Muhammad Salman Malik DevOps Engineer
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• US$500
• Duration: 60 days(3 days/week)
• Delivery mode: Flexible as per the student
• Group size: 3
• Instruction language: English
• Certificate provided: No

Course Learning Outcomes:
 
o CLO1: Introduction to DevOps and Cloud Computing

o Introduction to DevOps culture and its principles
o Understanding the principles of DevOps and its relevance to data science
o Exploring common challenges in data science projects and how DevOps can address them
o Introduction to Agile methodologies for software development
o Overview of cloud computing and cloud providers
o Cloud computing (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid)
 
o CLO2: Practical Assignments for CLO1:

o Primer on DevOps, Infrastructure and Cloud Computing, comparison of different cloud deployment and computing models.
o Create an AWS free tier account and deploy a virtual machine using EC2.
o Create an Azure free tier account and deploy a virtual machine using Azure VM.
 
o CLO3: Infrastructure as Code

o Introduction to infrastructure as code (IaC)
o Infrastructure as code tools (Terraform, CloudFormation and Ansible)
o Version control tools (Git)
o What to include in git and what not (Managing dilemma of secrets committed into git)
 
o CLO4: Practical Assignment for CLO3:

o Develop an infrastructure as code (IaC) solution using Terraform to deploy an infrastructure on AWS (using free tier account)
o Local deployment and development environment using Docker
o Use Git and a Git repository to manage IaC code.
 
o CLO5: Containerization

o Introduction to containerization
o Docker / Container Runtime
o Kubernetes High Level Architecture
o Deploying and managing containers on cloud-based infrastructure
o Using Helm Charts and Static Manifests
o Scaling and orchestration of containers
 
o CLO6: Practical Assignment for CLO5

o Develop and deploy a containerized application packaged (dummy application, Hello World or Web Server) by creating a Helm Chart and deploy using Docker and Kubernetes on a local environment.
o Use Kubernetes to manage and orchestrate containers on a cloud-based infrastructure and scale multiple replicas of your deployment.
 
o CLO7: Continuous Integration and Deployment

o Introduction to CI/CD pipeline
o CI/CD tools (Jenkins, GitLab and GitHub Actions)
o Integration of Automated testing (unit, integration, end-to-end)
 
o CLO8: Practical Assignment for CLO7:

o Develop and implement a CI/CD pipeline using Jenkins to automate the build, tests and deployment of an application.
o Local development environment using Jenkins on Docker
o Develop and implement a testing strategy for an application integrated in a CI/CD workflow using automated tests.
 
o CLO9: Monitoring and Logging

o Understanding the importance of monitoring and logging in DevOps practices
o Introduction to monitoring tools like Nagios, Prometheus, and Grafana
o Logging and aggregation tools like Elasticsearch, Logstash, and Kibana (ELK)
o Benefits of using Elastic Stack
o Using Opensource fork by Amazon (OpenSearch).
 
o CLO10: Practical Assignment for CLO9

o Setting up a monitoring and logging system for an application deployed, create an alert (Email, Slack message or WhatsApp) and logs are available for the deployed workload using Kibana.
 
o CLO11: Security and Compliance

o Security best practices for DevOps
o Students will learn how to integrate security practices into their DevOps workflows, including threat modeling, secure coding practices, and continuous security testing.
o Securing cloud-based environments
o Students will explore cloud security concepts and tools, including access control, network security, and identity and access management. They will learn how to apply security measures to cloud infrastructure, as well as how to monitor and audit security events in the cloud.
o Securing data pipelines
o Students will learn about data privacy and security regulations, as well as techniques for securing data pipelines and ensuring data integrity. They will also learn how to use encryption and other security measures to protect sensitive data.
 
o CLO12: Quiz for CLO11

o On the basis of lectures and provided sources quiz will be conducted to facilitate discussion, communication and problem-solving both as a group and individually.
 
o CLO13: Design and Implement a Secure and Automated Data Pipeline (Cloud Ready)

o The final project aims to assess the knowledge and skills acquired during the course by designing and implementing a secure automated data pipeline on the cloud using the DevOps practices learned throughout the course.
o The project will require students to design and implement an end-to-end data pipeline (using Jenkins or anything else), starting from data ingestion, storage, processing, and analysis, to deployment and monitoring of the final product.
o The project should be implemented ready to be deployed for a cloud platform such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
o Local docker based development environment will be used (Creating a Docker image is a must)
o Additional marks for running whole infrastructure locally using Kubernetes
o The project will be assessed based on the quality of the design and implementation, the level of automation achieved, the effectiveness of the security measures, and the overall performance and usability of the final product.

