Data Science Track 1: Data Analyst

Data Architecture Primer

Course: 1 Hour, 4 Minutes

• Course Overview
• Data Defined
• Data Privacy
• The Data Lifecycle
• SQL vs. NoSQL
• Create an Entity Relationship Diagram
• Implement a SQL Solution
• Implement a NoSQL Solution
• Big Data
• Data Architecture and Governance
• IT Data System Architecture Types
• Data Analytics and Reporting
• Exercise: Implement Data Architecture Best Practices

Data Engineering Fundamentals

Course: 46 Minutes

• Course Overview
• Overview of Distributed Systems
• Batch vs. In-Memory Processing
• NoSQL Stores
• Tools for Data Management
• What is ETL?
• ETL with Talend Open Studio
• Data Modeling
• AI and Machine Learning
• Data Partitioning
• Data Engineering
• Data Reporting
• Exercise: Create a Data Model

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

Course: 1 Hour

• Course Overview
• Introduction to NumPy and the NumPy Ecosystem
• Array Creation - Part 1
• Array Creation - Part 2
• Printing Arrays
• Basic Array Operations
• Universal Functions
• Indexing and Slicing
• Iterating Over Arrays
• Reshaping Arrays
• Exercise: Python NumPy Array Operations

Python for Data Science: Advanced Operations with NumPy Arrays

Course: 1 Hour, 8 Minutes

• Course Overview
• Splitting NumPy Arrays
• Images as Arrays
• Image Manipulation Using NumPy
• Views and NumPy Arrays
• Deep Copies of Arrays
• Introduction to Index Masks
• Applying Index Masks
• Indexing with Boolean Masks
• Structured Arrays
• Understanding Array Broadcasting
• Applying Broadcasting Rules on Array Operations
• Exercise: NumPy Multi-dimensional Array Operations

Python for Data Science: Introduction to Pandas

Course: 1 Hour, 6 Minutes

• Course Overview
• Features of Pandas and the Pandas Ecosystem
• Introduction to Pandas
• Work with Pandas
• Introduction to Data Frames
• Work with Data Frames
• Load Data into a Data Frame
• Add and Delete Data Frame Contents
• Select Parts of a Data Frame
• Access Pandas Data Frames
• Introduction to Multi-Indexing in a Data frame
• Reshape Data Frames
• Reshape Data frames Using Stack and Melt Operations
• Exercise: Pandas for Basic Tabular Data Manipulation

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

Course: 45 Minutes

• Course Overview
• Iterating Over the Contents of a Data Frame
• Exporting a Data Frame
• Sorting
• Handling Missing Data
• Grouping with a Multi-Index
• Merging Data Frames
• Applying Join Operations on Data Frames
• Pandas and Relational Databases
• Exercise: Pandas for Advanced Data Manipulation

R for Data Science: Data Structures

Course: 52 Minutes

• Course Overview
• Creating Vectors
• Manipulating Vectors
• Sorting Vectors
• Using Lists
• Creating Matrices
• Matrix Operations
• Creating Factors
• Creating Data Frames
• Data Frame Operations
• Exercise: Creating and Using a Data Frame

R for Data Science: Importing and Exporting Data

Course: 34 Minutes

• Course Overview
• Reading from CSV
• Reading from Excel
• Reading from HTML
• Exporting to CSV
• Exporting to Excel
• Exporting to HTML
• Exercise: Reading and Writing Data

R for Data Science: Data Exploration

Course: 41 Minutes

• Course Overview
• Creating dplyr Tables
• Selecting Subsets
• Filtering Tabular Data
• Piping Data
• Mutating Data
• Summarizing Data
• Combining Datasets
• Grouping Data
• Exercise: Querying Data

R for Data Science: Regression Methods

Course: 37 Minutes

• Course Overview
• Linear Data Preparation
• Creating Linear Models
• Interpreting Model Output
• Using Linear Prediction
• Logistic Data Preparation
• Using glm
• Exercise: Creating a Linear Model

R for Data Science: Classification & Clustering

Course: 39 Minutes

• Course Overview
• Preparing Data for Classification
• Using rpart
• Using ctree
• Preparing Data for Clustering
• Using K-Means Clustering
• Using Hierarchical Clustering
• Exercise: Creating a Decision Tree

Data Science Statistics: Simple Descriptive Statistics

Course: 1 Hour, 11 Minutes

• Course Overview
• Descriptive and Inferential Statistics
• Population vs. Sample
• Probability vs. Non-Probability Sampling
• Mean
• Median
• Mode
• IQR
• Variance
• Exercise: Using Descriptive Statistics

