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Adobe
Adobe Content Creator
Course 1: Design Fundamentals
Course 2: Social Media Content and Strategy
Course 3: Multimedia Content Creation
Course 4: Generative AI Content Creation
Adobe Graphic Designer
Course 1: Design Fundamentals
Course 2: Generative AI Content Creation
Course 3: Image Editing
Course 4: Graphic Design
Course 5: Document Design
Amazon
Amazon Junior Software Developer
Course 1: Introduction to Software Development
Course 2: Programming with Java
Course 3: Data Structures and Algorithms
Course 4: Database Management with Java and SQL
Course 5: Full Stack Web Development
Course 6: Generative AI in Software Development
Course 7: Application Development
AWS
AWS Cloud Technology Consultant
Course 1: Introduction to Information Technology and AWS Cloud
Course 2: AWS Cloud Technical Essentials
Course 3: Providing Technical Support for AWS Workloads
Course 4: Developing Applications in Python on AWS
Course 5: Skills for Working as an AWS Cloud Consultant
Course 6: DevOps on AWS and Project Management
Course 7: Automation in the AWS Cloud
Course 8: Data Analytics and Databases on AWS
AWS Cloud Support Associate
Course 1: Introduction to Information Technology and AWS Cloud
Course 2: AWS Cloud Technical Essentials
Course 3: Skills and Best Practices for Cloud Support Associates
Course 4: Cloud Support Essentials: A Technical Approach
Course 5: Automation in the AWS Cloud
Course 6: Python for Serverless Applications and Automation on AWS
Course 7: Capstone: Preparing to work as a Cloud Support Associate
AWS Cloud Solutions Architect
Course 2: Architecting Solutions on AWS
Course 3: Building Data Lakes on AWS
Course 4: Exam Prep: AWS Certified Solutions Architect – Associate
Course 1: AWS Cloud Technical Essentials
Board Infinity
.NET FullStack Developer Specialization
Course 1 – .Net Full Stack Foundation
Course 2 – Frontend Development using React
Course 3 – Backend Development for .Net Full Stack
Java Full Stack Developer Specialization
Course 1 – Fundamentals of Java Programming
Course 2 – Frontend for Java Full Stack Development
Course 3 – Data Structures & Backend with Java
DeepLearning.AI
Deep Learning Specialization
Course 1 – Neural Networks and Deep Learning
Course 2 – Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Course 3 – Structuring Machine Learning Projects
Course 4 – Convolutional Neural Networks
Course 5 – Sequence Models
Google
Google Data Analytics
Course 1: Foundations: Data, Data, Everywhere
Course 2: Ask Questions to Make Data-Driven Decisions
Course 3: Prepare Data for Exploration
Course 4: Process Data from Dirty to Clean
Course 5: Analyze Data to Answer Questions
Course 6: Share Data Through the Art of Visualization
Course 7: Data Analysis with R Programming
Course 8: Google Data Analytics Capstone: Complete a Case Study
Google Cybersecurity
Course 1 – Foundations of Cybersecurity
Course 2 – Play It Safe: Manage Security Risks
Course 3 – Connect and Protect: Networks and Network Security
Course 4 – Tools of the Trade: Linux and SQL
Course 5 – Assets, Threats, and Vulnerabilities
Course 6 – Sound the Alarm: Detection and Response
Course 7 – Automate Cybersecurity Tasks with Python
Course 8 – Put It to Work: Prepare for Cybersecurity Jobs
Google Project Management
Course 1: Foundations of Project Management
Course 2: Project Initiation: Starting a Successful Project
Course 3: Project Planning: Putting It All Together
Course 4: Project Execution: Running the Project
Course 5: Agile Project Management
Course 6: Capstone: Applying Project Management in the Real World
Google Digital Marketing & E-commerce
Course 2: Attract and Engage Customers with Digital Marketing
Course 6: Make the Sale: Build, Launch, and Manage E-commerce Stores
Course 7: Satisfaction Guaranteed: Develop Customer Loyalty Online
Course 3: From Likes to Leads: Interact with Customers Online
Course 4: Think Outside the Inbox: Email Marketing
Course 5: Assess for Success: Marketing Analytics and Measurement
Course 1: Foundations of Digital Marketing and E-commerce
Google Advanced Data Analytics
Course 3 – Go Beyond the Numbers: Translate Data into Insights
Week 2 – Explore raw data
Week 3 – Clean your data
Week 1 – Find and share stories using data
Week 4 – Data visualizations and presentations
Week 5 – Assess your Course 3 end-of-course project
Course 6 – The Nuts and Bolts of Machine Learning
Week 1 – The different types of machine learning
Week 2 – Workflow for building complex models
Week 3 – Unsupervised learning techniques
Week 4 – Tree-based modeling
Week 5 – Assess your Course 6 end-of-course project
Course 2 – Get Started with Python
Week 2 – Functions and conditional statements
Week 3 – Loops and strings
Week 4 – Data structures in Python
Week 5 – Course 2 end-of-course project
Week 1 – Hello, Python!