• US$800
• Duration: 90 days
• Delivery mode: Flexible as per the student
• Group size: 6 - 10
• Instruction language: English
• Certificate provided: No

Week 1 - Introduction to Data Science
Day 1: Course introduction, syllabus review, expectations, assessment method
Day 2: What is data science, fields in data science, applications of data science
Day 3: Role of a data scientist, required skills, ethics in data science
Day 4: Data Science tools and libraries overview (Python, R, SQL, Excel)
Day 5: Introduction to Python for Data Science: basic syntax, data types, variables

Week 2 - Python for Data Science
Day 1: Control structures: loops, conditionals, functions
Day 2: Introduction to Libraries: NumPy and Pandas
Day 3: Data Structures: Lists, Dictionaries, Sets, Tuples
Day 4: File handling in Python, Reading and Writing files
Day 5: Lab session: Exercises and practical implementation

Week 3 - Data Wrangling with Python
Day 1: Introduction to Pandas, DataFrames and Series
Day 2: Importing data, cleaning data
Day 3: Data manipulation: filter, sort, groupby, merge
Day 4: Handling missing data, data formatting, data standardization
Day 5: Lab session: Exercises and practical implementation

Week 4 - Exploratory Data Analysis (EDA)
Day 1: Introduction to EDA, descriptive statistics
Day 2: Data visualisation with Matplotlib
Day 3: Data visualisation with Seaborn
Day 4: Correlation, Covariance, Outliers detection
Day 5: Lab session: EDA on different datasets

Week 5 - Introduction to SQL for Data Science
Day 1: Introduction to SQL, DDL, DML
Day 2: Basic SQL queries: SELECT, WHERE, ORDER BY
Day 3: SQL JOINs, UNION, Aggregations (GROUP BY, HAVING)
Day 4: SQL subqueries, functions
Day 5: Lab session: SQL exercises

Week 6 - Advanced SQL
Day 1: Database design: normalisation, keys, index
Day 2: Advanced SQL functions: analytical functions, stored procedures
Day 3: Working with large datasets in SQL
Day 4: Integrating SQL with Python
Day 5: Lab session: Advanced SQL exercises

Week 7 - Probability and Statistics for Data Science
Day 1: Introduction to Probability: Basic concepts, probability rules
Day 2: Probability Distributions: Binomial, Poisson, Normal distributions
Day 3: Introduction to Statistics, descriptive vs inferential statistics
Day 4: Statistical measures: mean, median, mode, variance, standard deviation
Day 5: Hypothesis testing, p-value, confidence interval

Week 8 - Introduction to Machine Learning
Day 1: What is Machine Learning, Types of Machine Learning
Day 2: Introduction to Supervised Learning: Regression, Classification
Day 3: Introduction to Unsupervised Learning: Clustering, Dimensionality Reduction
Day 4: Introduction to Reinforcement Learning
Day 5: Evaluating Machine Learning models: accuracy, precision, recall, F1-score, AUC-ROC

Week 9 - Regression Techniques in Machine Learning
Day 1: Simple Linear Regression
Day 2: Multiple Linear Regression
Day 3: Polynomial Regression
Day 4: Logistic Regression
Day 5: Lab session: Regression exercises using sklearn

Week 10 - Classification Techniques in Machine Learning
Day 1: K-Nearest Neighbors (KNN)
Day 2: Support Vector Machine (SVM)
Day 3: Decision Trees and Random Forest
Day 4: Naive Bayes Classifier
Day 5: Lab session: Classification exercises using sklearn

Week 11 - Advanced Topics in Data Science
Day 1: Introduction to Big Data: Hadoop, Spark
Day 2: Introduction to Cloud Computing in Data Science: AWS, Google Cloud, Azure
Day 3: Overview of Natural Language Processing (NLP)
Day 4: Overview of Computer Vision, Convolutional Neural Networks
Day 5: Introduction to Time Series Analysis, ARIMA

Week 12 - Final Project Work
Final Project: Applying the techniques learned throughout the course to solve a real-world data science problem.