Next Level Python for Data Science | Working with Libraries, Frameworks, and Visualization Tools

This course explores using Python for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing using Big Data. In this course you’ll learn about essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, along with frameworks like TensorFlow and Spark. It also covers visualization tools like matplotlib, PIL, and Seaborn.

 

    Dec 7 2020

    December 7 - 11, 2020 | 10:00 AM - 6:00 PM (EST) | Virtual Classroom Live

    Date: 12/07/2020 - 12/11/2020 (Monday - Friday) | 10:00 AM - 6:00 PM (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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    Next Level Python for Data Science | Working with Libraries, Frameworks, and Visualization Tools

    December 7 - 11, 2020 | 10:00 AM - 6:00 PM (EST) | Virtual Classroom Live


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Python Review

  • Python Language
  • Essential Syntax
  • Lists, Sets, Dictionaries, and Comprehensions
  • Functions
  • Classes, Modules, and imports
  • Exceptions

iPython

  • iPython basics
  • Terminal and GUI shells
  • Creating and using notebooks
  • Saving and loading notebooks
  • Ad hoc data visualization
  • Web Notebooks (Jupyter)

NumPy

  • NumPy basics
  • Creating arrays
  • Indexing and slicing
  • Large number sets
  • Transforming data
  • Advanced tricks

SciPy

  • What can SciPy do?
  • Most useful functions
  • Curve fitting
  • Modeling
  • Data visualization
  • Statistics

SciPy subpackages

  • Clustering
  • Physical and mathematical Constants
  • FFTs
  • Integral and differential solvers
  • Interpolation and smoothing
  • Input and Output
  • Linear Algebra
  • Image Processing
  • Distance Regression
  • Root-finding
  • Signal Processing
  • Sparse Matrices
  • Spatial data and algorithms
  • Statistical distributions and functions
  • C/C++ Integration

pandas

  • pandas overview
  • Dataframes
  • Reading and writing data
  • Data alignment and reshaping
  • Fancy indexing and slicing
  • Merging and joining data sets

matplotlib

  • Creating a basic plot
  • Commonly used plots
  • Ad hoc data visualization
  • Advanced usage
  • Exporting images

The Python Imaging Library (PIL)

  • PIL overview
  • Core image library
  • Image processing
  • Displaying images

seaborn

  • Seaborn overview
  • Bivariate and univariate plots
  • Visualizing Linear Regressions
  • Visualizing Data Matrices
  • Working with Time Series data

SciKit-Learn Machine Learning Essentials

  • SciKit overview
  • SciKit-Learn overview
  • Algorithms Overview
  • Classification, Regression, Clustering, and Dimensionality Reduction
  • SciKit Demo

TensorFlow Overview

  • TensorFlow overview
  • Keras
  • Getting Started with TensorFlow

PySpark Overview

  • Python and Spark
  • SciKit-Learn vs. Spark MLlib
  • Python at Scale
  • PySpark Demo

RDDs and DataFrames

  • DataFrames and Resilient Distributed Datasets (RDDs)
  • Partitions
  • Adding variables to a DataFrame
  • DataFrame Types
  • DataFrame Operations
  • Dependent vs. Independent variables
  • Map/Reduce with DataFrames

Spark SQL

  • Spark SQL Overview
  • Data stores: HDFS, Cassandra, HBase, Hive, and S3
  • Table Definitions
  • Queries

Spark MLib

  • MLib overview
  • MLib Algorithms Overview
  • Classification Algorithms
  • Regression Algorithms
  • Decision Trees and forests
  • Recommendation with ALS
  • Clustering Algorithms
  • Machine Learning Pipelines
  • Linear Algebra (SVD, PCA)
  • Statistics in MLib

Spark Streaming

  • Streaming overview
  • Integrating Spark SQL, MLlib, and Streaming

Join an engaging hands-on learning environment, where you’ll learn:

  • How to work with Python in a Data Science context
  • How to use NumPy, Pandas, and MatPlotLib
  • How to create and process images with PIL
  • How to visualize with Seaborn
  • Key features of SciPy and SciKit Learn
  • How to interact with Spark using DataFrames
  • How to use SparkSQL, MLlib, and Big Data streaming

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

 

Before attending this course, you should have:

  • A solid data analytics and data science background
  • Python experience

Topics are covered in-depth and are geared for experienced students who have taken one of the prerequisite courses below or have practical hands-on experience.

Data Scientists, Data Engineers, and Software Engineers who are experienced with basic Python and data science.

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