PCDE Course Outline
Introduction
This is an index of sorts to course content and related notes to this course organized by module. For an overview of the course, see the PCDE Course Overview.
Module 0: Course Orientation
Notes Links
- PCDE Course Orientation
- Note-taking Strategies
- Notetaking Strategies for Lectures
- Time Management Strategies Notes
- Time Blocking Strategies Notes
Key Activities
- Course Introduction
- Learning Platform Overview
- Introduce Yourself
- Course Agreement
- Install Tools Needed for Modules 1-3
Module 1: Introduction to Python
Notes Links
Learning Outcomes
- Starts: 2022-12-07
- Due: 2022-12-14
- Compare Python basic data types and operators.
- Create basic Python data types in a coding environment.
- Identify lists, tuples, sets, and dictionaries in Python.
- Create Python lists, tuples, sets, and dictionaries in a coding environment.
- Use indexing and slicing in Python.
- Interpret memory allocation for Python objects.
- Define loops and conditionals in a Python coding environment.
- Integrate loops and conditionals in a Python coding environment.
- Define Python functions and variable scope.
- Use Python functions in a coding environment.
- Interpret Python classes.
- Read and write files in Python.
Key Activities
- Discussions
- Activities
- Knowledge Checks
- Coding Assignment
Module 2: Introduction to NumPy
Notes on Topic
Learning Outcomes
- Create NumPy arrays, functions, and multidimensional arrays.
- Define NumPy arrays, functions, and multidimensional arrays.
- Interpret NumPy memory allocation.
- Describe basic probability concepts.
- Explain the connection between histograms and probability densities.
- Differentiate between discrete and continuous distributions.
- Define probability density functions and probability distribution functions.
- Create discrete and continuous distributions.
- Define Matplotlib graphs.
- Visualize data using Matplotlib graphs.
- Interpret data using Matplotlib graphs.
Module 3: Introduction to Pandas
Learning Outcomes
- Define pandas series and dataframes
- Implement pandas series and dataframes
- Perform data cleaning in pandas
- Prepare data using one-hot encoding in pandas
- Explain time and data functionality in pandas
- Analyze data in pandas
- Design dataframes in pandas
Note Links
Module 4: Databases & Intro to SQL
- Notes on topic
Module 5: Databases with SQL Statements
Notes on Topic
Key Activities
- Discussions
- Activities
- Knowledge Checks
- Coding Assignment
Outcomes
- Outline big data and database systems.
- Design databases conceptually and formally.
- Interpret database components.
- Correlate databases.
- Interpret cardinality and normalization of tables.
- Design physical components of databases.
- Define a database in a coding environment.
- Manipulate a database in a coding environment.
- Explain database data types and indexing.
- SQL Tutorial - Full Database Course for Beginners (from FreeCodeCamp)
- Workbench Files (from mysql.com)
- MySQL Workbench Tutorial (on Youtube)
- MySQL Workbench Video Walkthrough (by Telusko on Youtube)
Module 6: Databases Analysis and the Client Server Interface
Notes on Topic
- Course materials
- SQL Notes
- Exploratory Data Analysis (EDA) in SQL
- Visualizing Data in SQL
- Cleaning Data in SQL
- Dates & Time in SQL
- Client Server Architecture Overview
Key Activities
- Discussions: 2
- Activities: 5
- Self Study Drag & Drop: 2
- Knowledge Checks: 7
- Coding Assignment: 1
- Video Lectures: 25
- Mini Lessons: 5
- Estimated 17.5hrs to complete
Time Log
- 23-01-26: 4.5hrs
Outcomes
- Write functional queries to explore a database.
- Analyze the structure of a database.
- Create visualizations of data using histograms in SQL.
- Clean a dataset in SQL.
- Handle date and time in SQL.
- Define the client-server interface.
- Read and write tables using a driver.
- Discriminate between RDBMS and in-memory databases.
