Course description
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.
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Who should attend?
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.
Training content
Session 1 -- The Python Environment
- About Python
- Starting Python
- Using the interpreter
- Running a Python script
- Python scripts on Unix/Windows
- Using the Spyder editor
Session 2 -- Getting Started
- Using variables
- Builtin functions
- Strings
- Numbers
- Converting among types
- Writing to the screen
- String formatting
- Command line parameters
Session 3 -- Flow Control
- About flow control
- White space
- Conditional expressions (if,else)
- Relational and Boolean operators
- While loops
- Alternate loop exits
Session 4 -- Sequences
- About sequences
- Lists and tuples
- Indexing and slicing
- Iterating through a sequence
- Sequence functions, keywords, and operators
- List comprehensions
- Generator expressions
- Nested sequences
Session 5 -- Working with files
- File overview
- Opening a text file
- Reading a text file
- Writing to a text file
- Raw (binary) data
Session 6 -- Dictionaries and Sets
- Creating dictionaries
- Iterating through a dictionary
- Creating sets
- Working with sets
Session 7 -- Functions
- Defining functions
- Parameters
- Variable scope
- Returning values
- Lambda functions
Session 8 -- Errors and Exception Handling
- Syntax errors
- Exceptions
- Using try/catch/else/finally
- Handling multiple exceptions
- Ignoring exceptions
Session 9 -- OS Services
- The os module
- Environment variables
- Launching external commands
- Walking directory trees
- Paths, directories, and filenames
- Working with file systems
- Dates and times
Session 10 -- Pythonic idioms
- Small Pythonisms
- Lambda functions
- Packing and unpacking sequences
- List Comprehensions
- Generator Expressions
Session 11 -- Modules and packages
- Initialization code
- Namespaces
- Executing modules as scripts
- Documentation
- Packages and name resolution
- Naming conventions
- Using imports
Session 12 -- Classes
- Defining classes
- Constructors
- Instance methods and data
- Attributes
- Inheritance
- Multiple inheritance
Session 13 -- Developer tools
- Analyzing programs with pylint
- Creating and running unit tests
- Debugging applications
- Benchmarking code
- Profiling applications
Session 14 -- 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
Session 15 -- iPython
- iPython basics
- Terminal and GUI shells
- Creating and using notebooks
- Saving and loading notebooks
- Ad hoc data visualization
Session 16 -- Numpy
- Numpy basics
- Creating arrays
- Indexing and slicing
- Large number sets
- Transforming data
- Advanced tricks
Session 17 -- Scipy
- What can scipy do?
- Most useful functions
- Curve fitting
- Modeling
- Data visualization
- Statistics
Session 18 -- 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
Session 19 -- Pandas
- Pandas overview
- Dataframes
- Reading and writing data
- Data alignment and reshaping
- Fancy indexing and slicing
- Merging and joining data sets
Session 20 -- Matplotlib
- Creating a basic plot
- Commonly used plots
- Ad hoc data visualization
- Advanced usage
- Exporting images
Session 21 -- The Python Imaging Library (PIL)
- PIL overview
- Core image library
- Image processing
- Displaying images
Costs
- Price: $2,495.00
- Discounted Price: $1,621.75
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Trivera Technologies
Trivera Technologies is a IT education services & courseware firm that offers a range of wide professional technical education services including: end to end IT training development and delivery, skills-based mentoring programs,new hire training and re-skilling services, courseware licensing and...