Python for Data Analysis Course
About the Course
This course introduces key Python analytics tools and the IPython environment, teaching you to use NumPy and Pandas to load, explore, and transform data, then progresses to importing, cleaning, and preparing real-world datasets, including handling errors and applying text processing. You will also learn to structure and combine data for analysis, perform aggregations and advanced calculations, and present insights through effective visualisations using Matplotlib and Seaborn. All hands-on examples are run using Google Colab, a cloud-based Jupyter-compatible notebook environment. Skills are fully transferable to local Jupyter Notebook installations.
Learning Outcomes
Upon successful completion of this course, students will be able to:
- ▪ Load, clean, and prepare real business data for analysis using Pandas
- ▪ Build and query analytical data models using star and snowflake schemas
- ▪ Group, aggregate, and filter data to produce meaningful business insights
- ▪ Apply advanced analytics, including running totals, rankings, and moving averages
- ▪ Work confidently in IPython and Google Colab
- ▪ Apply regular expressions to clean and extract data from text columns
- ▪ Combine data from multiple sources using joins, concatenation, and reshaping
- ▪ Create professional data visualisations using Matplotlib and Seaborn
Prerequisites
Participants must have completed the Python Beginner course or have equivalent knowledge of Python fundamentals, including data types, control flow, functions, and basic scripting. Familiarity with tabular data concepts such as rows, columns, and basic filtering will be beneficial but is not required. No prior experience with data analytics libraries is assumed.
Course Details
$1980 incl GST
- Duration:3 Days
- Max. Class Size:10
- Avg. Class Size:5
-
Study Mode:
Classroom Online Live
- Level:Intermediate
- CPD Hours:
- Times: Classroom: 9.00am to 5.00pm approx(Local Time) Online Live: 9.00am to 5.00pm approx(AEST or AEDT)
- Download Course PDF
Course Dates
Sydney Class Dates
Level 11, 32 Walker Street, North Sydney , NSW 2060
Classes scheduled on demand in Sydney
Please our waitlist and we'll notify you when a new class is scheduled or contact us to discuss your training needs.
Melbourne Class Dates
Level 12, 379 Collins Street, Melbourne , VIC 3000
Classes scheduled on demand in Melbourne
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Brisbane Class Dates
All courses facilitated in, Online Live format , QLD
Classes scheduled on demand in Brisbane
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Canberra Class Dates
All courses facilitated in, Online Live format , ACT
Classes scheduled on demand in Canberra
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Adelaide Class Dates
All courses facilitated in, Online Live format , SA
Classes scheduled on demand in Adelaide
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Perth Class Dates
All courses facilitated in, Online Live format , WA
Classes scheduled on demand in Perth
Please our waitlist and we'll notify you when a new class is scheduled or contact us to discuss your training needs.
Course Units
⊕ Expand full topic list
Unit 1: Python Setup and Getting Started
- Understand Why Python Is Used for Data Analytics
- Compare Python with SQL Server and Power BI
- Install Essential Python Libraries for Data Analytics
- Understand What a Python Script Is and How It Works
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Unit 2: Overview of the Data Analysis Module Library
- Identify the Core Python Modules Used in Data Analytics
- Understand the Role of Each Module in the Analytics Workflow
- Recognise the Basic Syntax for Importing and Using Modules
- Import and Verify All Core Analytics Modules
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Unit 3: Introduction to IPython
- Start and Use IPython for Interactive Coding
- Use Magic Commands Including %time, %timeit, %who, and %history
- Run Python Scripts from Within IPython Using %run
- Reset the Session and Suppress Output
- Get Help and Documentation Using the? Operator
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Unit 4: Introduction to NumPy
- Create and Manipulate NumPy Arrays
- Reshape, Index, and Slice Arrays
- Perform Vectorized Math and Broadcasting
- Use Aggregation Functions Including sum, mean, and axis
- Generate Random Numbers for Sampling and Testing
- Compare NumPy Arrays to Python Lists
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Unit 5: Introduction to Pandas
