IT

Prerequisites

No prior experience with Python is necessary, though it is assumed that you will be familiar with core data concepts such as simple table structures and data types – all the pre-requisites you need are covered by our Data Essentials course.

Target Audience

This course is intended for Data Analysts, Data Engineers, Data Ops roles, and those training to consume AI Services or become Data Scientists and tune and develop Machine Learning and AI models on our subsequent Data Science learning pathway.

This course covers the key pre-requisites for a large range of further learning opportunities involving Python, Data, and AI.

Data Handling in Python Course Content

1. Introduction to Programming for Data Handling

• Describe the pros and cons of using programming languages to work with data
• Identify the languages most suitable for data handling
• Explain the challenges of using programming languages versus data analysis tools

2. Introduction to Python and IDEs

• Describe the key attributes of the Python programming language.
• Explain the role of the Jupyter IDE for Python programming.
• Use the Jupyter IDE to write a basic Python program.
• Write a program which uses string, integer, float and boolean data types.

3. Data Structures, Flow Control, Functions, and Basic Types

• Construct collections to solve data problems.
• Utilise selection and iteration syntax to control the flow of a Python program.
• Write reusable functions which can be used to alter data & automate repetitive tasks.
• Use Python's built-in open function to create, read, and edit files.

4. Mathematical and Statistical Programming with NumPy

• Describe the core features of NumPy arrays.
• Create, index, and manipulate NumPy arrays to solve data problems.
• Use masking and querying syntax to retrieve desired values.
• Use vectorised ufuncs.

5. Introduction to Pandas

• Create, manipulate, and alter Series and DataFrames with Pandas.
• Define and change the indices of Series & Dataframes.
• Use Pandas' functions and methods to change column types, compute summary statistics and aggregate data.
• Read, manipulate, and write data from csv, xlsx, json and other structured file formats.

6. Data Cleaning with Pandas

• Identify missing data and apply techniques to deal with it.
• Deduplicate, transform and replace values.
• Use DataFrame string methods to manipulate text data.
• Write regular expressions which munge text data.

7. Data Manipulation with Pandas

• Construct Pivot tables in Pandas.
• Time series manipulation.
• Stream data into Pandas to handle data size problems.

8. Methods for Visualising Data

• Construct and tailor basic data visualisations using Matplotlib & Seaborn for both numeric & non-numeric data.
• Meaningfully visualise aggregate data using Matplotlib and Seaborn.

Related learning

Data Science Learning Pathways can be selected by choosing either Python or R and a Cloud Platform certification:

• QAIDSDP Introduction to Data Science for Data Professionals
• Sourcing and handling data:
  • QADHPYTHON Data Handling with Python
  • QADHR Data Handling with R
  • QAPDHAI Python Data Handling with AI APIs
• Statistics for Data Analysis:
  • QASDAPY Statistics for Data Analysis with Python
  • QASDAR Statistics for Data Analysis with R
• Programming and Software Development skills:
  • QAPYTH3 Python Programming
  • QARPROG R Programming
• Machine Learning Development:
  • QADSMLP Data Science and Machine Learning with Python
  • QADSMLR Data Science and Machine Learning with R
• Mathematics for Developing Algorithms for AI models, Big Data Mining, and working with Neural Networks:
  • QAMFDS Mathematics for Data Science
• Forecasting:
  • QATSFP Time Series and Forecasting with Python
  • QATSFR Time Series and Forecasting with R

Suggested courses leading to Certification:

• MDP100 Designing and Implementing a Data Science Solution on Azure (DP-100)
• AMWSMLP Machine Learning Pipelines on AWS
• GCPMLGC Machine Learning on Google Cloud