Module 7: A Model to Predict Housing Prices
Due Date: 1629 UTC February 8, 2023 Available for late submission till: February 22, 2023
Notes on Topic
Key Activities
- Discussions: 4
- Activities: 0
- Self Study Drag & Drop: 0
- Knowledge Checks: 3
- Coding Assignment: 1 (PROJECT)
- Video Lectures: 6 LONG LECTURES
- Mini Lessons: 0
- Estimated 18hrs to complete
- Divided by 7 days & 40% overshoot = 4hrs/day
Outcomes
- Describe how descriptive statistics are used in Python.
- Explain central limit theorem and correlation.
- Describe how to calculate a linear regression.
- Write Markdown syntax.
- Build a prediction model using linear regression.
Module 8: ETL, Analysis, Visualization
Due Date: 4:29 PM UTC February 15, 2023 Available for late submission till: February 22, 2023
Notes on Topic
Key Activities
- Discussions: 4
- Activities: 0
- Self Study Drag & Drop: 0
- Knowledge Checks: 3
- Coding Assignment: 1 (PROJECT)
- Video Lectures: 6 LONG LECTURES
- Mini Lessons: 0
- Estimated 18hrs to complete
- Divided by 7 days & 40% overshoot = 4hrs/day
Outcomes
- Describe how descriptive statistics are used in Python.
- Explain central limit theorem and correlation.
- Describe how to calculate a linear regression.
- Write Markdown syntax.
- Build a prediction model using linear regression.
Module 9: GitHub & Advanced Python
Notes on Topic
- Module 9 Materials
- VS Code
- Git
- GitHub
- Python: Classes
- Python: Advanced Functions
- Python: Decorators
- Python: Wrappers
Key Activities
- Discussions: 1
- Activities: 6
- Self Study: 2
- Knowledge Checks: 4
- Coding Assignment: 1
- Video Lectures: 90 minutes
- Mini Lessons: 0
Outcomes
- Debug Python code.
- Use GitHub for version control.
- Create a portfolio using GitHub Pages.
- Implement Python classes.
- Write code using advanced Python functions.
- Utilize Python decorators and wrappers.
Module 10: Networks
Outcomes
- Learn about how computer networks work
- HTTP
- Postman
- Strapi
- API
Notes on Topic
- CLI: Command Line Interface
- GNU CoreUtils
- Computer Networks
- HTTP: Hypertext Transport Protocol
- HTTP Headers
- Software Containers
- Docker
- VS Code
- Postman
- Swagger
Module 11: Client Server Architecture
Note Links
Outcomes
In this module these topics will be covered:
- Cookies & session cookies
- How session cookies protect API (application programming interface) routes
- How swagger can be used to detail an API
- Developing a Swagger interface
- Writing a flask Server
- Handling security tokens
- Kerberos to understand the need of security tokens
- PKI (public key infrastructure)
- Signing documents using private keys
- Passing public key into Github
The most difficult part of this section is correctly generating secure tokens for authentication, getting it wrong can mean loss of access to data or worse leaking data by an attacker.
Module 12: Types of Databases & Database Containerization
Due Data
- Due Wednesday, March 22, 2023 at 4:29 PM UTC
Note Links
- Python
- Types of Databases
- Relational Databases
- Document Databases
- MongoDB Using Python
- Key-Value Databases
- Distributed Databases
- Cassandra (Distributed Database)
Outcomes
- Describe applications of various types of databases.
- Identify key concepts related to database containerization.
- Update and delete data in different types of containerized databases.
- Identify key concepts related to different types of databases.
Module 13: Change Data Capture (CDC)
Due Data
- Due Wednesday, April 5, 2023 at 4:29 PM UTC
Note Links
Outcomes
- Identify key concepts of change data capture (CDC) systems.
- Create and delete containers Python.
- Implement time loops.
- Identify applications of time loops.
- Initialize database containers.
- Perform CDC on a variety of containers.
Module 14: Java & Debezium
Due Data
- Due Wednesday, April 12, 2023 at 4:29 PM UTC
Note Links
Outcomes
- Define elements of the Java programming language.
