Data Architecture Primer

Data Engineering Fundamentals

Python for Data Science: Introduction to NumPy for Multi-dimentional Data

Python for Data Science: Advanced Operations with NumPy Arrays

Python for Data Science: Introduction to Pandas

Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames

R for Data Science: Data Structures

R for Data Science: Importing and Exporting Data

R for Data Science: Data Exploration

R for Data Science: Regression Methods

R for Data Science: Classification & Clustering

Data Science Statistics: Simple Descriptive Statistics

Data Science Statistics: Common Approaches to Sampling Data

Data Science Statistics: Inferential Statistics

Accessing Data with Spark: An Introduction to Spark

Getting Started with Hadoop: Fundamentals & MapReduce

Getting Started with Hadoop: Developing a Basic MapReduce Application

Hadoop HDFS: Introduction

Hadoop HDFS: Introduction to the Shell

Hadoop HDFS: Working with Files

Hadoop HDFS: File Permissions

Data Silos, Lakes, & Streams: Introduction

Data Silos, Lakes, and Streams: Data Lakes on AWS

Data Silos, Lakes, & Streams: Sources, Visualizations, & ETL Operations

Data Analysis Application

Final Exam: Data Analyst

Deploying Data Tools: Data Science Tools

Delivering Dashboards: Management Patterns

Delivering Dashboards: Exploration & Analytics

Cloud Data Architecture: DevOps & Containerization

Compliance Issues and Strategies: Data Compliance

Implementing Governance Strategies

Data Access & Governance Policies: Data Access Oversight and IAM

Data Access & Governance Policies: Data Classification, Encryption, and Monitoring

Streaming Data Architectures: An Introduction to Streaming Data

Streaming Data Architectures: Processing Streaming Data

Scalable Data Architectures: Introduction

Scalable Data Architectures: Introduction to Amazon Redshift

Scalable Data Architectures: Working with Amazon Redshift & QuickSight

Building Data Pipelines

Data Pipeline: Process Implementation Using Tableau & AWS

Data Pipeline: Using Frameworks for Advanced Data Management

Data Sources: Integration

Data Sources: Implementing Edge on the Cloud

Securing Big Data Streams

Harnessing Data Volume & Velocity: Big Data to Smart Data

Data Rollbacks: Transaction Rollbacks & Their Impact

Data Rollbacks: Transaction Management & Rollbacks in NoSQL

Final Exam: Data Ops

Developing a Growth Mind-set

Developing Your Business Acumen

Using Strategic Thinking to Consider the Big Picture

Using Active Listening in Workplace Situations

Choosing the Right Interpersonal Communication Method to Make Your Point

Building a Culture of Design Thinking

Enabling Business Process Improvement

The Essential Role of the Agile Product Owner

Innovating with Lean Product Management

Six Sigma Measurement System Analysis

Reaching Sound Conclusions

Capturing the Attention of Senior Executives

Data Wrangling with Pandas: Working with Series & DataFrames

Data Wrangling with Pandas: Visualizations and Time-Series Data

Data Wrangling with Pandas: Advanced Features

Data Wrangler 4: Cleaning Data in R

Data Tools: Technology Landscape & Tools for Data Management

Data Tools: Machine Learning & Deep Learning in the Cloud

Trifacta for Data Wrangling: Wrangling Data

MongoDB for Data Wrangling: Querying

MongoDB for Data Wrangling: Aggregation

Getting Started with Hive: Introduction

Getting Started with Hive: Loading and Querying Data

Getting Started with Hive: Viewing and Querying Complex Data

Getting Started with Hive: Optimizing Query Executions

Getting Started with Hive: Optimizing Query Executions with Partitioning

Getting Started with Hive: Bucketing & Window Functions

Getting Started with Hadoop: Filtering Data Using MapReduce

Getting Started with Hadoop: MapReduce Applications With Combiners

Getting Started with Hadoop: Advanced Operations Using MapReduce

Accessing Data with Spark: Data Analysis Using the Spark DataFrame API

Accessing Data with Spark: Data Analysis using Spark SQL

Data Lake: Framework & Design Implementation

Data Lake: Architectures & Data Management Principles

Data Architecture – Deep Dive: Design & Implementation

Data Architecture – Deep Dive: Microservices & Serverless Computing

Final Exam: Data Wrangler

Balancing the Four Vs of Data: The Four Vs of Data

Data Driven Organizations

Raw Data to Insights: Data Ingestion & Statistical Analysis

Raw Data to Insights: Data Management & Decision Making

Tableau Desktop: Real Time Dashboards

Storytelling with Data: Introduction

Storytelling with Data: Tableau & PowerBI

Python for Data Science: Basic Data Visualization Using Seaborn

Python for Data Science: Advanced Data Visualization Using Seaborn

Data Science Statistics: Using Python to Compute & Visualize Statistics

Advanced Visualizations & Dashboards: Visualization Using Python

Advanced Visualizations & Dashboards: Visualization Using R

R for Data Science: Data Visualization

Powering Recommendation Engines: Recommendation Engines

Data Insights, Anomalies, & Verification: Handling Anomalies

Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools

Data Science Statistics: Applied Inferential Statistics

Data Research Techniques

Data Research Exploration Techniques

Data Research Statistical Approaches

Machine & Deep Learning Algorithms: Introduction

Machine & Deep Learning Algorithms: Regression & Clustering

Machine & Deep Learning Algorithms: Data Preperation in Pandas ML

Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML

Creating Data APIs Using Node.js

Final Exam: Data Scientist

Slack iOS: Using the iOS App

Confluence: Signing in & Navigating within Spaces

Confluence: Setting Up & Managing Spaces

Confluence: Working with Spaces

Confluence: Working with Team Members

Confluence: Configuring Spaces

Slack Web: Signing in and Setting Up

Slack Web: Using Channels

Slack Web: Private Messaging and Communication Tools

Slack Web: Creating, Finding, and Sharing Information

Slack Web: Configuring Slack