Big Data Analytics for Technical Professionals

Big Data Analytics for Technical Professionals

Advanced 20-day outline

Welcome to the advanced installment of "Big Data Analytics for Technical Professionals." This 20-day course is meticulously crafted for those who wish to venture into specialized fields within data analytics. Covering a range of advanced topics from supervised machine learning to NoSQL databases, and even natural language processing, this course is your gateway to becoming an expert in Big Data analytics. As always, we infuse the rich and diverse landscape of Malaysia into our examples, projects, and case studies.

Supervised Machine Learning

(5 Days)

The first section uncovers the advanced techniques in supervised machine learning, including ensemble methods and hyperparameter tuning.

Learning Outcomes

• Understand advanced supervised learning techniques
• Implement ensemble methods
• Conduct hyperparameter tuning in Python

Prerequisites

• Intermediate understanding of machine learning algorithms
• Familiarity with Python programming

Outline

• Days 1-2: Ensemble Methods
  • Random Forests
  • Gradient Boosting
  • Hands-On: Predicting Malaysian Election Outcomes
• Days 3-4: Hyperparameter Tuning
  • Grid Search
  • Random Search
  • Mini Project: Optimizing a Credit Scoring Model for Malaysian Banks
• Day 5: Advanced Techniques
  • Feature Engineering
  • Handling Imbalanced Data
  • Group Project: Fraud Detection in Malaysian Transactions

Unsupervised Machine Learning

(3 Days)

This section delves into unsupervised learning techniques such as clustering and dimensionality reduction, key for unearthing hidden patterns in data.

Learning Outcomes

• Understand unsupervised learning algorithms
• Perform clustering and dimensionality reduction
• Apply unsupervised learning on real-world datasets

Prerequisites

• Basic understanding of machine learning algorithms
• Familiarity with Python programming

Outline

• Day 1: Clustering Algorithms
  • K-Means Clustering
  • Hierarchical Clustering
  • Hands-On: Segmenting Malaysian Customer Data
• Day 2: Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • t-SNE
  • Mini Project: Feature Reduction in Malaysian Health Data
• Day 3: Advanced Techniques
  • Anomaly Detection
  • Recommender Systems
  • Group Project: Recommender System for Malaysian Tourist Attractions

Relational Database Design

(2 Days)

Understanding relational database design is crucial for effective data management. This section delves into complex database schema designs and best practices.

Learning Outcomes

• Design complex relational database schemas
• Implement best practices in database design
• Understand the role of relational databases in enterprise solutions

Prerequisites

• Basic understanding of SQL and databases

Outline

• Day 1: Advanced Schema Design
  • Normalization and Denormalization
  • Advanced SQL Functions
  • Hands-On: Designing a Database for a Malaysian eCommerce Platform
• Day 2: Best Practices
  • Database Integrity and Transactions
  • Database Security
  • Mini Project: Creating a Secure Database for a Malaysian Non-Profit Organization

NoSQL Essentials

(2 Days)

In this section, learn about NoSQL databases like MongoDB and Cassandra, and how they differ from traditional SQL databases.

Learning Outcomes

• Understand the basics of NoSQL databases
• Compare and contrast SQL and NoSQL databases
• Implement basic operations in a NoSQL database

Prerequisites

• Basic understanding of databases

Outline

• Day 1: Introduction to NoSQL
  • Types of NoSQL Databases
  • When to Use NoSQL
  • Hands-On: MongoDB for Storing Malaysian Social Media Data
• Day 2: Advanced NoSQL Topics
  • Data Modeling in NoSQL
  • Introduction to Cassandra
  • Mini Project: Implementing a NoSQL Database for Malaysian Weather Data

Big Data Analytics with Apache Spark

(2 Days)

This section introduces you to the world of Big Data analytics using Apache Spark, focusing on Spark's core functionalities and data processing capabilities.