- Create and Manipulate Series and DataFrames
- Select, Filter, Sort, and Reindex Data
- Apply Functions and Mapping to Columns
- Perform Basic Descriptive Statistics and Summarisation
- Inspect DataFrames Using .info() and .shape
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Unit 6: Data Loading and File Formats
- Read and Write Data in CSV and JSON Formats
- Read and Write Data in Excel Format
- Load the Sales Dataset from sales_data.xlsx for Analysis
- Retrieve Data from Public URLs and Web Tables
- Retrieve Data from Public APIs
- Understand How to Read Database Data
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Unit 7: Data Cleaning and Preparation
- Remove and Add Columns and Rows to Focus Analysis
- Split Columns and Add Calculated Columns Including Revenue
- Rename Columns and Set Data Types for Consistency
- Fix Spelling Mistakes, Abbreviations, and Formatting Errors
- Apply Forward Fill to Resolve Blank Cells
- Combine Multiple Cleaning Steps Using Method Chaining
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Unit 8: Regular Expressions (Regex) in Pandas
- Understand What Regular Expressions Are and Why They Are Useful
- Use Regex Patterns to Match, Extract, and Replace Text in Pandas
- Apply Regex with Common Pandas String Functions
- Recognise and Use Common Regex Wildcards and Patterns
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Unit 9: Analytical Functions and Grouping
- Group and Aggregate Data Using groupby and agg
- Apply Multiple and Custom Aggregation Functions
- Understand the Difference Between agg and transform
- Iterate Over Groups and Select Subsets
- Filter Groups Using Advanced Conditions
- Group by Multiple Columns for Deeper Insights
- Rank Values and Find Top N Within Groups
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Unit 10: Data Modelling
- Distinguish Between Analytical and Transactional Queries
- Identify Dimensions and Measures in Your Data
- Understand the Difference Between Measures and Calculated Columns
- Recognise Star and Snowflake Schema Patterns
- Apply Best Practices for Analytical Data Models
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Unit 11: Creating a Dates Dimension Table
- Understand the Purpose and Benefits of a Dates Dimension Table
- Create a Comprehensive Dates Table in Pandas
- Add Financial Year and Financial Month Columns
- Join the Dates Table to Sales Data for Time-Based Analysis
- Aggregate Revenue by Financial Year
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Unit 12: Joins and Combining Data
- Use Inner, Left, Right, and Outer Joins to Combine Tables
- Debug Joins Using the indicator Parameter
- Build a Star Schema Model by Joining Fact and Dimension Tables
- Add Calculated Columns Including Total Cost, Status, and Profit
- Concatenate DataFrames Vertically and Horizontally
- Reshape Data Using Pivot Tables, Melt, Stack, and Unstack
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Unit 13: Advanced Analytics
- Calculate Running Totals and Cumulative Metrics
- Apply Rolling and Expanding Window Functions
- Understand the Difference Between Rolling and Expanding Windows
- Rank Values Within Groups Using dense and first Methods
- Calculate Lag, Day-to-Day Change, and Percentage Change
- Normalise Values and Calculate Z-Scores Within Groups
- Compute Percentile Ranks Within Groups
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Unit 14: Data Visualisation with Matplotlib
- Choose the Right Chart Type for Your Data and Analytical Question
- Create Line Charts, Bar Charts, Scatter Plots, and Histograms
- Understand the Difference Between Bar Charts and Histograms
- Customise Plots with Titles, Labels, Colours, and Styles
- Create Subplots to Display Multiple Charts Together
- Save Charts to File for Use in Reports and Presentations
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Unit 15: Data Visualisation with Seaborn
- Understand What Seaborn Is and How It Builds on Matplotlib
- Apply Seaborn Themes and Styles to Produce Polished Charts
- Create Categorical Plots Including Bar, Box, and Count Plots
- Create Distribution Plots Including Histograms with KDE and KDE Plots
- Create Regression Plots with Trend Lines
- Create a Correlation Heatmap to Explore Relationships Between Variables
- Create a Pair Plot to Visualise All Numeric Relationships in One View
- Create Facet Grids to Compare Distributions Across Multiple Categories
⊕ See more
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