java programming

Python for Data Analysis Training – 5 Days
Onsite, Tailored, Lowest Price

Course Description – Python for Data Analysis Training

This Python training course introduces the Python language and all its features that data analysts can use to analyze data and display information. All relevant basics of the language are covered in the first part of the course.  In the second half students learn to use the Jupyter lab notebook tool where they can test out ideas and see how they run without the effort of developing a full program.  The course explores the extensive Python libraries that provide useful code for data analysis and visualization.

What You Will Learn

  • Installing Python and writing basic scripts
  • Using built in data structures
  • Using all flow control features
  • Reading and writing from and to files
  • Using Python’s extensive libraries and functions
  • Accessing databases
  • Using Jupyter Notebook
  • Reading, cleaning, and structuring data
  • Analyzing data
  • Visualizing data

Prerequisites and Audience

Good computer skills and familiarity with basic programming concepts like variables, loops, and functions.  This class is designed for students new to Python who wish to use it for analysis and display of data.

Outline

Basic Python

Set up

  • Hello World Interactively
  • Hello World in a Script File
  • Python IDEs
  • Installing Anaconda
  • The print function
  • Comments

Variables and Data Types

  • Variables
  • Identifiers
  • Binding
  • Data Types
  • Basic Numbers
  • Basic Strings
  • Strings and regular expressions
  • Raw and Unicode strings
  • The re module
  • Using Tuples and Sequences
  • Using and modifying Lists
  • Using Dictionaries
  • Sequence slicing

Operators

  • Basic Numeric Operators
  • Basic Arithmetic Operators
  • Bitwise
  • Augmented Assignment
  • Truncating Division
  • Comparison and Logical
  • The Range Function

Control Structures

  • The if Statement
  • For loops
  • Using enumerate
  • List comprehensions
  • While loops

Functions

  • Built-in functions
  • Defining functions
  • Using function objects
  • Passing arguments
  • Returning values
  • Function overloading
  • Named parameters
  • Default parameters
  • Function scope rules
  • Using the global statement
  • Pass by reference or value
  • Functions
  • Varargs with * and **
  • Defining vararg functions
  • Expanding sequences
  • Lambda functions
  • Script template with __main__

Simple File I/O

  • Opening files
  • Reading and writing files
  • Reading whole files
  • Using a file interator
  • Reading from the command line

Getting things done with modules and libraries

  • What is a module
  • Adding module names to your namespace
  • Docstrings and Pydoc
  • Finding modules
  • Standard modules
  • The sys module
  • The os module
  • Math with modules
  • Dates and Times

Advanced Python

Persistence

  • Persistence options
  • The marshal module
  • The pickle module
  • JSON and Python
  • Accessing the MySql or Sqlite database
  • SQL Injection
  • Parameterized Statements

XML Processing

  • Reading and parsing XML files
  • Writing XML files
  • Using XPath
  • Modifying XML

Jupyter Tutorial

  • Interactive Jupyter Notebook tutorial

Basic Numerical Processing

  • The NumPy module
  • Using NumPy
  • Array slicing in NumPy
    Using NumPy Functions
  • Array Features
  • Matrices

Matplotlib

  • About matplotlib
  • Simple plot
  • Figures and subplots
  • Styles
  • Labels and Legends
  • Plotting DataFrames

Pandas

  • Pandas Overview
  • Pandas Data Structures
  • Series
  • DataFrames
  • Reading Data into DataFrames
  • Cleaning data
  • Indexing
  • Grouping
  • Transforming
  • Filtering

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