Collecting Web Data
Web scraping is a technique for extracting information from websites. This can be done manually but it is usually faster, more efficient and less error-prone if it can be automated.
Web scraping allows you to convert non-tabular or poorly structured data into a usable, structured format, such as...
Keywords: Data Management, Python
Collecting Web Data
https://intersect.org.au/training/course/webdata201
https://dresa.org.au/materials/collecting-web-data
Web scraping is a technique for extracting information from websites. This can be done manually but it is usually faster, more efficient and less error-prone if it can be automated.
Web scraping allows you to convert non-tabular or poorly structured data into a usable, structured format, such as a .csv file or spreadsheet. But scraping is about more than just acquiring data: it can help you track changes to data online, and help you archive data. In short, it's a skill worth learning.
So join us for this web scraping workshop to learn web scraping, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- The concept of structured data
- The use of XPath queries on HTML document
- How to scrape data using browser extensions
- How to scrape using Python and Scrapy
- How to automate the scraping of multiple web pages
#### Prerequisites:
A good knowledge of the basic concepts and techniques in Python. Consider taking our [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to come up to speed beforehand.
**For more information, please click [here](https://intersect.org.au/training/course/webdata201).**
training@intersect.org.au
Data Management, Python
Start Coding without Hesitation: Programming Languages Showdown
Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are...
Keywords: Programming, Python, R, MATLAB, Julia
Start Coding without Hesitation: Programming Languages Showdown
https://intersect.org.au/training/course/coding001
https://dresa.org.au/materials/start-coding-without-hesitation-programming-languages-showdown
Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are exploring four of the most popular programming languages that are widely used in academia, namely Python, R, MATLAB, and Julia.
#### You'll learn:
- Why use Programming
- An overview of Python, R, MATLAB, and Julia
- Code comparison of the four programming languages
- Popularity and job opportunities
- Intersect's comparison
- General guidelines on how to choose the best programming language for your research
#### Prerequisites:
The webinar has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/coding001).**
training@intersect.org.au
Programming, Python, R, MATLAB, Julia
A showcase of Data Analysis in Python and R: A case study using COVID-19 data
In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and...
Keywords: Programming, Python, R
A showcase of Data Analysis in Python and R: A case study using COVID-19 data
https://intersect.org.au/training/course/coding002
https://dresa.org.au/materials/a-showcase-of-data-analysis-in-python-and-r-a-case-study-using-covid-19-data
In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and Python.
#### You'll learn:
- Cleaning up a dataset for analysis
- Using Jupyter lab for interactive analysis
- Making the most of the tidyverse (R) and pandas (python)
- Simple data visualisation using ggplot (R) and seaborn (python)
- Best practices for readable code
#### Prerequisites:
The webinar has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/coding002).**
training@intersect.org.au
Programming, Python, R
Learn to Program: Python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
We teach using Jupyter notebooks, which allow program code, results,...
Keywords: Programming, Python
Learn to Program: Python
https://intersect.org.au/training/course/python101
https://dresa.org.au/materials/learn-to-program-python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Introduction to the JupyterLab interface for programming
- Basic syntax and data types in Python
- How to load external data into Python
- Creating functions (FUNCTIONS)
- Repeating actions and anylsing multiple data sets (LOOPS)
- Making choices (IF STATEMENTS - CONDITIONALS)
- Ways to visualise data in Python
#### Prerequisites:
No prior experience with programming needed to attend this course.
We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/).
**For more information, please click [here](https://intersect.org.au/training/course/python101).**
training@intersect.org.au
Programming, Python
Python for Research
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
This workshop is an introduction to data structures (DataFrames using...
Keywords: Programming, Python
Python for Research
https://intersect.org.au/training/course/python110
https://dresa.org.au/materials/python-for-research
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
This workshop is an introduction to data structures (DataFrames using the pandas library) and visualisation (using the matplotlib library) in Python. The targeted audience for this workshop is researchers who are already familiar with the basic concepts in programming such as loops, functions, and conditionals.
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Introduction to Libraries and Built-in Functions in Python
- Introduction to DataFrames using the pandas library
- Reading and writing data in DataFrames
- Selecting values in DataFrames
- Quick introduction to Plotting using the matplotlib library
#### Prerequisites:
[Learn to Program: Python](https://intersect.org.au/training/course/python101/) or any of the [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Learn to Program: MATLAB](https://intersect.org.au/training/course/matlab101/) or [Learn to Program: Julia](https://intersect.org.au/training/course/julia101/), needed to attend this course. If you already have some experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) course to ensure that you are familiar with the knowledge needed for this course.
**For more information, please click [here](https://intersect.org.au/training/course/python110).**
training@intersect.org.au
Programming, Python
Data Manipulation in Python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using the...
Keywords: Programming, Python
Data Manipulation in Python
https://intersect.org.au/training/course/python201
https://dresa.org.au/materials/data-manipulation-in-python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets.