Course 1 – Foundations of Data Science
Week 3 – Your career as a data professional
Week 4 – Data applications and workflow
Week 5 – Assess your Course 1 end-of-course project
Week 1 – Introduction to data science concepts
Week 2 – The impact of data today
Course 5 – Regression Analysis: Simplify Complex Data Relationships
Week 1 – Introduction to complex data relationships
Week 2 – Simple linear regression
Week 3 – Multiple linear regression
Week 4 – Advanced hypothesis testing
Week 5 – Logistic regression
Week 6 – Course 5 end-of-course project
Course 4 – The Power of Statistics
Week 1 – Introduction to statistics
Week 2 – Probability
Week 3 – Sampling
Week 5 – Introduction to hypothesis testing
Week 6 – Course 4 end-of-course project
Week 4 – Confidence intervals
Google IT Support
Course 2: The Bits and Bytes of Computer Networking
Module 1: Introduction to Networking
Module 2: The Network Layer
Module 3: The Transport and Application Layers
Module 4: Networking Services
Module 5: Connecting to the Internet
Module 6: Troubleshooting and the Future of Networking
Course 1: Technical Support Fundamentals
Module 6: Troubleshooting
Module 1: Introduction to IT
Module 2: Hardware
Module 3: Operating System
Module 4: Networking
Module 5: Software
Google Business Intelligence
Course 2 – The Path to Insights: Data Models and Pipelines
Week 4 – Course 2 end-of-course project
Week 2 – Dynamic database design
Week 3 – Optimize ETL processes
Week 1 – Data models and pipelines
Course 3 -Decisions, Decisions: Dashboards and Reports
Week 3 – Automate and monitor
Week 4 – Present business intelligence insights
Week 5 – Course 3 end-of-course project
Week 2 – Visualize results
Week 1 – Business intelligence visualizations
Course 1 – Foundations of Business Intelligence
Week 4 – Course 1 end-of-course project
Week 1 – Data-driven results through business intelligence
Week 2 – Business intelligence tools and techniques
Week 3 – Context is crucial for purposeful insights
Google UX Design
Course 1: Foundations of User Experience (UX) Design
Module 1: Introducing user experience design
IBM
IBM Data Analytics with Excel and R
Course 2: Excel Basics for Data Analysis
Module 1: Introduction to Data Analysis Using Spreadsheets
Module 4: Analyzing Data Using Spreadsheets
Module 2: Getting Started with Using Excel Speadsheets
Module 3: Cleaning & Wrangling Data Using Spreadsheets
Course 3: Data Visualization and Dashboards with Excel and Cognos
Module 1: Visualizing Data Using Spreadsheets
Module 2: Creating Visualizations and Dashboards with Spreadsheets
Module 3: Creating Visualizations and Dashboards with Cognos Analytics
Course 6: SQL for Data Science with R
Module 2: Introduction to Relational Databases and Tables
Module 6: Course Project
Module 3: Intermediate SQL
Module 4: Getting Started with Databases using R
Module 5: Working with Database Objects using R
Module 1: Getting Started with SQL
Course 8: Data Visualization with R
Module 3: Dashboards
Module 1: Introduction to Data Visualization
Module 2: Basic Plots, Maps, and Customization
Module 4: Final Assignment
Course 9: Data Science with R – Capstone Project
Module 5: Building a R Shiny Dashboard App
Module 4: Predictive Analysis
Module 1: Capstone Overview and Data Collection
Module 2: Data Wrangling
Module 3: Performing Exploratory Data Analysis with SQL, Tidyverse & ggplot2
Course 5: Introduction to R Programming for Data Science
Module 1: R Basics
Module 2: Common Data Structures
Module 3: R Programming Fundamentals
Module 4: Working with Data
Module 5: Final Project
Course 1: Introduction to data analytics
Module 2: The Data Ecosystem
Module 3: Gathering and Wrangling Data
Module 4: Mining & Visualizing Data and Communicating Results
Module 5: Career Opportunities and Data Analysis in Action
Module 1: What is Data Analytics
