Next Level Python for Data Science and /or Machine Learning covers the essentials of using Python as a tool for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing on “Big Data”. In this course we cover essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, TensorFlow, as well as visualization tools like matplotlib, PIL, and Seaborn. This course is ‘intermediate level’ as it assumes that attendees have solid data analytics and data science background and have basic Python knowledge. Topics are introductory in nature, but are covered in-depth, geared for experienced students.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

Working in a hands-on learning environment, guided by our expert team, attendees will learn how to:
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

1

  • PYTHON QUICK REFRESHER

  • Python Language

    Essential Syntax

    Lists, Sets, Dictionaries, and Comprehensions

    Functions

    Classes, Modules, and imports

    Exceptions

2

  • IPYTHON

  • iPython basics

    Terminal and GUI shells

    Creating and using notebooks

    Saving and loading notebooks

    Ad hoc data visualization

    Web Notebooks (Jupyter)

3

  • NUMPY

  • numpy basics

    Creating arrays

    Indexing and slicing

    Large number sets

    Transforming data

    Advanced tricks

4

  • SCIPY

  • What can scipy do?

    Most useful functions

    Curve fitting

    Modeling

    Data visualization

    Statistics

5

  • A TOUR OF 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

6

  • PANDAS

  • pandas overview

    Dataframes

    Reading and writing data

    Data alignment and reshaping

    Fancy indexing and slicing

    Merging and joining data sets

7

  • MATPLOTLIB

  • Creating a basic plot

    Commonly used plots

    Ad hoc data visualization

    Advanced usage

    Exporting images

8

  • THE PYTHON IMAGING LIBRARY (PIL)

  • PIL overview

    Core image library

    Image processing

    Displaying images

9

  • SEABORN

  • Seaborn overview

    Bivariate and univariate plots

    Visualizing Linear Regressions

    Visualizing Data Matrices

    Working with Time Series data

10

  • SCIKIT-LEARN MACHINE LEARNING ESSENTIALS

  • SciKit overview

    SciKit-Learn overview

    Algorithms Overview

    Classification, Regression, Clustering, and Dimensionality Reduction

    SciKit Demo

11

  • OPTIONAL: WORKING WITH TENSORFLOW

  • TensorFlow overview

    Keras

    Getting Started with TensorFlow

Audience

This course is geared for experienced data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics or eventual machine learning tasks

Language

English

Prerequisites

Attending students are required to have a background in basic Python development skills

$2,595

Length: 5.0 days (40 hours)

Level:

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