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Python for Data Analysis - Beginner

Length 1 day

Course overview

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Why study this course

Python is a very versatile programming language, broad in its application due to its simplicity and power. While Python can be used for everything from developing websites to programming robots, our courses focus on using Python for data analytics.

The Python Beginner course focuses on the fundamentals of working with Python for Data Analysis. We introduce Anaconda and JupyterLab and the basics of Python syntax. You will learn how to work with objects in Python, access and manipulate dataframes, how to use functions and methods and create basic visualisations.

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What you’ll learn

After completing this course, students will be able to:

Create basic markdown files
Create and manipulate objects
Use functions and methods
Create basic data visualisations
Create and manage environments in Anaconda
Install packages in Anaconda environments
Work in JupyterLab and with Jupyter Notebooks

Python at Lumify Work

Lumify Work's Python offering has been refreshed to include courses which fully align to certifications from the Python Institute. Our courses are delivered by Python programming experts who can structure their topic flow to your goals and functions, whether it's Python for automation or for data science or for finance.

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Who is the course for?

This course is intended for those who have never used Python before.

Course subjects

Introduction

Introduction to Python
Introduction to Anaconda and JupyterLab
The Python Standard Library

Installing Python

Anaconda
JupyterLab
Installing additional packages

Anaconda and JupyterLab

Using Anaconda
Working with environments
Launching JupyterLab
Working in JupyterLab
Using Jupyter Notebooks
Basics of running code
Markdown
Shutting down kernels and the Jupyter Server

Using Python as a Calculator

Arithmetic operators
Relational operators
Logical operators

The Python Standard Library

Built-in functions
Other functions in the standard library

Working with Objects

Objects in Python
What are objects?
Creating variables
Naming rules
Naming conventions for variables

Data Types and Structures

Built-in data types
Built-in data structures
Tuples, lists, ranges and dictionaries
Pandas Series and DataFrames
Which data structure should I use?
Pandas DataFrame
Attributes
Methods

Pandas functions

Creating DataFrames
Importing data into a DataFrame
Uploading data in JupyterLab

Accessing data within DataFrames

Accessing specific rows
Accessing specific columns
Accessing data subsets by name or position

Manipulating DataFrames

Making changes in place
Renaming columns and rows
Replace a single value
Replace multiple values
Add data to a DataFrame
Remove rows or columns
Filter based on condition
Sort data

Working with data in DataFrames

Calculating summary statistics

Evaluation in Python

Order of operations

Evaluation with Numpy

Vectorised arithmetic
Vectorised functions
Broadcasting
Creating new columns with vectorised arithmetic and functions

Functions vs Methods

What is a function?
Syntax for using functions in Python
Syntax for using methods in Python
Parameters and arguments
Getting help with a function
Overview of help documentation

Exporting Data

Export data to csv file

Basic Data Visualisation

Matplotlib
Create a scatterplot
Create a linechart
Add text
Add a legend
Exporting plots

Notebook to Markdown

View your completed Notebook as a rendered Markdown file

Prerequisites

No specific pre-requisites for the course.

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.