Course 7: Data Analysis with R
Module 4: Model Development in R
Module 5: Model Evaluation
Module 6: Project
Module 1: Introduction to Data Analysis with R
Module 2: Data Wrangling
Module 3: Exploratory Data Analysis
IBM Data Science
Course 6 – Databases and SQL for Data Science with Python
Week 1 – Getting Started with SQL
Week 2 – Introduction to Relational Databases and Tables
Week 3 – Intermediate SQL
Week 4 – Accessing Databases using Python
Week 5 – Course Assignment
Week 6 – Bonus Module: Advanced SQL for Data Engineering (Honors)
Course 3 – Data Science Methodology
Week 1 – From Problem to Approach and From Requirements to Collection
Week 2 – From Understanding to Preparation and From Modeling to Evaluation
Week 3 – From Deployment to Feedback
Course 7 – Data Analysis with Python
Week 6 – Final Assignment
Week 4 – Model Development
Week 5 – Model Evaluation
Week 1 – Importing Datasets
Week 2 – Data Wrangling
Week 3 – Exploratory Data Analysis
Course 1 – What is Data Science?
Week 3 – Data Science in Business
Week 1 – Defining Data Science and What Data Scientists Do
Week 2 – Data Science Topics
Course 5 – Python Project for Data Science
Week 1 – Crowdsourcing Short squeeze Dashboard
Course 2 – Tools for Data Science
Week 2 – Languages of Data Science
Week 5 – RStudio & GitHub
Week 6 – Create and Share your Jupyter Notebook
Week 7 – [Optional] IBM Watson Studio
Week 1 – Overview of Data Science Tools
Week 3 – Packages, APIs, Datasets and Models
Week 4 – Jupyter Notebooks and JupyterLab
Course 10 – Applied Data Science Capstone
Week 1 – Introduction
Week 2 – Exploratory Data Analysis (EDA)
Week 3 – Interactive Visual Analytics and Dashboard
Week 4 – Predictive Analysis (Classification)
Course 9 – Machine Learning with Python
Week 2 – Regression
Week 3 – Classification
Week 4 – Linear Classification
Week 5 – Clustering
Week 6 – Final Exam and Project
Week 1 – Introduction to Machine Learning
Course 8 – Data Visualization with Python
Week 1 – Introduction to Data Visualization Tools
Week 2 – Basic and Specialized Visualization Tools
Week 3 – Advanced Visualizations and Geospatial Data
Week 4 – Creating Dashboards with Plotly and Dash
Week 5 – Final Project and Exam
Course 4: Python for Data Science, AI & Development
Module 4: Working with Data in Python
Module 1: Python Basics
Module 2: Python Data Structures
Module 3: Python Programming Fundamentals
Module 5: APIs and Data Collection
IBM Data Analyst
Course 3 – Data Visualization and Dashboards with Excel and Cognos
Week 2 – Creating Visualizations and Dashboards with Spreadsheets
Week 1 – Visualizing Data Using Spreadsheets
Week 3 – Creating Visualizations and Dashboards with Cognos Analytics
Course 1 – Introduction to Data Analytics
Week 3 – Gathering and Wrangling Data
Week 4 – Mining & Visualizing Data and Communicating Results
Week 5 – Career Opportunities and Data Analysis in Action
Week 1 – What is Data Analytics
Week 2 – The Data Ecosystem
Course 9 – IBM Data Analyst Capstone Project
Week 3 – Exploratory Data Analysis
Week 2 – Data Wrangling
Week 1 – Data Collection
Week 4 – Data Visualization
Course 8 – Data Visualization with Python
Week 1 – Introduction to Data Visualization Tools
Week 3 – Advanced Visualizations and Geospatial Data
Week 4 – Creating Dashboards with Plotly and Dash
Week 5 – Final Project and Exam
Week 2 – Basic and Specialized Visualization Tools
Course 7 – Data Analysis with Python
Week 6 – Final Assignment
Week 5 – Model Evaluation
Week 4 – Model Development
Week 2 – Data Wrangling
Week 1 – Importing Datasets
Week 3 – Exploratory Data Analysis
Course 6 – Databases and SQL for Data Science with Python
Week 5 – Course Assignment
Week 6 – Bonus Module: Advanced SQL for Data Engineering (Honors)
Week 1 – Getting Started with SQL
Week 2 – Introduction to Relational Databases and Tables