- Write basic Java code.
- Compare and contrast Java and Python.
- Upload and download files to and from containers.
- Develop a web application in Java.
- Identify key characteristics of Java applications.
- Set up a network for Debezium in Docker.
- Connect a database to Debezium.
- Identify key elements of Debezium.
Module 15: Advanced Python and Web Applications
Due Date
- Due Wednesday, April 19, 2023 at 4:29 PM UTC
- 5:29 PM CET
- 11:29 AM EST
- 6:29 PM EAT
Activities
Key Activities
- Knowledge Checks
- Knowledge Check 15.1: Authorization Servers
- Knowledge Check 15.2: Relational Databases in Python
- Knowledge Check 15.3: Redundant Dictionaries in Python
- Discussion
- Discussion 15.1: Applications of Authorization Servers
- Project Assignments
- Project 15.1: Project 1: Creating a Books Web Application
- Project 15.2: Project 2 Part 1: Creating a Student Grades Database
- Project 15.3: Project 2 Part 2: Redundant Dictionaries in Python
Self-Study Activities
- Self-Study Discussion 15.2: Thinking Like a Data Scientist: Using Advanced Python Programming to Create Web Applications
- Self-Study Flashcards: Module 15 Flashcards
Module 16: Transit Data & APIs
Module 16: Due Date
- Due Wednesday, April 26, 2023 at 1629 UTC
- 1829 CET
- 1929 EAT
- 1229 EST
Module 16: Goals
- Describe use cases of location-based applications
- Define web development tools for building an application
- Identify key components of Mapbox
- Build a transit data application
Module 16: Activities
Module 16: Key Activities
- Knowledge Checks
- Knowledge Check 16.1: Location-Based Applications
- Knowledge Check 16.2: Mapbox
- Discussion
- Discussion 16.1: Use Cases of Location-Based Applications
- Project
- Project 16.1: Build a Transit Data Application
Module 16: Self-Study Activities
- Self-Study Discussion 16.2: Thinking Like a Data Scientist: Transit Data and APIs
- Self-Study Flashcards: Module 16 Flashcards
Module 16: Related Notes
- PCDE Module 16 Content
- PCDE Project 16: Build a Transit Data Application
- Mapbox
- MySQL
- Maven (Java Build Tool)
- JavaScript Object Notation (JSON)
- Change Data Capture (CDC)
- Debezium
- CSS
- MBTA API
Module 17: Performing ETL Using NiFi
Module 17: Due Date
- Due Wednesday, May 10, 2023 at 1629 UTC
- 1829 CET
- 1229 EST
Module 17: Goals
- Identify use cases of ETL in data engineering.
- Identify basic elements of NiFi.
- Identify other Apache ETL tools and discuss their pros & cons.
- Use NiFi to create an ETL pipeline.
Module 17: Activities
Module 17: Key Activities
- Knowledge Checks
- Knowledge Check 17.1: Basics of NiFi
- Discussion
- Discussion 17.1: Use Cases of ETL in Data Engineering
- Discussion 17.2: Pros and Cons of Apache ETL Tools
- Activities
- Activity 17.1: Activity 17.1: Using NiFi to Connect to an Excel Spreadsheet
- Activity 17.2: Activity 17.2: Using NiFi to Create a Pipeline in MySQL
- Activity 17.3: Activity 17.3: Using NiFi to Create a Pipeline in MongoDB
- Activity 17.4: Activity 17.4: Using NiFi to Create a Pipeline in Redis
- Activity 17.5: Activity 17.5: Using NiFi to Create a Pipeline in Cassandra
- Final Assignment
- Module 17 Final Assignment: Performing ETL Using NiFi
Module 17: Self-Study Activities
- Self-Study Drag & Drop Activity 17.2: Using NiFi to Create a Pipeline in various databases
- Self-Study Discussion 17.3: Thinking Like a Data Scientist: Performing ETL Using NiFi
- Self-Study Flashcards: Module 17 Flashcards
Module 17: Related Notes
- PCDE Module 17 Content
- NiFi (ETL Pipeline Software by Apache)
- Extract, Transform, Load (ETL)
- MySQL
- MacOS (Operating System)
- Windows (Operating System)
Module 18: Platforms for Handling Big Data
Module 18: Due Date
- Due Wednesday, May 17, 2023 at 1629 UTC
- 1829 CET
- 1229 EST
Module 18: Goals
- Discuss the importance of big data.
