Introduction to Python and Data Analysis

4 Day Course
Hands On
Code PYPDAI

Book Now - 2 Delivery Methods Available:

Classroom Virtual Classroom Private Group - Virtual Self-Paced Online

Overview

This course is an introduction to Python and its main data analysis libraries, Pandas and Matplotlib for delegates with some understanding of programming concepts. It is a two-part course, the first is an introduction to Python programming, the second introduces Python's data analysis tools. For the programming environment we use JupyterLab on the Anaconda platform. Anaconda is one of the most, if not the most, popular Data Science platforms. Please note, this course is not meant for Data Analysts or Scientists who should instead consider our Data Analysis Python course.

Approach:

We believe in learning by doing and take a hands-on approach to training. Delegates are provided with all required resources, including data, and are expected to code along with the instructor. The objective is for delegates to reproduce the analysis in our manuals as well as gain a conceptual understanding of the methods.

Exercises and examples are used throughout the course to give practical hands-on experience with the techniques covered.

Objectives

The delegate will learn and acquire skills as follows:

Python

  • Variables and data type
  • If statements and loops
  • Comprehensions
  • Functions
  • Map, reduce and filter

Pandas and Matplotlib

  • Read csv, excel and json format data into Pandas DataFrame objects
  • Fetch data from local files, web url and a relational database
  • Clean, group, pivot, manipulate and summarise tabular data
  • Plot bar and pie charts, histograms, scatter and line graphs, using Matplotlib
  • Use JupyterLab

Target Audience

This course is designed for anyone who wants to acquire basic proficiency in Python and its data analysis tools for use in their own work. It is for numerate people who are familiar with programming constructs but are not necessarily programmers nor aiming to become data analysts or scientists but, want to be able to do some data manipulation and visualization using Python.

Modules

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Getting Started (7 topics)

  • About Python
  • Python versions
  • Python documentation
  • Python runtimes
  • Installing Python
  • The REPL shell
  • Python editors

Python: Scripts & Syntax (10 topics)

  • Script naming
  • Comments
  • Docstring
  • Statements
  • The backslash
  • Code blocks
  • Whitespace
  • Console IO (to enable the writing of simple programs)
  • A first Python program
  • Script execution

Python: Variables & Data Types (9 topics)

  • Literals
  • Identifiers
  • Assignment
  • Numbers (bool, int, float, complex)
  • Binary, octal, and hexadecimal numbers
  • Collections (str, list, tuple, set, dict)
  • None
  • Implicit and explicit type conversion (casting)
  • The type function

Operators & Expressions (7 topics)

  • Arithmetic Operators
  • Assignment Operators
  • Comparison Operators
  • Logical Operators
  • Membership Operators
  • Bitwise Operators
  • Identity Operators

Conditions & Loops (9 topics)

  • Conditional statements (if, elif, else)
  • Short hand if/if else
  • Python's alternative to the ternary operator
  • Iterative statements (while, for, else)
  • The range function
  • Iterating over a list
  • Break
  • Continue
  • Nested conditional/iterative statements

Functions (11 topics)

  • Declaration
  • Invocation
  • Default values for parameters
  • Named arguments
  • args and kwargs
  • Returning multiple values
  • Nested functions
  • Functions as data
  • Introduction to lambda expressions
  • Variable scope
  • The pass keyword

Comprehension (4 topics)

  • List Comprehension
  • Set Comprehension
  • The zip Function
  • Dictionary Comprehension

Functional Programming (4 topics)

  • Lambdas
  • Mapping
  • Filtering
  • Reducing

Object Oriented Concepts (3 topics)

  • Concepts
  • Simple Class Example
  • Object Creation

Introduction to Dataframes (9 topics)

  • What is a DataFrame?
  • Loading DataFrames
  • Accessing contents
  • Useful functions
  • Adding and dropping columns and rows
  • Fitering and assigning data
  • Missing values and duplicates
  • Arithmetic basics
  • Applymap and apply

GroupBy and Aggregation: Split-Apply-Combine (3 topics)

  • Basic GroupBy
  • Hierarchical GroupBy
  • Group by function of Index

GroupBy and Aggregation: Split-Apply-Combine Part 2 (4 topics)

  • Aggregate by mapping on Index and Columns
  • Aggregate by user-defined functions
  • Aggregate using multiple functions
  • Aggregate using separate function for each column

GroupBy and Aggregation: Split-Apply-Combine Part 3 (3 topics)

  • Transform
  • The Apply function
  • Pivoting with Aggregation

Plotting with Matplotlib (5 topics)

  • Pie chart
  • Bar chart
  • Histogram
  • Scatter plot
  • Line plot

Prerequisites

Programming:

  • Understanding of, and experience coding small programs that use variables, arrays or lists, conditional statements, loops and functions. Skills and knowledge that can be acquired by attending our Introduction to Programming course.

Numeracy:

  • Able to calculate and interpret averages, standard deviations and similar basic statistics.
  • Ability to read and understand charts and graphs.
  • Mathematics: GCSE or equivalent.

Scheduled Dates

Please select from the dates below to make an enquiry or booking.

Pricing

Different pricing structures are available including special offers. These include early bird, late availability, multi-place, corporate volume and self-funding rates. Please arrange a discussion with a training advisor to discover your most cost effective option.

Code Location Duration Price Jan Feb Mar Apr May Jun
PYPDAI 4 Days $2,715
12-15

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