DatosConsejos simples para el análisis de datos
Explora en detalle Pandas,nacido en el mar, Yellowbrick, Plotly y Shap, Aprenda cómo hacer hermosos gráficos y cómo extraer información de su análisis de datos. Un analista de datos necesita proporcionar información a los socios comerciales y a un ingeniero de aprendizaje automático. Los conocimientos necesarios pueden ser muy diferentes y la comprensión de los datos se utilizará de diferentes maneras. Perfeccionemos nuestras habilidades de análisis de datos de Python y parcelas connacido en el mar, Pandas, Plotly y Shap.
Consejos para el análisis de datos de Seaborn en Python
Sumérjase en el análisis de datos con Seaborn. La biblioteca de Python crea hermosos gráficos pero también mejora la capacidad de extraer información de su análisis de datos. repasar consejos desde principiante hasta avanzado sobre cómo aprovechar al máximo su análisis de datos de Python en seaborn.
Análisis univariante
Level 1, 15 minutes
Use your Python data analysis skills to study what happened when isolated islanders were given drugs. Perform data analysis on a medical dataset with Pandas and learn how to build your first workflow to extract insights to will provide real-world understanding.
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Aprenda a usar el diagrama de caja de Seaborn para resaltar los valores atÃpicos que le preocupan.
Level 5, 33 minutes
We will use Pandas and Seaborn to perform our data analysis and better understand when new titles are released. Use WordCloud in Python to better understand the most common words in Movie and TV Show titles.
Level 7, 26 minutes
From the years 1966 to 2022 we will explore the Sri Lanka economy in our economic data analysis. We will attempt to develop a theory as to why the Sri Lanka economy turn into the 2022 economic crisis. Use the data to understand this real would economic situation.
Level 2, 29 minutes
We will use our Python data analysis skills in this beginner data analysis project to understand the eye color, fur color, and height of common dog breeds.
To start our Python data analysis project we will start by doing a little processing to enable our analyses. This is needed because of the semi-structured data format that happens when we have a list of different sizes.
Develop Marketing Campaign from Customer Data
Level 3, 29 minutes
In the development of a marketing strategy or atleast many different ideas for a marketing strategy based on the data. We get practice collecting observations and then at the end of our project we put these insights together in creative ways to offer ideas for a potential marketing strategy based on our data analysis.
Level 5, 35 minutes
Write your own user defined and use the .apply function in Pandas to apply the functions and provide valuable business insights to this Supermarket chain using your data analysis skills. Extract insights and compile them in interesting ways in the summary.
Level 6, 23 minutes
Use your data analysis skills to understand Data Analyst job listing and understand Data Job Market better. Explore job titles of different analyst positions business analyst, data analyst, BI analyst, and see what in what industries these sub-job groups are popular in
Spaceship Titanic Data Analysis Machine Learning Prep - Level 8, 23 minutes
Use pandas and seaborn in Python to analyze the spaceship titanic data from the Kaggle competition as if you were prepping it for a data scientist. extract insights using pandas seaborn and created user-defined functions to keep your analyses clean and to the point coding.
Python Real Data Analysis
Guided Projects
Real data sets have added difficulties that datasets found on Kaggle just don't have. On the job you will be working with these issues and in the Python Real Data Analysis we will our real datasets, Google Forms Survey Responses, Google Analytics and so much more and analyze real-world business problems.
Análisis univariante
Google Forms Survey Responses Guided Projects
Level 3, 21 minutes
We will use Pandas and Seaborn to understand the responses on Googles form. The goal of this survey was to understand how to improve these guided projects. However to analyze the data we need to spend a little extra time cleaning up the data which is common in real world datasets.