Week 3 – Intermediate SQL
Week 4 – Accessing Databases using Python
Course 4: Python for Data Science, AI & Development
Module 3: Python Programming Fundamentals
Module 2: Python Data Structures
Module 1: Python Basics
Module 4: Working with Data in Python
Module 5: APIs and Data Collection
Course 2 – Excel Basics for Data Analysis
Week 1 – Introduction to Data Analysis Using Spreadsheets
Week 4 – Analyzing Data Using Spreadsheets
Week 2 – Getting Started with Using Excel Speadsheets
Week 3 – Cleaning & Wrangling Data Using Spreadsheets
Course 5 – Python Project for Data Science
Week 1 – Crowdsourcing Short squeeze Dashboard
IBM Generative AI Engineering
Course 3: Generative AI: Prompt Engineering Basics
Module 2: Prompt Engineering: Techniques and Approaches
Module 3: Course Quiz, Project, and Wrap-up
Module 1: Prompt Engineering for Generative AI
Course 4: Python for Data Science, AI & Development
Module 5: APIs and Data Collection
Module 1: Python Basics
Module 2: Python Data Structures
Module 3: Python Programming Fundamentals
Module 4: Working with Data in Python
Course 2: Generative AI: Introduction and Applications
Module 1: Introduction and Capabilities of Generative AI
Module 2: Applications and Tools of Generative AI
Module 3: Course Quiz, Project, and Wrap-up
Course 1: Introduction to Artificial Intelligence (AI)
Module 1: Introduction and Applications of AI
Module 3: Business and Career Transformation Through AI
Module 2: AI Concepts, Terminology, and Application Domains
Module 4: Issues, Concerns, and Ethical Considerations
Key Technologies for Business
Course 1: Introduction to Cloud Computing
Week 1 – Overview of Cloud Computing
Week 2 – Cloud Computing Models
Week 3 – Components of Cloud Computing
Week 4 – Emergent Trends and Practices
Week 5 – Cloud Security, Monitoring, Case Studies, Jobs
Week 6 – Final Project and Assignment
Course 2: Introduction to Artificial Intelligence (AI)
Week 3 – AI: Issues, Concerns and Ethical Considerations
Week 4 – The Future with AI, and AI in Action
Week 1 – What is AI? Applications and Examples of AI
Week 2 – AI Concepts, Terminology, and Application Areas
Course 3: What is Data Science?
Week 3 – Data Science in Business
Week 1 – Defining Data Science and What Data Scientists Do
Week 2 – Data Science Topics
Meta
Meta AR Developer
Course 3 – AR for web using JavaScript
Week 1 – Basics of Web AR development
Week 2 – Javascript in PlayCanvas
Week 3 – Content development and integration
Week 4 – Creating an AR learning experience with PlayCanvas
Course 2 – AR in marketing using Meta Spark
Week 3 – Meta Spark pro
Week 4 – Game creation in Meta Spark
Week 1 – Meta Spark Quick Start
Week 2 – Meta Spark fundamentals
Course 5 – Using AR Foundation in Unity
Week 1 – Say hello to AR in Unity
Week 2 – AR Foundation marker-based game creation
Week 3 – Create your first AR game using AR Foundation
Week 4 – Finish and deploy your first AR game using AR Foundation
Course 6 – AR games using Vuforia SDK
Week 2 – Use plane detection to build a portal to an AR world
Week 3 – Create an AR game using Vuforia
Week 4 – Finish and deploy your AR game built with Vuforia
Week 1 – Introduction to Vuforia and plane detection in Unity
Course 1 – Foundations of AR
Week 2 – AR technologies and capabilities
Week 1 – Introduction to AR
Week 3 – Computer vision
Week 4 – AR software development lifecycle
Course 4 – Unity and C# basics
Week 4 – C# and basic gameplay
Week 1 – Introduction to Unity
Week 2 – Asset creation and player controls
Week 3 – C# basics in Unity
Meta Front-End Developer
Course 3 – Version Control
Week 3 – Working with Git
Week 4 – Graded Assessment
Week 1 – Software collaboration
Week 2 – Command Line
Course 4 – HTML and CSS in depth
Week 1 – HTML in depth
Week 2 – Interactive CSS
Course 6 – Advanced React
Week 4 – Final project