- Identify key components of big data.
- Identify key components of Hadoop architecture.
- Set up Hadoop in a Docker container.
- Utilize Hadoop to handle big data.
- Identify key components of the Hadoop ecosystem.
- Describe applications of Hadoop.
- Write a Java program to access the Hadoop database.
Module 18: Activities
Module 18: Key Activities
- Knowledge Checks
- Knowledge Check 18.1: Big Data and the Architecture of Hadoop
- Knowledge Check 18.2: Advanced Hadoop Architecture
- Discussion
- Discussion 18.1: The Importance of Big Data
- Discussion 18.2: Exploring the Hadoop Ecosystem
- Discussion 18.3: Use Cases of Hadoop
- Activities
- Activity 18.1: Setting Up Hadoop in a Docker Container
- Activity 18.2: Executing the WordCount Program
- Final Assignment
- Module 18 Final Assignment: Writing a Java Program to Access the Hadoop Database
Module 18: Self-Study Activities
- Self-Study Discussion 18.4: Thinking Like a Data Scientist: Platforms for Handling Big Data
- Self-Study Flashcards: Module 18 Flashcards
Module 18: Related Notes
Module 19: Processing Big Data with Spark and Airflow
Module 19: Due Date
- Due Wednesday, May 14, 2023 at 1629 UTC
- 1829 CET
- 1229 EST
Module 19: Goals
- Describe how scalable solutions address challenges of big data.
- Use Docker to create and manipulate Spark images and containers.
- Use PySpark to query data.
- Identify key components of Spark and Airflow.
- Identify use cases for Spark and Airflow.
- Create a workflow in Airflow.
Module 19: Activities
Module 19: Key Activities
- Knowledge Checks
- Knowledge Check 19.1: Spark
- Knowledge Check 19.2: Airflow
- Discussion
- Discussion 19.1: Addressing Challenges of Big Data with Scalable Solutions
- Discussion 19.2: Use Cases for Spark
- Discussion 19.3: Use Cases for Airflow
- Activities
- Activity 19.1: Creating Spark Docker Images and Containers
- Activity 19.2: Loading Data into a Spark Docker Container
- Activity 19.3: Using PySpark to Query Data
- Activity 19.4: Creating a Workflow in Airflow
- Final Assignment
- Module 19 Final Assignment: Processing Big Data with Spark and Airflow
Module 19: Self-Study Activities
- Self-Study Discussion 19.4: Thinking Like a Data Scientist: Processing Big Data with Spark and Airflow
- Self-Study Flashcards: Module 19 Flashcards
Module 19: Related Notes
- PCDE Module 19 Content
- Spark
- Airflow (Data Workflow and Scheduling)
- Docker (Container Runtime)
- PySpark (Python Spark Library)
- Containers (Software)
Module 20: Introduction to Machine Learning
Module 20: Due Date
- Due Wednesday, May 31, 2023 at 1629 UTC
- 1829 CET
- 1229 EST
Module 20: Goals
- Solve advanced mathematical problems.
- Describe use cases of linear regression.
- Apply gradient descent to reduce error.
- Explain the importance of optimization in gradient descent
- Describe applications of Bayes Theorem.
- Implement spam detection using Python.
- Identify use cases for Naive Bayes and Gaussian Naive Bayes theorems.
- Implement Naive Bayes theorem using Sci-Kit Learn.
- Implement Gaussian Naive Bayes theorem using Sci-Kit Learn.
