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Python for Data Analysts

In this course, you’ll learn to code with Python. Python is a general-purpose programming language used for a variety of purposes, including web, software, and game development, artificial intelligence, machine learning, and data analysis.

Is this for you?

This course is for those who are interested in learning the Python programming language specifically for applications in data analysis and data science.

Award and Associated Qualifications

Awarded 52 CPD points upon successful completion

Start Date


Study Type

in centre, Online, or a combination

Training Type



52 hours

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    About this course

    The course includes a series of video lectures combined with a variety of conceptual and hands-on activities to help you develop the skills in Python to write commands, manipulate and analyse data, interpret results, and visualise data effectively.

    The course begins with fundamental programming concepts, such as functions and statements. We then cover several of the Python libraries most relevant for data management and manipulation (i.e., NumPy, Pandas, Seaborn, and Matplotlib). You’ll also learn about extracting, transforming, and loading data (ETL) techniques and operations, as well as what is known as “data wrangling”.

    The course is divided into four modules and includes knowledge tests at the end of each section.

    Course Content

    Module 1: Introduction to Programming with Python

    Getting Started with Python; Performing Operations with Complex Data Types; Working with “If” Statements, Loops, and Comprehensions; Defining, Configuring, and Invoking Functions; Leveraging Functions with Lambdas, Generators, Closures, and Decorators

    Module 2: Python for Data Science

    Introduction to NumPy for Multi-dimensional Data; Advanced Operations with NumPy Arrays; Manipulating and Analysing Data in Pandas Dataframes; Data Wrangling with Pandas; Data Visualisation Using Seaborn

    Module 3: Operations with petl

    Petl: Introduction; Basic Data Transformations; Advanced Extractions and Transformations

    Module 4: Data Science Statistics

    Using Python to Compute and Visualise Statistics; Applied Inferential Statistics

    Aims and Objectives


    Finance Options

    Career Path

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      What's the difference?

      We offer a range of training packages in a range of subject areas and can offer blended learning opportunities to best meet your needs


      Our courses are practical in nature and focus on a single subject and can last anywhere from a few hours to a few days.


      Diplomas are designed to give you a complete skillset mapped to a specific career path and contain a number of core and elective courses