Week 1 – Components
Week 2 – React Hooks and Custom Hooks
Week 3 – JSX and testing
Course 1 – Introduction to Front-End Development
Week 3 – UI Frameworks
Week 1 – Get started with web development
Week 2 – Introduction to HTML and CSS
Week 4 – End-of-Course Graded Assessment
Course 8 – Front-End Developer Capstone
Week 1 – Starting the project
Week 2 – Project foundations
Week 3 – Project functionality
Week 4 – Project Assessment
Course 2 – Programming with JavaScript
Week 3 – Programming Paradigms
Week 4 – Testing
Week 5 – End-of-Course Graded Assessment
Week 2 – The Building Blocks of a Program
Week 1 – Introduction to Javascript
Course 7 – Principles of UX/UI Design
Week 1 – Introduction to UX and UI design
Week 2 – Evaluating interactive design
Week 4 – Designing your UI
Week 5 – Course summary and final assessment
Week 3 – Applied Design Fundamentals
Course 9 – Coding Interview Preparation
Week 1 – Introduction to the coding interview
Week 2 – Introduction to Data Structures
Week 3 – Introduction to Algorithms
Week 4 – Final project
Course 5 – React Basics
Week 1 – React Components
Week 2 – Data and State
Week 3 – Navigation, Updating and Assets in React.js
Meta Social Media Marketing
Course 1 – Introduction to Social Media Marketing
Week 4 – Understand Your Audience
Week 2 – Social Media Platforms Overview
Week 5 – Choose Your Social Media Channels
Week 1 – The Social Media Landscape
Week 3 – Goals and Planning for Success
Course 4 – Advertising with Meta
Week 3 – Select Your Audience, Platforms and Schedule
Week 2 – Determine Your Campaign Objective and Budget
Week 4 – Create Your Ads and Evaluate Your Campaign Results
Week 5 – Build Your Own Campaign in Ads Manager
Week 1 – First Steps in Ads Manager
Course 2 – Social Media Management
Week 3 – Social Media Content Management
Week 4 – Evaluate Your Efforts
Week 1 – Establish Your Presence
Week 2 – Social Media Content
Course 3 – Fundamentals of Social Media Advertising
Week 2 – Creating Effective Ads on Social Media
Week 3 – Data, Privacy and Policies on Social Media
Week 4 – Getting Started with Advertising on Facebook and Instagram
Week 1 – Introduction to Social Media Advertising
Course 5 – Measure and Optimize Social Media Marketing Campaigns
Week 3 – Optimize Your Ad Campaigns
Week 4 – Communicate Your Marketing Results
Week 1 – Evaluate Your Marketing Results Against Goals
Week 2 – Measure Your Advertising Effectiveness
Google Cloud
Google Cloud Developer
Course 5: Developing Applications with Cloud Run Functions on Google Cloud
Module 2: Introduction to Cloud Run Functions
Module 3: Calling and Connecting Cloud Run Functions
Module 4: Securing Cloud Run Functions
Module 5: Integrating with Cloud Databases
Module 6: Best Practices for Functions
Course 6: Developing Containerized Applications on Google Cloud
Module 2: Introduction to Containers
Module 3: Introduction to Cloud Run and Google Kubernetes Engine
Course 7: Developing Applications with Cloud Run on Google Cloud: Fundamentals
Module 2: Fundamentals of Cloud Run
Module 3: Service Identity and Authentication
Module 4: Application Development, Testing, and Integration
Course 8: Integrating Applications with Gemini 1.0 Pro on Google Cloud
Module 1: Integrating Applications with Gemini 1.0 Pro on Google Cloud
Course 2: Google Cloud Fundamentals: Core Infrastructure
Module 6: Containers in the Cloud
Module 2: Introducing Google Cloud
Module 3: Resources and Access in the Cloud
Module 4: Virtual Machines and Networks in the Cloud
Module 5: Storage in the Cloud
Module 7: Applications in the Cloud
Module 8: Prompt Engineering
Course 3: Developing Applications with Google Cloud: Foundations
Module 2: Best Practices for Cloud Application Development
Module 3: Getting Started with Google Cloud Development
Module 4: Data Storage Options
