Module 1:

The command line

Bash scripting
Navigation
Essential Commands and syntax
Finding help

Module 2:

Git and Github

Essential commands and syntax
Version control
Branching
Pull requests
Software collaboration best practices

Module 3:

Python Programming

The Python Environment
Debugging
Arithmetic Variable Assignment and Strings
Lists and Sets
Dictionaries, Tuples and Operators
Control Flow
Loops
Functions
Object Oriented Programming
Advanced Python
Error Handling

Module 4:

Data Formats and Processing Libraries

Numpy
JSON, CSV, XLSX and YAML
Intro to Pandas

Module 5:

APIs & Webscraping

Web protocols and requests
APIs
Webscraping with Selenium

Module 6:

Algorithms and Data Structures

Big O Notation
Sorting and Searching
Linked Lists
Stacks and Queues
Trees and Graphs
Dynamic Programming

Module 7:

Software Design and Testing

Principles of OOP Design
Inheritance, Polymorphism, Abstraction, Encapsulation
Class Decorators
Docstring and Typing
Testing
Project Strcucture
Code Review

Module 8:

SQL

PgAdmin4
CRUD
JOINs
Aggregations
CTEs
Psycopg2
SQLAlchemy

Module 9:

Containerisation with Docker

Creating Docker Containers
Docker Networking and Storage
Monitoring with Prometheus and Grafana

Module 10:

Essential Cloud Technology

The AWS CLI and Python SDK (boto3)
Virtual Compute with AWS EC2
Data Lake Storage on AWS S3
AWS RDS for Data Warehouse Storage

Module 11:

CI-CD

Github Actions

Module 1:

Big Data Engineering Foundations

Introduction
Big data ecosystem overview
Batch vs real-time processing
Structured, unstructured and complex data
The data engineering lifecycle

Module 2:

Data Ingestion

Principles of data ingestion
Batch processing
Real-time data processing
Kafka
Flume

Module 3:

Data Management

Data Governance
Data Quality
Reference Data Management
Metadata Management
Challenges and Risks
Data Fabric
Data Quality and Cleaning
Data Enrichment
Big Data Privacy and Security

Module 4:

Data Wrangling and Transformation

ELT/ETL
SQL
NoSQL
Cassandra/MongoDB
Distributed Processing with Spark
Spark Streaming

Module 1:

Data Cleaning and Exploratory Data Analysis

Data Visualisation
Multicollinearity
Influential points - Leverages and Outliers

Module 2:

Statistical testing

Significance testing
A/B Testing

Module 3:

Introduction to machine learning

Data for ML
Intro to models - Linear Regression
Validation and Testing
Gradient Based Optimisation
Bias and Variance
Hyperparameters, Grid Search and K-Fold Cross Validation

Module 4:

Classification

Binary Classification
Multiclass Classification
Multilabel Classification

Module 5:

Theory

Maximum Likelihood Estimation
Evaluation Metrics

Module 6:

Popular Supervised Models

K-Nearest Neighbours
Classification Trees
Support Vector Machines
Regression Trees

Module 7:

Ensembles

Ensembles
Random Forests and Bagging
Boosting and Adaboost
Gradient Boosting
XGBoost

Module 8:

Neural Networks

Neural networks
Dropout
Batch Normalisation
Optimisation for deep learning
Convolutional Neural Networks (CNNs)
ResNets

Module 1:

Data Wrangling and Cleaning

Data loading
Data cleaning
Data integration
Data exporting

Module 2:

PostgreSQL RDS Data Import and Reporting

Conneting to pgAdming4
Creating databases and tables
Importing data
Data exploration and statistical analysis

Module 3:

Integrate Tableau Desktop with PostgreSQL RDS

Setting up Tableau Desktop
Configuring PostgreSQL connector
Connecting to databases

Module 4:

Create Tableau Reports

Tableau data exploration
Data analysis and visualisation
Creating reports

Module 1:

Introduction to machine learning

Data for ML
Intro to models - Linear Regression
Validation and Testing
Gradient Based Optimisation
Bias and Variance
Hyperparameters, Grid Search and K-Fold Cross Validation

Module 2:

Classification

Binary Classification
Multiclass Classification
Multilabel Classification

Module 3:

PyTorch

Automatic differentiation
PyTorch Datasets and DataLoaders
Making custom datasets

Module 4:

Neural Networks

Neural networks
Dropout
Batch Normalisation
Optimisation for deep learning
Convolutional Neural Networks (CNNs)
ResNets

Module 5:

Practical

Architecture tips, data augmentation & debugging tips
Pre-trained models
Transfer learning
Hardware acceleration (GPUs & TPUs)

Module 6:

Applications

Churn modelling
Content based recommendation systems
Collaborative filtering
Lead scoring

Module 7:

Building APIs

Intro to FastAPI
Deploying FastAPI
Efficient FastAPI

Module 8:

Kubernetes (K8s)

KubeCTL
Workloads
Networking
Storage
StatefulSets
Deploying K8s

Module 9:

Kubeflow

Kubeflow Core
Kubeflow Serving
Kubeflow Pipelines

Module 10:

Apache Airflow

Scheduling with Airflow