Data Science Statistics: Common Approaches to Sampling Data

Course: 47 Minutes

• Course Overview
• Terms in Sampling
• Sampling Bias
• Simple Random Sampling
• Systematic Random Samplin
• Stratified Sampling
• Non-Probability Sampling
• Exercise: Efficient and Correct Sampling

Data Science Statistics: Inferential Statistics

Course: 1 Hour, 2 Minutes

• Course Overview
• Gaussian Distribution
• Inferential Statistics and Hypothesis Testing
• Simplified Example of Hypothesis Testing
• T-tests
• Skewness and Kurtosis
• Correlation and Autocorrelation
• Introducing Linear Regression
• Overfitting and Goodness-of-Fit
• Exercise: Basic Inferential Statistics

Accessing Data with Spark: An Introduction to Spark

Course: 1 Hour, 7 Minutes

• Course Overview
• Introduction to Spark and Hadoop
• Resilient Distributed Datasets (RDDs)
• RDD Operations
• Spark Data Frames
• Spark Architecture
• Spark Installation
• Working with RDDs
• Creating Data Frames from RDDs
• Contents of a Data Frame
• The SQL Context
• The map() Function of an RDD
• Accessing the Contents of a Data Frame
• Data Frames in Spark and Pandas
• Exercise: Working with Spark

Getting Started with Hadoop: Fundamentals & MapReduce

Course: 1 Hour, 4 Minutes

• Course Overview
• An Introduction to Big Data
• Building Systems to Scale with Data
• A Quick Overview of Hadoop
• MapReduce Overview
• The Map Phase of a MapReduce
• The Shuffle and Reduce Phases
• Exercise: Fundamentals of Hadoop and MapReduce

Getting Started with Hadoop: Developing a Basic MapReduce Application

Course: 1 Hour, 14 Minutes

• Course Overview
• Provisioning a Hadoop Cluster on the Cloud
• Browsing the Hadoop Web Applications
• Creating a MapReduce project
• Coding the Map Phase
• Coding the Reduce Phase
• Defining the Driver Program
• Building the Application
• Executing the MapReduce Application
• Exercise: Developing a Basic MapReduce Application

Hadoop HDFS: Introduction

Course: 1 Hour, 15 Minutes

• Course Overview
• Scaling Datasets
• Horizontal Scaling for Big Data
• Distributed Clusters and Horizontal Scaling
• Overview of HDFS
• HDFS Architectures
• MapReduce for HDFS
• YARN for HDFS
• The Mechanism of Resource Allocation in Hadoop
• Apache Zookeeper for HDFS
• The Hadoop Ecosystem
• Exercise: An Introduction to HDFS

Hadoop HDFS: Introduction to the Shell

Course: 53 Minutes

• Course Overview
• Creating a Hadoop Cluster on the Google Cloud
• Exploring Hadoop Clusters
• The YARN Cluster Manager UI
• The HDFS Name Node UI
• Browsing the Packaged Hadoop Tools
• Configuring HDFS
• The HDFS Shells
• Exercise: Introduction to the HDFS Shell

Hadoop HDFS: Working with Files

Course: 48 Minutes

• Course Overview
• Basic Directory Commands in HDFS
• Using the copy From Local Command in HDFS
• Using the put Command in HDFS
• Using the copy To Local Command in HDFS
• Retrieving files from HDFS
• Append and Delete Operations in HDFS
• Exercise: Working with Files on HDFS

Hadoop HDFS: File Permissions

Course: 49 Minutes

• Course Overview
• The HDFS count and du Commands
• Viewing and Setting File Permissions in HDFS
• Applying Permissions Recursively in HDFS
• An Introduction to Bash Scripting
• Scripting HDFS Operations
• Exploring the HDFS Name Node UI
• Cleanup Operations in HDFS
• Exercise: File Permissions on HDFS

Data Silos, Lakes, & Streams: Introduction

Course: 1 Hour, 20 Minutes

• Course Overview
• Data Silos
• Data Lakes
• Characteristics of Data Lakes
• Data Lake Architecture, Features, and Challenges
• Data Warehouses
• Data Warehouses vs. Data Lakes
• Data Streams
• Migrating Data to AWS
• Data Lakes on AWS
• Working with Data Lakes on AWS
• Exercise: Data Silos, Lakes, and Streams