Module 5: Handling Authentication and Authorization
Module 6: Adding Intelligence to Your Application
Module 7: Deploying Applications
Module 8: Compute Options for Your Application
Module 9: Monitoring and Performance Tuning
Course 4: Service Orchestration and Choreography on Google Cloud
Module 4: Choreography and Orchestration
Module 2: Introduction to Microservices
Module 3: Event-Driven Applications
Microsoft
Microsoft Power BI Data Analyst
Course 4 – Data Modeling in Power BI
Module 3 – Optimize a model for performance in Power BI
Module 2 – Using Data Analysis Expressions (DAX) in Power BI
Module 4 – Final project and assessment: Modeling data in Power BI
Module 1 – Concepts for data modeling
Course 1 – Preparing Data for Analysis with Microsoft Excel
Module 4 – Final project and assessment: Preparing data for analysis with Microsoft Excel
Module 1 – Excel fundamentals
Module 2 – Formulas and functions
Module 3 – Preparing data for analysis using functions
Course 2 – Harnessing the Power of Data with Power BI
Module 3 – Final project and assessment: Harnessing the power of data in Power BI
Module 1 – Data analysis in business
Module 2 – The right tools for the job
Course 5 – Data Analysis and Visualization with Power BI
Module 4 – Identifying Patterns and Trends
Module 2 – Navigation and Accessibility
Module 5 – Guided Project: Data Analysis and Visualization with Microsoft Power BI
Module 3 – Bringing Data to the User
Module 1 – Creating Reports
Module 6 – Final project and assessment: Data Analysis and Visualization with Power BI
Course 8 – Microsoft PL-300 Exam Preparation and Practice
Module 3 – Visualize and analyze data
Module 4 – Deploy and maintain assets
Module 5 – Practice Exam
Module 1 – Preparing Data
Module 2 – Modeling Data
Course 3 – Extract, Transform and Load Data in Power BI
Module 3 – Advanced ETL in PowerBI
Module 1 – Data Sources in Power BI
Module 4 – Graded Assessment and Course wrap up
Module 2 – Transforming Data in Power BI
Course 6 – Creative Designing in Power BI
Module 3 – Dashboard Design and Storytelling
Module 4 – Final project and assessment: Creative Design in Power BI
Module 1 – Visualization and Design
Module 2 – Designing Powerful Report Pages
Course 7 – Deploy and Maintain Power BI Assets and Capstone project
Module 2 – Deploying assets
Module 3 – Security and monitoring
Module 4 – Guided project: Deploy and maintain Power BI Assets and Capstone project
Module 5 – Capstone project
Module 1 – Power BI in enterprise
Microsoft UX Design
Course 3: User Interface Design and Prototyping
Module 1: Wireframes and mockups
Module 2: Design systems and style guides
Module 3: Interactive prototypes
Module 4: Design critiques and user testing
Course 4: UX Design in Practice: Accessibility and Collaboration
Module 4: Collaborative design and communication
Module 5: Portfolio presentation and job resources
Module 1: Visual design, high-fidelity mockups, and platform considerations
Module 2: Accessibility and inclusive design
Module 3: AI for UX design
Course 2: Designing for User Experience
Module 5: Information architecture
Module 1: User research and empathy building
Module 2: User needs assessment
Module 3: Design ideation
Module 4: Storyboarding
Course 1: Fundamentals of UI/UX Design
Module 2: Roles in a UX design team
Module 3: Frameworks and process: Design thinking
Module 4: Introducing the portfolio project
Module 1: Introduction to user experience design
University Of Michigan
Python for Everybody Specialization
Course 5: Capstone: Retrieving, Processing, and Visualizing Data with Python
Course 1: Programming for Everybody (Getting Started with Python)
Week 5 – Chapter Three: Conditional Code
Week 6 – Chapter Four: Functions
Week 7 – Chapter Five: Loops and Iteration
Week 3 – Chapter One: Why We Program (continued)
Week 4 – Chapter Two: Variables and Expressions
