Data Analysis in Python - Beginner Course
Use Python for efficient Data Analysis
Understanding how to use Python for Data Analysis, empowers you to be much more efficient and opens up the possibility of using a wide array of freely available tools. Read our full course outline below.
Frequently Asked Questions
Meet your Trainer
Tamara Shatar holds a PhD in Agricultural Data Science. She has extensive experience, including many years working as a research scientist focused on data analysis, modelling using machine learning, simulation and other techniques. While working in both academia and at the CSIRO, part of her role included teaching a variety of data analysis skills. She is consistently well reviewed by her students.
"Great trainer, structure, material and manual. I have completed programming courses before but this one just made more sense! I'm sure it was the trainer. Looking forward to the Intermediate course when it is available." -Using R Beginner Sydney
Is Python compatible with Microsoft?
Python can be used within a number of Microsoft products. In Power BI, reusable Python scripts can be used to manipulate data and create visualisations. In Azure, machine learning models can be written in Python and trained and deployed within the Azure Machine Learning Workspace.
What is Remote Training?
Remote training at Nexacu, means our team of experienced trainers will deliver your training virtually. With remote learning students can access our usual classroom training courses via video conferencing, ask questions, participate in discussion and share their screen with the trainer if they need help at any point in the course. Students have the same level of participation and access to the trainer as they would in classroom training sessions.
Data Analysis in Python Beginner Course Details
Data Analysis in Python Beginner Course Details
Data Analysis in Python Beginner Course Details
Data Analysis in Python Beginner Course Details
Data Analysis in Python Beginner Course Details
Data Analysis in Python Beginner Course Details
Data Analysis in Python Beginner Course Details
Data Analysis in Python Beginner Course Details
Python Course Outlines
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Python Certification
Python City Pages
Skills Test
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What do I need to know to attend?
Data Analysis in Python Beginner Learning Outcomes
- use the extensive data manipulation capabilities of pandas DataFrames
- customise the display of the output in Jupyter Notebooks
- use the plotting capabilities of Matplotlib to plot distributions and bar charts
- use the data visualisation library, Seaborn, and
- fit a basic model using scikit-learn.
Data Analysis in Python Beginner Course Content
- Introduction
- Data Analysis in Python
- User-Defined Functions in Python
- Function basics
- Parameters
- Positional vs keyword arguments
- Defining a function
- Indentation
- User-Defined Functions in Python (cont'd)
- Scope
- *args and **kwargs
- Unpacking operators
- Lambda expressions
- Conditional expressions
- List comprehensions
- Modify the DataFrame Display
- pandas options
- Working with pandas styles
- Applying a style that is not dependent on values
- Formatting values
- String formats
- Applying a style that is dependent on values
- Built-in conditional formatting
- Export Notebook as
- Export to PDF or HTML
- Create slides
- Copy vs View
- Setting with copy warning
- Working with Missing Values
- Missing values
- inf and -inf
- Removing missing values
- Replacing missing values
- Importing Data
- Importing into a pandas DataFrame
- Manipulating Data
- Summarise a dataset
- Report and display multiple summary statistics
- Ordering data
- Manipulating Data (cont'd)
- Working with dates
- Add columns with assign()
- Working with strings
- Reordering and dropping columns
- Manipulating Data (cont'd)
- Selecting rows based on values
- Grouping and summarising data
- Replacing values
- Concatenate data
- Bin continuous variables into categories
- Working with Relational Data
- Joining data from two DataFrames
- Visualising Distributions
- Visual representation of distributions with Matplotlib and Seaborn
- Histograms
- Boxplots
- Bar and column charts
- Multivariate Analysis
- Scatterplot matrix
- Bar and column charts
- Basic Modelling
- Create a linear model with scikit-learn
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