Applied Python for Data Science
Applied Python for Data Science Course Details:
Geared for data scientists and engineers with potentially light practical programming background or experience, this course provides a ramp-up to using Python for scientific and mathematical computing. Students will explore basic Python scripting and concepts, and then move to the most important Python modules for working with data, from arrays to statistics to plotting results. Throughout the course you will learn to write essential Python scripts and apply them within a scientific framework working with the latest technologies.
Call (919) 283-1674 to get a class scheduled online or in your area!
The Python Environment
- About Python
- Starting Python
- Using the interpreter
- Running a Python script
- Python scripts on Unix/Windows
- Using the Spyder editor
Getting Started
- Using variables
- Builtin functions
- Strings
- Numbers
- Converting among types
- Writing to the screen
- String formatting
- Command line parameters
Flow Control
- About flow control
- White space
- Conditional expressions (if,else)
- Relational and Boolean operators
- While loops
- Alternate loop exits
Sequences
- About sequences
- Lists and tuples
- Indexing and slicing
- Iterating through a sequence
- Sequence functions, keywords, and operators
- List comprehensions
- Generator expressions
- Nested sequences
Working with files
- File overview
- Opening a text file
- Reading a text file
- Writing to a text file
- Raw (binary) data
Dictionaries and Sets
- Creating dictionaries
- Iterating through a dictionary
- Creating sets
- Working with sets
Functions
- Defining functions
- Parameters
- Variable scope
- Returning values
- Lambda functions
Errors and Exception Handling
- Syntax errors
- Exceptions
- Using try/catch/else/finally
- Handling multiple exceptions
- Ignoring exceptions
OS Services
- The os module
- Environment variables
- Launching external commands
- Walking directory trees
- Paths, directories, and filenames
- Working with file systems
- Dates and times
Pythonic idioms
- Small Pythonisms
- Lambda functions
- Packing and unpacking sequences
- List Comprehensions
- Generator Expressions
Modules and packages
- Initialization code
- Namespaces
- Executing modules as scripts
- Documentation
- Packages and name resolution
- Naming conventions
- Using imports
Classes
- Defining classes
- Constructors
- Instance methods and data
- Attributes
- Inheritance
- Multiple inheritance
Developer tools
- Analyzing programs with pylint
- Creating and running unit tests
- Debugging applications
- Benchmarking code
- Profiling applications
XML and JSON
- Using ElementTree
- Creating a new XML document
- Parsing XML
- Finding by tags and XPath
- Parsing JSON into Python
- Parsing Python into JSON
iPython
- iiPython basics
- Terminal and GUI shells
- Creating and using notebooks
- Saving and loading notebooks
- Ad hoc data visualization
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
*Please Note: Course Outline is subject to change without notice. Exact course outline will be provided at time of registration.
Join an engaging hands-on learning environment, where you’ll:
- Create and run basic programs
- Design and code modules and classes
- Implement and run unit tests
- Use benchmarks and profiling to speed up programs
- Process XML and JSON
- Manipulate arrays with NumPy
- Get a grasp of the diversity of subpackages that make up SciPy
- Use iPython notebooks for ad hoc calculations, plots, and what-if?
- Manipulate images with PIL
- Solve equations with SymPy
This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.
While there are no specific programming prerequisites, you should be comfortable working with files, folders, and the command line.
Data Scientist, Data Analyst, Data Engineer, or anyone tasked with utilizing Python for data analytics actions.