Empower: AI for Girls course has successfully been running every term at Ngee Ann Polytechnic for the past few years, giving girls from all polytechnics the opportunity to learn basic skills in AI.
Encourage the girls to learn and grow as far as they can go in the area of Data Analytics, namely to
● Understand a business problem
● Know how to handle and manipulate data
● Perform exploratory data analysis
● Be able to interpret and perform data visualization
● Know fundamental Machine Learning models to solve different problems
● Appreciate the opportunities and challenges of applying AI in real life.
Self directed learning for 9 weeks + 10th week Capstone project presentation.
Fixed set of 5 modules to go through, at the same starting point. Students are free to advance as quickly as they want.
Optional modules at the back for students to go through if they have completed the fixed set of modules
Introduction to Python
Intermediate Python for Data Science
Pandas Foundation
Introduction to Data Visualization with Python
Supervised Learning with scikit-learn
(Optional) Unsupervised Learning in Python
Capstone Project
Create variables and understand data types Store, access, manipulate data in lists
Use functions, methods, and packages to reduce amount of code.
Learn to work with Numpy array and use it to efficiently do data science
Create variables and understand data types Store, access, manipulate data in lists
Use functions, methods, and packages to reduce amount of code.
Learn to work with Numpy array and use it to efficiently do data science
Create variables and understand data types Store, access, manipulate data in lists
Use functions, methods, and packages to reduce amount of code.
Learn to work with Numpy array and use it to efficiently do data science
Build various types of plots, customize them to make it visually appealing and interpretable using Matplotlib.
Create, manipulate, access data from Dictionary and Pandas.
Learn comparison operators, how to combine them and use boolean outcomes in control structures.
Learn loops to iterate over all kinds of data structures.
Build Pandas DataFrame, how to import and inspect datasets using Pandas.
Ingest, inspect, and explore your data visually and quantitatively.
Manipulate and visualize time series data with tools like upsampling, downsampling and interpolation
Customize plots through methods like overlaying, making splots, controlling axes.
Use, present and orientate grids to represent two- variable functions.
Use seaborn to compute and visualize linear regressions, univariate and multivariate distributions.
Analyze time series and images.
Introduction to classification problems Use regression to solve a problem that requires a continuous outcome.
Evaluate a model's performance and the metrics to use to gauge how good it is.
Learn about pipelines, transformer, estimators and pre-processing techniques.
Create variables and understand data types Store, access, manipulate data in lists
Use functions, methods, and packages to reduce amount of code.
Learn to work with Numpy array and use it to efficiently do data science
(optional) Unsupervised Learning in python
Students work in teams of 3 -4 (mixed sch) on group projects, applying the techniques they have learnt.
Allow students to either provide a very solid in depth analysis of data with visualization or apply Machine Learning models
