Python for Predictive Data Analytics
This is a course for data analysts, quants, statisticians, software developers, and other technical staff interested in learning to use Python for analysing and visualising data and performing powerful predictive analytics. Includes a thorough introduction to machine learning for regression and classification.
Prerequisites
Some familiarity with programming concepts (in any language) is assumed.
Expected Outcomes
By the end of the course, you will have all the knowledge you need to start using Python competently for processing, analysing, modelling, and visualising various kinds of data, with a focus on time series. You will have had experience with using Python for various scripting, data-manipulation and plotting tasks with data in a variety of formats, including CSV, Excel spreadsheets, SQL databases, JSON, and API endpoints. You will have applied powerful tools for optimisation, regression, classification, and clustering, in useful practical settings on a variety of data sets. You will understand the elegance and power of the Python language and its powerful ecosystem of packages for data analytics, and you will be well-placed to continue learning more as you use it day-to-day.
Course Syllabus
Day 1: Python Basics
Day 1 covers how to use Python for basic scripting and automation tasks, including tips and tricks for making this easy:
- Why Python? What’s possible?
- The Jupyter notebook for rapid prototyping
- Modules and packages
- Python concepts: an introduction through examples
- Essential data types: strings, tuples, lists, dicts
- Worked example: retrieving real-time data from a REST web API
- Raising and handling exceptions
Day 2: Handling, Analyzing, and Presenting Data in Python
Python offers amazingly productive tools like Pandas for working with different kinds of data. Day 2 gives a thorough introduction to analyzing and visualizing data easily:
- Reading and writing essential data formats: CSV, Excel, SQL, time-series (others on request)
- Indexing and selecting data in Pandas
- Data fusion: joining & merging datasets
- Summarization with “group by” operations; pivot tables
- Visualization and statistical graphics with Seaborn
- Worked example: creating automated reports with Jupyter
Day 3: Time-series, Simulation, Inference and Modelling
Day 3 demonstrates more advanced features of Pandas for working with data, including time-series data. It then describes Monte Carlo simulation methods and walks you through using powerful methods of inference and modelling as well as clustering and outlier detection:
- Time-series analysis: parsing dates, resampling, handling time-zones, rolling-window operations
- Introduction to NumPy for manipulating vector and matrix data: data types, powerful indexing, reshaping, ufuncs
- Linear regression with statsmodels
- Statistics: Monte Carlo simulation; density estimation
- Clustering with scikit-learn
- Outlier and anomaly detection with pyod
Day 4: Machine Learning
Day 4 introduces a more automated approach to modelling real-world data with several powerful machine learning algorithms using scikit-learn. The datasets are selected from a range of industries: financial, geospatial, medical, and social sciences:
- Concepts of machine learning (ML)
- Overview of the ML package ecosystem in Python
- Nonlinear regression, with application to forecasting
- Classification with scikit-learn: Application to diagnosis, AI systems, and time-series prediction
- Overview of "classical" ML algorithms: Naive Bayes, logistic regression, SVMs, random forests
- Validation and model selection; feature selection
- Feature engineering
Personal help
We are happy to offer on-the-spot problem-solving after each day of the training for you to ask one-on-one questions — whether about the course content and exercises or about specific problems you face in your work and how to solve them. If you would like us to prepare for this in advance, you are welcome to send us background info before the course.
Other information
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Materials:
We will provide you with printed course notes, cheat sheets, and a USB stick containing kitchen-sink Python installers for multiple platforms, solutions to the programming exercises, several written tutorials, and reference documentation on Python and the third-party packages covered in the course.
Venues:
Modern computer-based training facilities (CBD locations).
Computer:
An internet-connected compupter will be provided for you.
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Timing:
The course will run from 9:00 to roughly 17:00 each day, with breaks of 50 minutes for lunch and 20 minutes each for morning and afternoon tea.
Food and drink:
We will provide lunch, morning and afternoon tea, and drinks.
Certificate of completion:
We will provide you a certificate if you complete the course and successfully answer the majority of the exercise questions.
Testimonials
“The course was delivered by trainers who were extremely knowledgeable in their field. It was really good to learn from the best!”
“Great course. Enjoyed it and will follow up with some practical implementation of some of the work.”
“One of the best training courses I've been on.”
“I loved it. Ed was inspiring.”
“Really impressed by Python's capability and excited to use as alternative to MatLab, as is free and better supported.”
“Very comprehensive intro to every aspect of python. Highly qualified trainer. Beyond my expectation on every aspect.”
“The VM setup and USB is great. Ed is an excellent instructor - he presents well and welcomes any questions. He is clearly a super smart guy who has a great grasp on what he is teaching - able to just prototype on the fly and the course overall really opened my eyes to python.”
“Course content was well presented and easily digested. Practical exercises were an essential part of the course – a good ratio of lecture/play was achieved. Well done Ed and Henry!”
“Simply awesome!!”
“Both Ed and Henry presented well…. The course structure was adjusted to suit the participants quickly and easily.”
“Excellent training course, excellently presented. Perhaps the best that I have had in the area of IT / programming.”
“One of the best programming courses I have attended - thanks!”
“This course has shown me how I could have done the work I was doing just last week 10x more efficiently in Python.”
“It was a pleasure ... Shared feedback from all involved was that it’s been one of the most beneficial and well delivered training courses we’ve been a part of.”
“Was the most fulfilling and rewarding class I have taken since "general relativity" at uni. Was extremely well run. Excellent all round!”
“Very impressed with the course, delivery. And depth of knowledge of Ed and Henry. Far exceeded my expectations and has greatly improved my core skills as well as inspired so many new ideas for my current work / projects. Thank you!”