Course 3: Using python to access web data
Week 6 – JSON and the REST Architecture (Chapter 13)
Week 2 – Regular Expressions (Chapter 11)
Week 3 – Networks and Sockets (Chapter 12)
Week 4 – Programs that Surf the Web (Chapter 12)
Week 5 – Web Services and XML (Chapter 13)
Course 2: Python Data Structures
Week 6 – Chapter Ten: Tuples
Week 1 – Chapter Six: Strings
Week 3 – Chapter Seven: Files
Week 4 – Chapter Eight: Lists
Week 5 – Chapter Nine: Dictionaries
Course 4: Using Databases with Python
Week 3 – Data Models and Relational SQL
Week 1 – Object Oriented Python
Week 2 – Basic Structured Query Language
Week 4 – Many-to-Many Relationships in SQL
Intuit
Intuit Academy Bookkeeping
Course 3 – Liabilities and Equity in Accounting
Week 2 – Payroll, Obligations, and Loans
Week 3 – Equity and Liabilities
Week 4 – Practice with Liabilities and Equity
Week 1 – Liabilities and Equity in Accounting
Course 4 – Financial Statement Analysis
Week 1 – Understanding Reconciliations
Week 2 – How to Read Financial Statements
Week 3 – Analyzing Key Reports and Transactions
Week 4 – Application and Practice with Reconciliations and Financial Analysis
Course 2 – Assets in Accounting
Week 1 – Accounting Concepts and Measurement
Week 4 – Applying Accounting Principles and Knowledge
Week 2 – Inventory Accounting Methods
Week 3 – Property and Equipment
Course 1 – Bookkeeping Basics
Week 4 – Accounting Principles and Practices
Week 2 – The Accounting Cycle (Part 1)
Week 1 – Accounting Concepts and Measurement
Week 3 – The Accounting Cycle (Part 2)
Stanford
Machine Learning Specialization
Course 1 – Supervised Machine Learning: Regression and Classification
Week 2: Regression with multiple input variables
Week 3: Classification
Week 1: Introduction to Machine Learning
Course 3 – Unsupervised Learning, Recommenders, Reinforcement Learning
Week 1: Unsupervised learning
Week 2: Recommender systems
Week 3: Reinforcement learning
Course 2 – Advanced Learning Algorithms
Week 2: Neural network training
Week 3: Advice for applying machine learning
Week 4: Decision trees
Week 1: Neural Networks
Linkedin
N
Node.js
NoSQL
I
IT Operations
iMovie
A
Adobe Lightroom
AWS Lambda
Adobe XD
AutoCAD
Adobe Photoshop
Agile Methodologies
Adobe Acrobat
Avid Media Composer
Adobe After Effects
Adobe Animate
Amazon Web Services (AWS)
Android
Angular
Arcgis Products
Adobe InDesign
Autodesk Fusion 360
Accounting
Adobe Premiere Pro
Autodesk Maya
Adobe Illustrator
.Net Framework
E
Eclipse
D
Dreamweaver
Django
G
Git
GO (Programming Language)
Google Ads
Google Analytics
Google Cloud Platform (GCP)
O
Objective-C
Object-Oriented Programming (OOP)
Q
Quickbooks
M
Matlab
Course 3: Multimedia Content Creation
Module 1: Introduction to mulitmedia
Module 2: Creating podcasts
Course 3: Multimedia Content Creation
Module 2: Creating podcasts
Module 1: Introduction to mulitmedia
Course 3: Multimedia Content Creation
Module 1: Introduction to mulitmedia
Module 2: Creating podcasts
Course 3: Multimedia Content Creation
Module 1: Introduction to mulitmedia
Module 2: Creating podcasts
Microsoft Power BI
Microsoft PowerPoint
Microsoft Project
MySql
Microsoft Azure
Maven
Microsoft Access
Microsoft Excel
Machine Learning
Microsoft Outlook
W
WordPress
Windows Server
C
Cybersecurity
C#
C++
Cascading Style Sheets (CSS)
C (Programming Language)
P
Pro Tools
Python
PHP
B
Bash
S
Search Engine Optimization (SEO)
Scala
SharePoint
SketchUp
SOLIDWORKS
Spring Framework
Swift (Programming Language)
L
Logic Pro
Linux
K
Kotlin
Keynote
R
Ruby on Rails
Rhino 3D
R (Programming Language)
Revit
REST APIs
Rust (Programming Language)
React.js
J
jQuery
JavaScript
U
Unity
H
Hadoop
HTML
F
Front-End Development
Final Cut Pro
T
Transact-SQL (T-SQL)
V
Visual Basic for Applications (VBA)
Visio
X
XML
Community
Contact
About
meta coursera professional certificate