Learning Outcomes

• Understand the architecture of Apache Spark
• Learn to perform data analytics tasks using Spark
• Explore Spark’s machine learning library

Prerequisites

• Intermediate understanding of Python or another programming language

Outline

• Day 1: Introduction to Apache Spark
  • Spark Architecture
  • RDDs and DataFrames
  • Hands-On: Analyzing Malaysian News Articles
• Day 2: Advanced Apache Spark
  • Spark MLlib for Machine Learning
  • Streaming Analytics
  • Group Project: Real-time Analysis of Malaysian Traffic Data

NLP Basics

(3 Days)

Natural Language Processing (NLP) is transforming how we interact with machines. This section, using Spacy, focuses on text analytics and NLP basics.

Learning Outcomes

• Understand the fundamentals of NLP
• Perform text analytics using Spacy
• Apply NLP in practical business cases

Prerequisites

• Familiarity with Python programming

Outline

• Day 1: Introduction to NLP and Spacy
  • Text Processing
  • Tokenization
  • Hands-On: Text Analytics of Malaysian News
• Day 2: Text Classification and Sentiment Analysis
  • Text Classification Algorithms
  • Sentiment Analysis
  • Mini Project: Malaysian Customer Review Analysis
• Day 3: Advanced NLP Techniques
  • Named Entity Recognition (NER)
  • Word Embeddings
  • Group Project: Identifying Key Entities in Malaysian Government Documents

Deep Learning (Using PyTorch)

(3 Days)

Deep learning has wide applications from autonomous vehicles to healthcare. Learn the basics of deep learning using PyTorch in this advanced section.

Learning Outcomes

• Understand the principles behind deep learning
• Learn to build neural networks using PyTorch
• Apply deep learning techniques in various domains

Prerequisites

• Intermediate understanding of machine learning algorithms
• Familiarity with Python programming

Outline

• Day 1: Introduction to Deep Learning and PyTorch
  • Neural Networks
  • PyTorch Basics
  • Hands-On: Simple Neural Network for Malaysian Currency Recognition
• Day 2: Convolutional Neural Networks (CNN)
  • CNN Architecture
  • Image Classification
  • Mini Project: Malaysian Wildlife Classification
• Day 3: Recurrent Neural Networks (RNN) and LSTM
  • RNN and LSTM Basics
  • Sequence Prediction
  • Group Project: Predicting Malaysian Stock Prices using LSTMs

NLP: Advanced Techniques Using Spacy

(3 Days)

Moving beyond basics, this section will cover advanced NLP techniques like topic modeling and machine translation using Spacy.

Learning Outcomes

• Understand advanced natural language processing techniques
• Apply topic modeling and machine translation
• Recognize the limitations and challenges in advanced NLP

Prerequisites

• Basic understanding of NLP and Spacy

Outline

• Day 1: Topic Modeling
  • LDA (Latent Dirichlet Allocation)
  • NMF (Non-negative Matrix Factorization)
  • Hands-On: Topic Modeling on Malaysian Political Speeches
• Day 2: Machine Translation
  • Sequence-to-Sequence Models
  • Language Models
  • Mini Project: Translating English to Bahasa Malaysia
• Day 3: Advanced Topics and Challenges
  • NLP Ethics
  • Text Summarization
  • Group Project: Summarizing Malaysian News Articles

Data Science Development Tools

(1 Day)

To round off the course, this section will introduce you to essential Data Science tools such as Git, Docker, and Jupyter notebooks, crucial for real-world applications.

Learning Outcomes

• Learn to manage code and data science projects with Git
• Understand containerization with Docker
• Familiarize with Jupyter Notebook for Data Science

Prerequisites

• Familiarity with Python and basic command-line operations

Outline

• Day 1: Introduction to Essential Tools
  • Git for Version Control
  • Introduction to Docker
  • Jupyter Notebook Best Practices
  • Hands-On: Setting Up a Malaysian Data Science Project using Git, Docker, and Jupyter