UCSD: Introduction to Big Data

• Big Data: Why and Where
• Characteristics of Big Data and Dimensions of Scalability
• Data Science: Getting Value out of Big Data
• Foundations for Big Data Systems and Programming
• Systems: Getting Started with Hadoop

UCSD: Big Data Modelling and Management Systems

• Introduction to Big Data Modelling and Management
• Big Data Modelling
• Big Data Modelling (Part 2)
• Working with Data Models
• Big Data Management: the “M” in DBMS
• Designing a Big Data Management System for an Online Game

UCSD: Big Data Integration and Processing

• Retrieving Big Data (Part 1)
• Retrieving Big Data (Part 2)
• Big Data Integration
• Processing Big Data
• Big Data Analytics using Spark
• Learn by Doing: Putting MongoDB and Spark to Work

UCSD: Machine Learning with Big Data

• Introduction to Machine Learning with Big Data
• Data Exploration
• Data Preparation
• Classification
• Evaluation of Machine Learning Models
• Regression, Cluster Analysis, and Association Analysis

UCSD: Graph Analytics for Big Data

• Welcome to Graph Analytics
• Introduction to Graphs
• Graph Analytics
• Graph Analytics Techniques
• Computing Platforms for Graph Analytics

UCSD: Big Data Capstone Project

• Simulating Big Data for an Online Game
• Acquiring, Exploring, and Preparing the Data
• Data Classification with KNIME
• Clustering with Spark
• Graph Analytics of Simulated Chat Data with Neo4j
• Reporting and Presenting your Work
• Final Submission

UC Davis: SQL for Data Science

• Getting Started and Selecting & Retrieving Data with SQL
• Filtering, Sorting, and Calculating Data with SQL
• Subqueries and Joins in SQL
• Modifying and Analysing Data with SQL

UC Davis,: SQL Problem Solving

• Data of Unknown Quality
• Creating Clean Datasets
• SQL Problem Solving
• Case Study: AB Testing

UC Davis: SQL for Data Science Capstone Project

• Getting Started and Milestone 1: Project Proposal and Data Selection/Preparation
• Milestone 2: Descriptive Stats and Understanding Your Data
• Milestone 3: Beyond Descriptive Stats (Dive Deeper/Go Broader)
• Milestone 4: Presenting Your Findings (Storytelling)

HarvardX: CS50’s Introduction to Computer Science

HarvardX: CS50’s Introduction to Programming with Python

HarvardX: CS50’s Introduction to Programming with Python

HarvardX: Fat Chance: Probability from the Ground Up

• Basic Counting
• Advanced Counting
• Basic Probability
• Expected Value
• Conditional Probability
• Bernoulli Trials
• The Normal Distribution

HarvardX: Introduction to Data Science with Python

• Linear Regression
• Multiple and Polynomial Regression
• Model Selection and Cross-Validation
• Bias, Variance, and Hyperparameters
• Classification and Logistic Regression
• Multi-logstic Regression and Missingness
• Bootstrap, Confidence Intervals, and Hypothesis Testing
• Capstone Project

U.Mich: Python Debugging: A Systematic Approach

• Review and Setup - Jupyter environment
• The Debugging Framework
• Framework Skills
• Stop Bugs Before they Happen

U.Mich: NumPy and Pandas Basics for Future Data Scientists

• Introduction to Numpy Arrays and Basic Operations
• Advanced Numpy Array Manipulations and Operation
• Mastering Pandas for Data Science
• Advanced Data Handling and Analysis with Pandas

U.Mich: Statistics with Python Using NumPy, Pandas, and SciPy

• Vector Operations and Text Representation in Data Science
• Understanding and Visualising Data Distributions
• Understanding and Analyzing Data Distribution Characteristics
• Sampling Methods and Statistical Inference

U.Mich: Python Debugging Capstone Project: Fixing and Extending Code

• Project Briefing
• Updated Requirements and Final Deliverable

U.Mich: Introduction to Data Science in Python

• Fundamentals of Data Manipulation with Python
• Basic Data Processing with Pandas
• More Data Processing with Pandas
• Answering Questions with Messy Data

U.Mich: Applied Plotting, Charting, and Data Representation in Python

• Principles of Information Visualisation
• Basic Charting
• Charting Fundamentals
• Applied Visualisations

U.Mich: Applied Machine Learning in Python

• Fundamentals of Machine Learning - Intro to SciKit Learn
• Supervised Machine Learning - Part 1
• Evaluation
• Supervised Machine Learning - Part 2

U.Mich: Applied Text Mining in Python

• Working with Text in Python
• Basic Natural Language Processing
• Classification of Text
• Topic Modelling

U.Mich: Applied Social Network Analysis in Python

• Why Study Networks and Basics on NetworkX
• Network Connectivity
• Influence Measures and Network Centralisation
• Network Evolution

IBM: What is Data Science?

IBM: Tools for Data Science

IBM: Data Science Methodology

Databases and SQL for Data Science with Python

University of London/IBM: The Data Science Profession – Student View

University of London/IBM: What is Data Science?

