Introduction to Python and Data Analysis [STA_PYPDAI]

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Introduction to Python and Data Analysis [STA_PYPDAI]

Global Knowledge Belgium BV
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Dates et lieux de début

placeVirtual
10 sept. 2024 jusqu'au 13 sept. 2024
placeVirtual
26 nov. 2024 jusqu'au 29 nov. 2024

Description

Vrijwel iedere training die op een onze locaties worden getoond zijn ook te volgen vanaf huis via Virtual Classroom training. Dit kunt u bij uw inschrijving erbij vermelden dat u hiervoor kiest.

OVERVIEW

Introduction to Python and Data Analysis Course 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.

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 in…

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Foire aux questions (FAQ)

Il n'y a pour le moment aucune question fréquente sur ce produit. Si vous avez besoin d'aide ou une question, contactez notre équipe support.

Vous n'avez pas trouvé ce que vous cherchiez ? Voir aussi : Data Science, Python, Data privacy, Data management et Big data.

Vrijwel iedere training die op een onze locaties worden getoond zijn ook te volgen vanaf huis via Virtual Classroom training. Dit kunt u bij uw inschrijving erbij vermelden dat u hiervoor kiest.

OVERVIEW

Introduction to Python and Data Analysis Course 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.

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

Course Objectives

This course aims to develop delegates skills in Python and its main data analysis libraries. On completion of the course they will have gained enough proficiency to allow them to apply these tools in their day to day data analysis activities.

AUDIENCE

Who will the Course Benefit?

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 who want to be able to do some data manipulation and visualization using Python.

NEXT STEP

Further Learning

  • Python Programming 1

CONTENT

Introduction to Python and Data Analysis Training Course

Course Contents - DAY 1

Course Introduction

  • Administration and Course Materials
  • Course Structure and Agenda
  • Delegate and Trainer Introductions

Session 1: INTRODUCTION

  • Python as an interpreted language
  • Script mode by example
  • Interactive mode
  • Statements
  • Comments
  • Whitespace and Indentation

Session 2: PYTHON: VARIABLES & SCALAR TYPES

  • Numerical types
  • Text
  • Boolean
  • Variables as references
  • The type() function

Session 3: OPERATORS & EXPRESSIONS

  • Arithmetic Operators
  • Assignment Operators
  • Comparison Operators
  • Logical Operators
  • Membership Operators

Session 4: CONTAINERS

  • Lists
  • Tuples
  • Sets
  • Dictionary

Introduction to Python and Data Analysis Training Course

Course Contents - DAY 2

Session 5: CONDITIONS & LOOPS

  • Basic if statement
  • Else clause
  • For loop
  • While loop
  • The range function
  • Iterating over a list
  • Break
  • Continue

Session 6: FUNCTIONS

  • inbuilt functions (len(),sum(),min(),max(),sorted())
  • defining functions
  • positional arguments
  • names arguments
  • default value arguments

Session 7: OBJECTS

  • What is a Class?
  • Data Attributes and Methods
  • A simple example
  • Some methods of inbuilt containers

Introduction to Python and Data Analysis Training Course

Course Contents - DAY 3

Session 8: INTRODUCTION TO DATAFRAMES

  • What is a DataFrame?
  • DataFrame attributes
  • Loading and writing DataFrames
  • Exploratory functions
  • Subsetting
  • Conditional subsetting
  • Adding and dropping columns
  • Inbuilt aggregating functions
  • Missing values

Introduction to Python and Data Analysis Training Course

Course Contents - DAY 4

Session 9: GROUPBY AND AGGREGATION: SPLIT-APPLY-COMBINE

  • Groupby one column and aggregate using single inbuilt function
  • Groupby two columns and aggregate using single inbuilt function
  • Groupby one column and aggregate using separate function for each column

Session 10: PLOTTING WITH MATPLOTLIB

  • Bar chart
  • Histogram
  • Line plot

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