Module 20: Activities
- Knowledge Checks
- Knowledge Check 20.1: Mathematics Fundamentals
- Knowledge Check 20.2: Gradient Descent
- Knowledge Check 20.3: Advanced Probability
- Discussion
- Discussion 20.1: Use Cases of Linear Regression
- Discussion 20.2: The importance of Optimizing Gradients
- Discussion 20.3: Applications of Bayes Theorem
- Discussion 20.4: Use cases for Naive Bayes and Gaussian Naive Bayes Theorems
- Activities
- Activity 20.1: Optimizing Gradient Descent Using Learning Rates
- Activity 20.2: Using Python for Spam Detection
- Final Assignment
- Module 20 Final Assignment: Implementing Naive Bayes and Gaussian Naive Bayes Classifiers Self-Study Activities
- Self-Study Discussion 20.5: Thinking Like a Data Scientist: Machine Learning and Advanced Probability
- Self-Study Flashcards: Module 20 Flashcards
Module 20: Related Notes
- PCDE Course: Module 20 Content
- Matrix
- Gradient Descent
- Bayes Theory
- SciKit-Learn (Python Machine Learning Library)
Module 21: Introduction to Reinforcement Learning and Deep Neural Networks
Module 21: Due Date
- Due Wednesday, June 14, 2023 at 1629 UTC
- 1829 CET
- 1229 EST
Module 21: Goals
- Discuss applications of machine learning algorithms.
- Implement k-means using Scikit-learn.
- Identify key components of the k-means algorithm.
- Discuss use cases for reinforcement learning.
- Implement the Quality matrix and the Bellman equation.
- Implement the fundamental steps of reinforcement learning.
- Identify key components of reinforcement learning and deep neural networks.
Module 21: Activities
- Knowledge Checks
- Knowledge Check 21.1: The K-Means Algorithm and Machine Learning Algorithms
- Knowledge Check 21.2: Reinforcement Learning and Deep Neural Networks
- Discussion
- Discussion 21.1: Applications of Machine Learning Algorithms
- Discussion 21.2: Use Cases for Reinforcement Learning
- Activities
- Try-It Activity 21.1: Experimenting with Reinforcement Learning
- Activity 21.1: Reinforcement Learning Activity
- Codio Coding Activity 21.1: Coding K-Means Using Scikit-Learn
- Final Assignment
- Module 21 Final Assignment: Implementing the K-Means Algorithm and Reinforcement Learning Algorithm Self-Study Activities
- Self-Study Discussion 21.3: Thinking Like a Data Scientist: Reinforcement Learning and Deep Neural Networks
Module 21: Related Notes
- PCDE Course: Module 21 Content
- Machine Learning
- Reinforcement Learning
- Neural Network
- TensorFlow
- K-Means Clustering
- Confusion Matrix (Machine Learning)
Module 22: Processing and Streaming Big Data
Module 22: Due Date
- Due Wednesday, June 21, 2023 at 1629 UTC
- 1829 CET
- 1229 EST
Module 22: Goals
- Compare applications of the Parquet and Feather formats for reading and writing big data.
- Run parallel operations in DASK.
- Discuss use cases for parallel computing.
- Identify key concepts of DASK and parallel computing.
- Discuss use cases of web sockets.
- Stream data through web sockets.
Module 22: Activities
- Knowledge Checks
- Knowledge Check 22.1: DASK and Parallel Computing
- Discussion
- Discussion 22.1: Use Cases for Parallel Computing
- Discussion 22.2: Use Cases for Web Sockets
- Activities
- Activity 22.1: Using DASK to Create Multiple Files in Parallel
- Activity 22.2: Using DASK to Read and Analyze Multiple Files in Parallel
- Activity 22.3: Simulating Parallel Processing
- Activity 22.4: Streaming Web Sockets
- Try-It Activities
- Try-It Activity 22.1: Reading Big Data
- Try-It Activity 22.2: Running Parallel Operations in DASK
- Final Assignment
- Final Assignment 22.1: Part 1: Parallel Computing with Pandas, NumPy, and DASK
- Final Assignment 22.1: Part 2: Streaming Data Using Web Sockets Self-Study Activities
- Self-Study Drag & Drop Activity 22.1: Parallel Computing and Web Sockets
- Self-Study Discussion 22.3: Thinking Like a Data Scientist: Processing and Streaming Big Data
- Self-Study Flashcards: Module 22 Flashcards
Module 22: Related Notes
- PCDE Course: Module 22 Content
- Pandas (Python Dataframe Library)
- Parquet (Apache Columnar Storage Format)
- Feather (Apache Columnar Storage Format)
- DASK (Python Multiprocessing Library)
- Web Sockets
- Web Sockets in Python
Module 23: Creating a Data Pipeline
Module 23: Due Date
- Due Wednesday, June 28, 2023 at 1629 UTC
- 1829 CET
- 1229 EST
Module 23: Goals
- Discuss use cases for JavaScript.