University of London/IBM: Tools for Data Science

University of London/IBM: Problems, Algorithms and Flowcharts

University of London/IBM: Python for Data Science, AI & Development

University of London/IBM: Statistics and Clustering in Python

University of London/IBM: Data Science Project Capstone: Predicting Bicycle Rental

University of London/IBM: Python Project for Data Science

IBM: Data Engineering Basics for Everyone

• What is Data Engineering
• Data Engineering Ecosystem
• Data Engineering Lifecycle
• Career Opportunities and Learning Paths

IBM: Python Basics for Data Science

• Python Basics
  
• Python Data Structures
• Python Programming Fundamentals
• Working with Data in Python
• Working with Numpy Arrays

IBM: Python for Data Engineering Project

• Extract, Transform, Load (ETL)

IBM: Relational Database Basics

• Relational Database Concepts
• Using Relational Databases
• MySQL and PostgreSQL
• Database Design Project

IBM: SQL for Data Science

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IBM: SQL Concepts for Data Engineers

• Additional SQL

IBM: Data Engineering Basics for Everyone

• What is Data Engineering
• Data Engineering Ecosystem
• Data Engineering Lifecycle
• Career Opportunities and Learning Paths

IBM: Python Basics for Data Science

• Python Basics
  
• Python Data Structures
• Python Programming Fundamentals
• Working with Data in Python
• Working with Numpy Arrays

IBM: Python for Data Engineering Project

• Extract, Transform, Load (ETL)

IBM: Relational Database Basics

• Relational Database Concepts
• Using Relational Databases
• MySQL and PostgreSQL
• Database Design Project

IBM: SQL for Data Science

IBM: SQL Concepts for Data Engineers

• Additional SQL

IBM: Linux Commands & Shell Scripting

• Introduction to Linux Commands and Shell Scripting

IBM: Relational Database Administration (DBA)

• Introduction to Database Management
• Managing Databases
• Monitoring and Optimization
• Troubleshooting and Automation
• Final Project

IBM: Building ETL and Data Pipelines with Bash, Airflow and Kafka

IBM: Data Warehousing and BI Analytics

• Data Warehouses, Data Marts, and Data Lakes
• Designing, Modeling and Implementing Data Warehouses
• Data Warehouse Analytics
• Final Assignment

IBM: NoSQL Database Basics

• Introducing NoSQL
• Introducing MongoDB – An Open-Source NoSQL Database
• Introducing Apache Cassandra – An Open-Source NoSQL Database
• Introducing IBM Cloudant – A NoSQL DBaaS
• Final Project – Working with NoSQL Databases

IBM: Big Data, Hadoop, and Spark Basics

• What is Big Data?
• Introduction to the Hadoop Ecosystem
• Introduction to Apache Spark
• DataFrames and SparkSQL
• Development and Runtime Environment options
• Monitoring & Tuning

IBM: Apache Spark for Data Engineering and Machine Learning

• Spark for Data Engineering
• Spark ML for Machine Learning
• Final Project

IBM: Data Engineering Capstone Project

Introduction to Data Engineering📅 2025-04-1

Python for Data Science, AI & Development

Python Project for Data Engineering

Introduction to Relational Databases (RDBMS)

Databases and SQL for Data Science with Python

Hands-on Introduction to Linux Commands and Shell Scripting

Relational Database Administration (DBA)

ETL and Data Pipelines with Shell, Airflow and Kafka

Data Warehouse Fundamentals

BI Dashboards with IBM Cognos Analytics and Google Looker

Introduction to NoSQL Databases

Introduction to Big Data with Spark and Hadoop

Machine Learning with Apache Spark

Data Engineering Capstone Project

Generative AI: Elevate your Data Engineering Career

Data Engineering Career Guide and Interview Preparation

Google: Foundations: Data, Data, Everywhere

• Introducing data analytics and analytical thinking
• The wonderful world of data
• Set up your data analytics toolbox
• Become a fair and impactful data professional

Google: Ask Questions to make Data-Driven Decisions

• Ask effective questions
• Make data-driven decisions
• Spreadsheet magic
• Always remember the stakeholder

Google: Prepare Data for Exploration

• Data types and structures
• Data responsibility
• Database essentials
• Organise and protect data
• Engage in the data community

Google: Process Data from Dirty to Clean

• The importance of integrity
• Clean data for more accurate insights
• Data cleaning with SQL
• Verify and report on cleaning results
• Add data to your resume

Google: Analyse Data to Answer Questions

• Organise data for more effective analysis
• Format and adjust data
• Aggregate data for analysis
• Perform data calculations

Google: Share Data Through the Art of Visualisation

• Visualise data
• Create data visualisations with Tableau
• Craft data storeis
• Develop presentations and slideshows

Google: Data Analysis with R Programming

• Programming and data analytics
• Programming with RStudio
• Working with data in R
• More about visualisations, aesthetics, and annotations
• Documentation and reports

Google: Data Analyitcs Capstone Case Study

• Learn about capstone basics
• Build your portfolio
• Use your portfolio
• Put your certificate to work

Google: Foundations of Data Science

• Introduction to data science conepts
• The impact of data today
• Your career as a data professional
• Data applications and workflow

Google: Get Started with Python

• Hello, Python
• Functions and conditional statements
• Loops and strings
• Data structures in Python

Google: Go Beyond the Numbers: Translate Data into Insights

• Find and share stories using data
• Explore raw data
• Clean your data
• Data visualisations and presentations

Google: The Power of Statistics

• Introduction to statistics with Python
• Probability
• Sampling
• Confidence intervals
• Introduction to hypothesis testing

Google: Regression Analysis: Simplify Complex Data Relationships

• Introduction to Complex Data Relationships
• Simple linear regression
• Multiple linear regression
• Advanced hypothesis testing
• Logistic regression

Google: The Nuts and Bolts of Machine Learning

• The different types of machine learning
• Workflow for building complex models
• Unsupervised learning techniques
• Tree-based modeling

Google: Advanced Data Analytics Capstone

• Capstone Project
• Data-focused career resources
• Put your Advanced Data Analytics Certificate to work