Intermediate Python for Data Science | Explore NumPy, Pandas, SciKit Learn, SciPy, TensorFlow & More)

Course Overview

Intermediate Python for Data Science 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.

Key Learning Areas

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current “on-the-job” experience into every classroom.

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

Course Outline

Python Quick Refresher

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

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

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

Optional: Working with TensorFlow

TensorFlow overview
Keras
Getting Started with TensorFlow

Who Benefits

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

Prerequisites

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

Take Before: Students should have attended or have incoming skills equivalent to those in the following courses:

Python Primer for Data Science
Applied Python for Data Science

Want this course for your team?

Atmosera can provide this course virtually or on-site. Please reach out to discuss your requirements.

Atmosera is thrilled to announce that we have been named GitHub AI Partner of the Year.