Python for data science and big data essential

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In this course, you will learn first the basics of Python for Data Science and data visualization, to later apply them in the analysis of large volumes of data using the Spark environment, one of the most popular tools in the Big Data sector.

Python course for data science and big data essential


In this course, you will learn first the basics of Python for Data Science and data visualization, to later apply them in the analysis of large volumes of data using the Spark environment, one of the most popular tools in the Big Data sector. The content of the course is practical. We will lay the foundations for you to learn to develop your data project, from start to finish.

1. Introduction to Python for data science and big data

 Big data needs assessment

 Install Jupyter Notebook

 Install PySpark

 Evaluate code efficiency

 

2. Data management in Python

 Introduction to Pandas

 Filter data in Python

 Transformations of the database

 Groupby: get essential information

 Treat duplicate and lost data

 Introduction to the Numpy bookstore

 

3. Advanced data management

 Correlations Understand the relationships between the variables

 Test of the Chi-Square

 Analysis of extreme data

 Principles of relational databases

 Transform a dataframe into a relational database

 Joins. Work with relational databases

 Parallel loops in Python

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4. Data visualization in Matplotlib

 Introduction to Matplotlib

 Modify elements of the graph in Matplotlib

 Labels and legends in Matplotlib

 Graphics for time series in Matplotlib

 Histograms and box plots in Matplotlib

 Point clouds and heat maps in Matplotlib

 Introduction to the Plotly. Interactive visualization

 Advanced graphics with Plotly

 Map Display with Plotly

 

5. Essential Machine Learning: Clustering

 Machine Learning needs: clustering and modeling

 Prepare data for Machine Learning

 K-Means, the clustering algorithm

 The hierarchical clustering algorithm

 

6. Essential Machine Learning. Modeling

 Linear regression

 Logistic regression

 Naives Bayes Classifier

 Classification and regression trees

 Random forest

 Support vector machine

 K-Nearest Neighbors

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7. Work with PySpark

 Introduction to PySpark

 Syntax in PySpark. What do we need to know

 What are RDD (Resilient Distributed Databases)

 Lambda functions

 Dataframes in PySpark

 Basic transformations in PySpark

 Basic actions in PySpark

 Numerical operations with RDD

 

8. Advanced PySpark

 Joins at PySpark

 Accumulators How to detect patterns in our data

 How to build functions map

 How to build functions reduces

 Basic examples of MapReduce in PySpark

 

9. Challenge and solution: MapReduce

 Challenge: MapReduce applied with PySpark

 Resolution of the MapReduce exercises


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Grade 
03/03/2019

Excelente

Gran curso

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    Python for data science and big data essential

    Python for data science and big data essential

    In this course, you will learn first the basics of Python for Data Science and data visualization, to later apply them in the analysis of large volumes of data using the Spark environment, one of the most popular tools in the Big Data sector.

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