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Working with pandas DataFrames
- Indexing, slicing and subsetting in pandas DataFrames
- Missing data values
- Combine multiple pandas DataFrames
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course.
**For more information, please click [here](https://intersect.org.au/training/course/python201).**
training@intersect.org.au
Programming, Python
Data Visualisation in Python
Course Materials
You'll learn:
- Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
- Configuring plot elements within seaborn and matplotlib
- Exploring different types of plots using seaborn
Prerequisites:
Either [Learn to...
Keywords: Programming, Python
Data Visualisation in Python
https://intersect.org.au/training/course/python202
https://dresa.org.au/materials/data-visualisation-in-python
Course Materials
#### You'll learn:
- Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
- Configuring plot elements within seaborn and matplotlib
- Exploring different types of plots using seaborn
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course.
We also strongly recommend attending the [Data Manipulation in Python](https://intersect.org.au/training/course/python201/).
**For more information, please click [here](https://intersect.org.au/training/course/python202).**
training@intersect.org.au
Programming, Python
Data Manipulation and Visualisation in Python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using the...
Keywords: Programming, Python
Data Manipulation and Visualisation in Python
https://intersect.org.au/training/course/python203
https://dresa.org.au/materials/data-manipulation-and-visualisation-in-python
Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.
In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. You will also explore different types of graphs and learn how to customise them using two of the most popular plotting libraries in Python, matplotlib and seaborn (Data Visualisation).
We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research.
Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- Working with pandas DataFrames
- Indexing, slicing and subsetting in pandas DataFrames
- Missing data values
- Combine multiple pandas DataFrames
- Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
- Configuring plot elements within seaborn and matplotlib
- Exploring different types of plots using seaborn
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course.
**For more information, please click [here](https://intersect.org.au/training/course/python203).**
training@intersect.org.au
Programming, Python
Introduction to Machine Learning using Python: Introduction & Linear Regression
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...
Keywords: Programming, Python
Introduction to Machine Learning using Python: Introduction & Linear Regression
https://intersect.org.au/training/course/python205
https://dresa.org.au/materials/introduction-to-machine-learning-using-python-introduction-linear-regression
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries.
#### You'll learn:
- Understand the difference between supervised and unsupervised Machine Learning.
- Understand the fundamentals of Machine Learning.
- Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training.
- Understand the Machine Learning modelling workflows.
- Use Python and scikit-learn to process real datasets, train and apply Machine Learning models
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) needed to attend this course.
If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax and basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries.
Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.
#### Why do this course:
- Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.
- It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.
- We do have applications on real datasets!
- Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.
- Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.
For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops:
- Introduction to Machine Learning using Python: Introduction & Linear Regression
- Introduction to Machine Learning using Python: Classification
- Introduction to Machine Learning using Python: SVM & Unsupervised Learning
**For more information, please click [here](https://intersect.org.au/training/course/python205).**
training@intersect.org.au
Programming, Python
Introduction to Machine Learning using Python: Classification
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...
Keywords: Programming, Python
Introduction to Machine Learning using Python: Classification
https://intersect.org.au/training/course/python206
https://dresa.org.au/materials/introduction-to-machine-learning-using-python-classification
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries.
#### You'll learn:
- Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning.
- Know the differences between various core Machine Learning models.
- Understand the Machine Learning modelling workflows.
- Use Python and scikit-learn to process real datasets, train and apply Machine Learning models
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course.
If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training.
Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.
#### Why do this course:
- Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.
- It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.
- We do have applications on real datasets!
- Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.
- Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.
For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops:
- Introduction to Machine Learning using Python: Introduction & Linear Regression
- Introduction to Machine Learning using Python: Classification
- Introduction to Machine Learning using Python: SVM & Unsupervised Learning
**For more information, please click [here](https://intersect.org.au/training/course/python206).**
training@intersect.org.au
Programming, Python
Introduction to Machine Learning using Python: SVM & Unsupervised Learning
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore...
Keywords: Programming, Python
Introduction to Machine Learning using Python: SVM & Unsupervised Learning
https://intersect.org.au/training/course/python207
https://dresa.org.au/materials/introduction-to-machine-learning-using-python-svm-unsupervised-learning
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries.
#### You'll learn:
- Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.
- Know the differences between various core Machine Learning models.
- Understand the Machine Learning modelling workflows.
- Use Python and scikit-learn to process real datasets, train and apply Machine Learning models
#### Prerequisites:
Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course.
If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training.
Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.
#### Why do this course:
- Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.
- It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.
- We do have applications on real datasets!
- Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.
- Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.
For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops:
- Introduction to Machine Learning using Python: Introduction & Linear Regression
- Introduction to Machine Learning using Python: Classification
- Introduction to Machine Learning using Python: SVM & Unsupervised Learning
**For more information, please click [here](https://intersect.org.au/training/course/python207).**
training@intersect.org.au
Programming, Python