Programming for Data Analytics - 6 half-days, Fall 2019, Registration Deposit
Programming for Data Analytics - 6 half-days, Fall 2019, Registration Deposit

Programming for Data Analytics - 6 half-days, Fall 2019, Registration Deposit

Regular price $100.00 Sale

Note: this is the registration deposit only. The full cost of this course is $399.  There will be an additional payment of $299 due before the start of the second class.

This class is for students who wish to develop some coding ability to improve their analytics capability. We will be studying coding fundamentals in Python. These core concepts are transferable to other languages as well.

This course runs 6 half days on Saturdays, from 9:30am to 1:30pm.  Nov 9, 16, 23, 30, Dec 7 and 14th, 2019. The location is the Callingwood Recreation Centre in Edmonton, also known as the Callingwood Twin Arenas.

Visualization and data description is a foundation of machine learning. Students will learn techniques such as cluster analysis and scatter plots as well as techniques to quickly produce common charts and plots. We will look at network based classifiers (Naive Bayes), clustering and rule based classifiers.

Tools: We will use public domain Python libraries for data preparation, plotting and machine learning. These include Pandas, matplotlib and scikit-learn.

Class outline:
1. Coding fundamentals in Python.
2. Python language features
3. Data transformation, exploration and description, vocabulary. Pandas, matplotlib.
4. Data API’s, machine learning conecpts and vocabulary. scikit-learn
5. Clustering and Rule based Classifiers. Model error and performance.
6. Naive Bayes classifiers, model lift. Testing.

Your Instructor
Doug Kaweski has a degree in civil engineering with addition studies in decision analysis,  operations research, statistics and machine learning. He has worked as a consultant in:
Screening and validation of databases

Design of statistical studies
Evaluating reliability of measurement and scoring systems
Regression and Bayesian regression
Bayesian regression mathematics and software
Publication of a Bayesian regression analysis handbook
Data visualization and pictograms for embedding in analysis software
Optimization techniques, optimization software

He also has experience in machine learning including work with many machine learning techniques, evaluation and real world testing.