Data Analytics and Deep Learning for Financial Services (IBF Funded)

by Tertiary Courses

These are the exercise files used for # Data Analytics and Deep Learning for Financial Services (IBF Funded) course.

The course outline can be found in

https://www.tertiarycourses.com.sg/ibf-data-analytics-and-deep-learning-course-for-financial-services.html

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Topic 1 - Python Fundamental

Topic 1.1 Get Started on Python

• Overview of Python
• Set Python
• Code Your First Python Script

Topic 1.2: Data Types

• Number
• String
• List
• Tuple
• Dictionary
• Set

Topic 1.3 Operators

• Arithmetic Operators
• Compound Operators
• Comparison Operators
• Membership Operators
• Logical Operators

Topic 1.4 Control Structure, Loop and Comprehension

• Conditional
• Loop
• Iterating Over Multiple Sequences
• Comprehension

Topic 1.5 Function

• Function Syntax
• Return Values
• Default Arguments
• Variable Arguments
• Lambda, Map, Filter

Topic 1.6 Modules & Packages

• Import Modules and Packages
• Python Standard Packages
• Third Party Packages

Topic 2 - Data Analytics and Visualization with Python

Topic 2.1 Data Preparation

• Data Analytics with Pandas
• Pandas DataFrame and Series
• Import and Export Data
• Filter and Slice Data
• Clean Data

Topic 2.2 Data Transformation

• Join Data
• Transform Data
• Aggregate Data

Topic 2.3 Data Visualization

• Data Visualization with Matplotlib and Seaborn
• Visualize Statistical Relationships with Scatter Plot
• Visualize Categorical Data with Bar Plot
• Visualize Correlation with Pair Plot and Heatmap
• Visualize Linear Relationships with Regression

Topic 2.4 Data Analysis

• Statistical Data Analysis
• Time Series Analysis

Topic 2.5 Advanced Data Analytics

• Data Piping
• Groupby and Apply Custom Functions
• Linear Regression

Topic 3 Basic Deep Learning with Tensorflow

Topic 3.1 Introduction to Deep Learning

• Overview of Artificial Intelligence (AI)
• Applications of AI to Finance Services
• Deep Learning Methodology
• Setup Tensorflow Keras

Topic 3.2 Introduction to Neural Network

• What is Neural Network (NN)?
• Loss Function and Optimizer
• Build a Neural Network Model for Regression

Topic 3 Classification Model with Neural Network

• One Hot Encoding and SoftMax
• Cross Entropy Loss Function
• Build a Neural Network Model for Classification

Topic 4 Advanced Deep Learning Computational Models

Topic 4.1 Convolutional Neural Network (CNN)

• Introduction to Convolutional Neural Network (CNN)
• Convolution & Pooling
• Build a CNN Model for Image Recognition
• Overfitting and Underfitting Issues
• Methods to Solve Overfitting
• Small Dataset Overfitting Issue
• Data Augmentation & Dropout

Topic 4.2 Transfer Learning

• Introduction to Transfer Learning
• Pre-trained Models
• Transfer Learning for Feature Extraction & Fine Tuning

Topic 4.3 Recurrent Neural Network (RNN)

• Introduction to Recurrent Neural Network (RNN)
• Types of RNN Architectures
• RNN Model for Sentiment Analysis
• RNN Model for Stock Price Prediction

Final Assessment

• Written Assessment (Q&A)
• Written Assessment (Case Study)