Applied Python for Data Science

Geared for scientists and engineers with potentially light practical programming background or experience, Applied Python for Data Scientists is a hands-on Python course that provides a ramp-up to using Python for scientific and mathematical computing. Students will explore basic Python scripting skills and concepts, and then move to the most important Python modules for working with data, from arrays, to statistics, to plotting results.

This course is about 50% hands-on lab to 50% lecture ratio, combining engaging instructor presentations, demonstrations and discussions with extensive machine-based student labs and practical project work. Throughout the course students will learn to write essential Python scripts and apply them within a scientific framework working with the latest technologies listed in the agenda. Although the course is introductory in nature, it will increase in complexity as more sophisticated skills and techniques are introduced. Students can rely on our highly experienced instructors to provide informed, relatable, ‘real-world' answers to their questions.

Available formats for this course

In-Person

Live Online

Corporate

Corporate Online

Duration

4 days/32 hours of instruction

Starting at: $2495

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Part 1 – The Python Environment
1.   About Python
2.   Starting Python
3.   Using the interpreter
4.   Running a Python script
5.   Python scripts on Unix/Windows
6.   Using the Spyder editor

Part 2 – Getting Started
1.   Using variables
2.   Builtin functions
3.   Strings
4.   Numbers
5.   Converting among types
6.   Writing to the screen
7.   String formatting
8.   Command line parameters

Part 3 – Flow Control
1.   About flow control
2.   White space
3.   Conditional expressions (if,else)
4.   Relational and Boolean operators
5.   While loops
6.   Alternate loop exits

Part 4 – Sequences
1.   About sequences
2.   Lists and tuples
3.   Indexing and slicing
4.   Iterating through a sequence
5.   Sequence functions, keywords, and operators
6.   List comprehensions
7.   Generator expressions
8.   Nested sequences

Part 5 – Working with files
1.   File overview
2.   Opening a text file
3.   Reading a text file
4.   Writing to a text file
5.   Raw (binary) data

Part 6 – Dictionaries and Sets
1.   Creating dictionaries
2.   Iterating through a dictionary
3.   Creating sets
4.   Working with sets

Part 7 – Functions
1.   Defining functions
2.   Parameters
3.   Variable scope
4.   Returning values
5.   Lambda functions

Part 8 – Errors and Exception Handling
1.   Syntax errors
2.   Exceptions
3.   Using try/catch/else/finally
4.   Handling multiple exceptions
5.   Ignoring exceptions

Part 9 – OS Services
1.   The os module
2.   Environment variables
3.   Launching external commands
4.   Walking directory trees
5.   Paths, directories, and filenames
6.   Working with file systems
7.   Dates and times

Part 10 – Pythonic idioms
1.   Small Pythonisms
2.   Lambda functions
3.   Packing and unpacking sequences
4.   List Comprehensions
5.   Generator Expressions

Part 11 – Modules and packages
1.   Initialization code
2.   Namespaces
3.   Executing modules as scripts
4.   Documentation
5.   Packages and name resolution
6.   Naming conventions
7.   Using imports

Part 12 – Classes
1.   Defining classes
2.   Constructors
3.   Instance methods and data
4.   Attributes
5.   Inheritance
6.   Multiple inheritance

Part 13 – Developer tools
1.   Analyzing programs with pylint
2.   Creating and running unit tests
3.   Debugging applications
4.   Benchmarking code
5.   Profiling applications

Part 14 – XML and JSON
1.   Using ElementTree
2.   Creating a new XML document
3.   Parsing XML
4.   Finding by tags and XPath
5.   Parsing JSON into Python
6.   Parsing Python into JSON

Part 15 – iPython
1.   iiPython basics
2.   Terminal and GUI shells
3.   Creating and using notebooks
4.   Saving and loading notebooks
5.   Ad hoc data visualization

Part 16 – numpy
1.   numpy basics
2.   Creating arrays
3.   Indexing and slicing
4.   Large number sets
5.   Transforming data
6.   Advanced tricks

Part 17 – scipy
1.   What can scipy do?
2.   Most useful functions
3.   Curve fitting
4.   Modeling
5.   Data visualization
6.   Statistics

Part 18 – A tour of scipy subpackages
1.   Clustering
2.   Physical and mathematical Constants
3.   FFTs
4.   Integral and differential solvers
5.   Interpolation and smoothing
6.   Input and Output
7.   Linear Algebra
8.   Image Processing
9.   Distance Regression
10.   Root-finding
11.   Signal Processing
12.   Sparse Matrices
13.   Spatial data and algorithms
14.   Statistical distributions and functions
15.   C/C++ Integration

Part 19 – pandas
1.   pandas overview
2.   Dataframes
3.   Reading and writing data
4.   Data alignment and reshaping
5.   Fancy indexing and slicing
6.   Merging and joining data sets

Part 20 – matplotlib
1.   Creating a basic plot
2.   Commonly used plots
3.   Ad hoc data visualization
4.   Advanced usage
5.   Exporting images

Part 21 — The Python Imaging Library (PIL)
1.   PIL overview
2.   Core image library
3.   Image processing
4.   Displaying images

This course is geared for data analysts, developers, engineers, or anyone tasked with utilizing Python for data analytics tasks. While there are no specific programming prerequisites, students should be comfortable working with files and folders and should not be afraid of the command line and basic scripting.