Programs

Outlines

• Computer Science 101
• Introduction to Python
• Hands On Training

Learning OutComes

• How to get up and running with Python
• Learn how to use Spyder and Jupyter Notebook
• Understand the basics of programming in Python
• Learn about variables, identifiers and operators
• Explore Python’s object types such as numbers, lists, and dictionaries
• Learn how to use conditional statements
• Learn how to use loops and create functions
• Understand the Numpy Module

Outlines

• Introduction to Data Analysis
• Working with Data, ETL
• Numpy
• Pandas
• Data Visualisation
• Hands On Training
• Build Portfolio

Learning OutComes

• Have an intermediate skill level of Python programming.
• Use the Jupyter Notebook Environment.
• Use the numpy library to create and manipulate arrays.
• Use the pandas module with Python to create and structure data.
• Learn how to work with various data formats within python, including: JSON,HTML, and MS Excel Worksheets.
• Create data visualizations using matplotlib and the seaborn modules with python.
• Have a portfolio of various data analysis projects.

Outlines

• Introduction to Data Science
• Design Thinking
• Data Cleaning
• Data Mining
• Exploratary Data Analaysis
• Data Modelling
• Regressions & Classification
• Prediction
• Data Visualisation & Story Telling
• Capstone Project

Learning OutComes

• Understanding of the data science field and the type of analysis carried out
• Mathematics
• Statistics
• Python
• Applying advanced statistical techniques in Python
• Data Visualization
• Portfolio data science projects
• Identify the appropriate model for different data types
• Create your own data process and analysis workflow
• Define and explain the key concepts and models relevant to data science
• Differentiate key data ETL process, from cleaning, processing to visualization
• Implement algorithms to extract information from dataset
• Apply best practices in data science, and become familiar with standard tools.

Outlines

• Introduction to Machine Learning
• Applications of Machine Learning
• Mathematics Fundementals in Machine Learning
• Machine Learning & Statiscal Algorithms
• Use Cases & Research
• Hands On

Learning OutComes

• Master Machine Learning on Python
• Have a great intuition of many Machine Learning models
• Make accurate predictions
• Make powerful analysis
• Make robust Machine Learning models
• Create strong added value to your business
• Use Machine Learning for personal purpose
• Know which Machine Learning model to choose for each type of problem
• Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Outlines

• Introduction to Deep Learning
• Algorthims
• Applications of Deep Learning ,
• Mathematics in Neural Networks
• Tensor Flow, Keras - Deep Learning Frameworks
• Hands On

Learning OutComes

• Understand the intuition behind Artificial Neural Networks
• Apply Artificial Neural Networks in practice
• Understand the intuition behind Convolutional Neural Networks
• Apply Convolutional Neural Networks in practice
• Understand the intuition behind Recurrent Neural Networks
• Apply Recurrent Neural Networks in practice
• Understand the intuition behind Self-Organizing Maps
• Apply Self-Organizing Maps in practice
• Understand the intuition behind Boltzmann Machines