Applied Python for Data Science
Essential Python for Analytics, Scientific & Math Computing | With numpy, scipy, pandas, PIL & More
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
Duration
4 days/32 hours of instructionPricing
Starting at: $2495
GSA Price: $1870
Group Rate: $2395
Get the full details on this course. Download the .PDF Brochure below:
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.
- Students should be comfortable working with files and folders and should not be afraid of the command line and basic scripting.
- 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