- Identify key concepts related to visualization, unstructured data, and Javascript.
- Implement Python tools to visualize word frequency data.
- Implement JavaScript tools to visualize word frequency data.
- Create a sense-making data pipeline.
Module 23: Activities
- Knowledge Checks
- Knowledge Check 23.1: Visualization, Unstructured Data and Javascript
- Discussion
- Discussion 23.1: Use Cases for Javascript
- Try-It Activities
- Try-It Activity 23.1: Visualizing Word Frequency Data
- Project
- Project 23.1: Creating a sense-making data pipeline Self-Study Activities
- Self-Study Discussion 23.2: Thinking Like a Data Scientist: Creating a Data Pipeline
- Self-Study Flashcards: Module 23 Flashcards
Module 23: Related Notes
- PCDE Course: Module 23 Content
- PCDE Course Project 23.1
- cURL (C HTTP Client)
- Sense-Making Pipelines
- Python
- URL-Lib (Python StdLib)
- HyperText Markup Language (HTML)
- Exploratory Data Analysis (EDA, Data Science)
- Javascript
Module 24: Handling Big Data with Mosquito, ThingsBoard and Kafka
Module 24: Due Date
- Due Wednesday, July 5, 2023 at 1629 UTC
- 1829 CET
- 1229 EST
Module 23: Goals
- Identify key concepts related to Mosquito.
- Discuss use cases for Mosquito.
- Stream live data to ThingsBoard.
- Identify key concepts related to ThingsBoard.
- Analyze live streaming data using ThingsBoard.
- Discuss use cases for ThingsBoard.
- Identify key concepts related to Kafka.
- Discuss use cases for Kafka.
- Construct a web server using Kafka.
Module 24: Activities
- Knowledge Checks
- Knowledge Check 24.1: Mosquito
- Knowledge Check 24.2: ThingsBoard
- Knowledge Check 24.3: Kafka
- Discussion
- Discussion 24.1: Use Cases for Mosquito
- Discussion 24.2: Use Cases for ThingsBoard
- Discussion 24.3: Use Cases for Kafka
- Project
- Project 24.1: Streaming Live Data to ThingsBoard
- Project 24.2: Analyzing Streaming Live Data Using ThingsBoard
- Project 24.3: Constructing a Web Server Using Kafka Self-Study Activities
- Self-Study Discussion 23.2: Thinking Like a Data Scientist: Handling Big Data with Mosquito, ThingsBoard and Kafka
- Self-Study Flashcards: Module 24 Flashcards
Module 24: Related Notes
- PCDE Course: Module 24 Content
- PCDE Project 24.1
- PCDE Project 24.2
- PCDE Project 24.3
- Message Queue Telemetry Transport (MQTT)
- Mosquitto (MQTT Broker)
- Paho (Python MQTT Library)
- Firebase
- Thingsboard (IoT Platform)
- Kafka
- Node.JS (Javascript Runtime)
References
Notes References
- PCDE Course Overview
- PCDE Course Orientation
- Note-taking Strategies
- Notetaking Strategies for Lectures
- Time Management Strategies Notes
- Time Blocking Strategies Notes
- Introduction to Python Notes
- Introduction to Python
- PCDE Course: Module 2 Content
- Mathematical Probability Overview
- NumPy: Numerical Python Library
- Matplotlib: Python Plotting Library
- Normal Distribution
- PCDE COurse: Module 3 Content
- Pandas: Python Dataframes & Data Manipulation
- PCDE Course Materials (Module 5)
- SQL Overview
