Course description
Fast Track to Python for Data Science
Python Primer for Data Science is a three-day, hands-on course that introduces data analysts and business analysts to the Python programming language, as it’s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice.
Students will explore basic Python syntax and concepts applicable to using Python to work with data. The course begins with quick introduction to Python, with demonstrations of both script-based and web notebook-based Python, and then dives into the essentials of Python necessary to a data scientist. The tail end of the course explores a quick integration of these skills with key Data Science libraries including NumPy, Pandas and Matplotlib. Students will explore the concepts and work with large data sets in a workshop style lab.
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Who should attend?
This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with SAS or working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course.
Training content
Session 1: An Overview of Python
- Why Python?
- Python in the Shell
- Python in Web Notebooks (iPython, Jupyter, Zeppelin)
- Demo: Python, Notebooks, and Data Science
- Python 2 vs 3
Session 2: Getting Started
- Using variables
- Builtin functions
- Strings
- Numbers
- Converting among types
- Writing to the screen
- Command line parameters
- Running standalone scripts under Unix and Windows
Session 3: Flow Control
- About flow control
- White space
- Conditional expressions
- Relational and Boolean operators
- While loops
- Alternate loop exits
Session 4: Sequences, Arrays, Dictionaries and Sets
- About sequences
- Lists and list methods
- Tuples
- Indexing and slicing
- Iterating through a sequence
- Sequence functions, keywords, and operators
- List comprehensions
- Generator Expressions
- Nested sequences
- Working with Dictionaries
- Working with Sets
Session 5: Working with files
- File overview
- Opening a text file
- Reading a text file
- Writing to a text file
- Reading and writing raw (binary) data
Session 6: Functions
- Defining functions
- Parameters
- Global and local scope
- Nested functions
- Returning values
Session 7: Sorting
- The sorted() function
- Alternate keys
- Lambda functions
- Sorting collections
- Using operator.itemgetter()
- Reverse sorting
Session 8: Errors and Exception Handling
- Syntax errors
- Exceptions
- Using try/catch/else/finally
- Handling multiple exceptions
- Ignoring exceptions
Session 9: Essential Demos
- Importing Modules
- Classes
- Regular Expressions
Session 10: The standard library
- Math functions
- The string module
Session 11: Dates and times
- Working with dates and times
- Translating timestamps
- Parsing dates from text
- Formatting dates
- Calendar data
Session 12: Numpy
- Numpy basics
- Creating arrays
- Indexing and slicing
- Large number sets
- Transforming data
- Advanced tricks
Session 13: Python and Data Science
- Data Science Essentials
- Working with Python in Data Science
Session 14: Working with Pandas
- Pandas overview
- Dataframes
- Reading and writing data
- Data alignment and reshaping
- Fancy indexing and slicing
- Merging and joining data sets
Time Permitting
Session: Matplotbil
- Creating a basic plot
- Commonly used plots
- Ad hoc data visualization
- Advanced usage
- Exporting images
Costs
- Price: $2,195.00
- Discounted Price: $1,426.75
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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...