickly and easily gain understanding of our data.
Data Analysis Guided Project 4
In our 3rd Python data analysis guided project
Python Data Analysis BootCamp 5
Data Analysis Hell Week 2 - Seaborn Univariate Analysis Class 5
Seaborn is a power data analysis tool and although it’s worth using Seaborn just because of the beautiful data analysis it does easily. The reason we need to use Seaborn in our data analysis are the many tools, from the hue argument, to figure level plotting that allow for really deep analysis.
Data Analysis Guided Project 5
In our 3rd Python data analysis guided project
Python Data Analysis BootCamp 6
Data Analysis Workshop 1 - Extracting Business with Python Clss 6
Before we start our data analysis we should always be aware of what our goal is to stay targeted in our data analysis and not getting lost in the data. In this workshop we will go through the process of extracting business insights and then combining them into interesting marketing strategies.
Data Analysis Guided Project 6
In our 3rd Python data analysis guided project
Python Data Analysis BootCamp 7
Data Analysis Hell week 3 - Seaborn Bivariate Analysis
Understanding our interrelationships in our dataset are important for business insights or to help a machine learning engineer. Often our relationships are more complex than simply linear and Seaborn has many tools that allow us to inspect our deep into sub-groupings that hold unique interrelationships.
Data Analysis Guided Project 7
In our 7th Python data analysis guided project
Python Data Analysis BootCamp 8
Data Analysis Hacking Hypothesis Testing
Hypothesis testing in fundamentally important to understanding if our insights are valuable or simply random chance. We can use Python to simulate the concept and use central limit theorem to gain a better understand of a p value of .07 or .03 really mean in terms of the distribution.
Data Analysis Guided Project 8
In our 8th Python data analysis guided project
Python Data Analysis BootCamp 9
Data Analysis Hell Week 4 - Plotly Express
Plotly is a modern visualization library used in dashboard creation. This is quite coding intensize. Plotly Express to the rescue. Plotly Express allows quick and easy access to interactive plot. Plotly allows for a unique ability to extract insights around our distribution and with the 3D Scatter plot we can almost walk inside our data and gain an unparalleled understanding of our data.
Data Analysis Guided Project 9
In our 9th Python data analysis guided project
Python Data Analysis BootCamp 10
Data Analysis Hacking Hypothesis Testing
Although there are many similarities in the insights we will focus on extracting from our data in collecting business insights. Are are many different things we need to pay attention to properly support a data scientist. This workshop will focus on data analysis techniques that will support the development of a machine learning model.
Data Analysis Guided Project 10
In our 10th Python data analysis guided project