- Logical Operators in SQL
- Regular Expressions (RegEx)
- PCDE Course Module 6: Database Analysis & the Client Server Interface
- Exploratory Data Analysis in SQL
- Visualizing Data in SQL
- Cleaning Data in SQL
- Dates & Time in SQL
- Client Server Architecture Overview
- PCDE Course Module 7 Content: Model to Predict Housing Prices
- Statistics Using Python
- Markdown
- Predicting Housing Prices through Linear Regression & Python
- PCDE Course Module 8 Content: ETL, Analysis and Visualization
- PCDE Course: Module 9 Content
- VS Code
- Git
- GitHub
- Python
- Python: Collections
- Python: Classes
- Python: Advanced Functions
- Python: Decorators
- Python: Wrappers
- PCDE Course: Module 10 Content
- CLI: Command Line Interface
- GNU CoreUtils
- Computer Networks
- HTTP: Hypertext Transport Protocol
- HTTP Headers
- Software Containers
- Docker
- VS Code
- Postman
- Swagger
- PCDE Course: Module 11 Content
- Flask
- Cookies
- OAuth2
- PCDE Course: Module 12 Content
- Types of Databases
- Relational Databases
- Document Databases
- MongoDB Using Python
- Key-Value Databases
- Distributed Databases
- Cassandra (Distributed Database)
- PCDE Module 13 Content
- Change Data Capture
- Python Interaction with OS Shell
- PCDE Module 14 Content
- Java
- Debezium
- Java Spring
- Nano Editor
- PCDE Module 15 Course Content
- PCDE Project 15.1: Creating a Books Web App
- PCDE Project 15.2: Creating a Student Grades Database
- PCDE Project 15.3: Redundant Dictionaries in Python
- PCDE Module 16 Content
- PCDE Project 16: Build a Transit Data Application
- Mapbox
- MySQL
- Maven (Java Build Tool)
- JavaScript Object Notation (JSON)
- Change Data Capture (CDC)
- CSS
- MBTA API
- PCDE Module 17 Course Content
- Apache NiFi
- PCDE Module 18 Content
- Big Data
- Hadoop
- Spark
- Airflow (Data Workflow and Scheduling)
- Docker (Container Runtime)
- PySpark (Python Spark Library)
- Containers (Software)
- Extract, Transform, Load (ETL)
- MacOS (Operating System)
- Windows (Operating System)
- PCDE Course: Module 20 Content
- Matrix
- Gradient Descent
- Bayes Theory
- SciKit-Learn (Python Machine Learning Library)
- PCDE Course: Module 21 Content
- Machine Learning
- Reinforcement Learning
- Neural Network
- TensorFlow
- K-Means Clustering
- Confusion Matrix (Machine Learning)
- PCDE Course: Module 22 Content
- Parquet (Apache Columnar Storage Format)
- Feather (Apache Columnar Storage Format)
- DASK (Python Multiprocessing Library)
- Web Sockets
- Web Sockets in Python
- PCDE Course Module 23 Content
- PCDE Course Project 23.1
- cURL (C HTTP Client)
- Sense-Making Pipelines
- URL-Lib (Python StdLib)
- HyperText Markup Language (HTML)
- Exploratory Data Analysis (EDA, Data Science)
- Javascript
- PCDE Course: Module 24 Content
- PCDE Project 24.1
- PCDE Project 24.2
- PCDE Project 24.3
- Message Queue Telemetry Transport (MQTT)
- Mosquitto (MQTT Broker)
- Paho (Python MQTT Library)
- Firebase
- Thingsboard (IoT Platform)
- Kafka
- Node.JS (Javascript Runtime)