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

Course: 1 Hour, 10 Minutes

• Course Overview
• Create a Role for the AWS Glue Service
• Upload Data to S
• Explore the Glue Web Console
• Manually Create Glue Tables
• Query the Data Lake Using Amazon Athena
• Configure and Run Glue Crawlers
• Access Data in Crawled Tables
• Crawl Multiple CSV Files in the Same Folder Path
• Merge Data in Multiple Files in the Same Folder Path
• Work with Files Having the Exact Same Schema
• Exercise: Data Lakes on AWS with S3 and Glue

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

Course: 1 Hour, 29 Minutes

• Course Overview
• Set Up a Redshift Cluster
• Create Tables and Load Data From S
• Establish a JDBC Connection to Redshift
• Crawl Redshift Using a JDBC Connection
• Crawl DynamoDB
• Configure Quick Sight to Visualize Data
• Visualize Data in Quick Sight
• Configure a Job to Perform Extract, Transform, Load
• Execute an ETL Operation in Glue
• Perform ETL to Back Up Redshift Data in S3 Buckets
• Perform ETL to Back Up DynamoDB Data in S3 Buckets
• Exercise: Multiple Sources, Visualizations, and ETL

Data Analysis Application

Course: 1 Hour, 25 Minutes

• Course Overview
• Install and Configure Anaconda Python
• Install R Using Anaconda
• Use Jupyter Notebook
• Import and Export Data in Python
• Import and Export Data in R
• Deal with Missing Data in R
• Transform Data in R
• Work with Numpy
• Work with Pandas
• Mean, Median, and Mode in R
• Analyze Data with Pandas
• Plot Data in R
• Visualize Data in Python
• Exercise: Perform Data Analysis

Data Science Track 2: Data Wrangler

Data Wrangling with Pandas: Working with Series & DataFrames

Course: 1 Hour, 11 Minutes

• Course Overview
• Installing Pandas
• Pandas Series Objects
• Operations on Series
• Appending and Sorting Series Values
• Pandas DataFrames
• Indexing Operations with DataFrames
• Missing Data
• Column Aggregations
• Statistical Operations
• Exercise: Operations on Series and DataFrames

Data Wrangling with Pandas: Visualizations and Time-Series Data

Course: 1 Hour, 29 Minutes

• Course Overview
• Pandas and Matplotlib for Visualizations
• Pie Charts, Box Plots, and Scatter Plots
• Time-Series Data
• Deltas and Percentage Change Calculations
• Time Deltas and Date Ranges
• Mismatched DataFrames and Missing Data
• Working with String Data
• Advanced Operations on Strings
• Applying Functions on Series
• Transforming Data With User-Defined Functions
• Applying Functions on DataFrames
• Exercise: Plot Charts and Transform Column Values

Data Wrangling with Pandas: Advanced Features

Course: 1 Hour, 12 Minutes

• Course Overview
• Grouping and Aggregations
• MultiIndex DataFrames
• Grouping and Aggregations with MultiIndex DataFrames
• General Aggregation Functions
• Filtering
• Masking Column Values
• Working with Duplicates
• Working with Categorical Data
• Filtering, Adding, and Removing Categories
• Reindexing
• Exercise: Filtering, Duplicates and Categorical Data5

Data Wrangler 4: Cleaning Data in R

Course: 1 Hour, 3 Minutes

• Course Overview
• Types of Unclean Data
• Data Quality
• Downloading JSON Data
• Excel Sheets
• Reading Dirty CSVs
• Querying Relational Databases
• Joining Tabular Data
• Spreading Data
• Summarizing Data
• Imputing Data
• Extracting Matches
• Exercise: Wrangling Data

Data Tools: Technology Landscape & Tools for Data Management

Course: 27 Minutes

• Course Overview
• Technology Landscape and Tools
• Tool Comparison
• Machine Learning in Data Analytics
• Machine Learning Tools
• Machine Learning Implementation
• Python and R for Data Management
• Cloud and Machine Learning
• Exercise: Implement Machine Learning on Scikit-learn

Data Tools: Machine Learning & Deep Learning in the Cloud

Course: 23 Minutes

• Course Overview
• Microsoft Machine Learning Toolkit
• AWS and Machine Learning
• Spark Machine Learning Capabilities
• Deep Learning Frameworks
• Deep Learning Implementation
• Data Mining and Analytical Tools
• KNIME Capabilities
• Exercise: Implement Deep Learning

Trifacta for Data Wrangling: Wrangling Data

Course: 50 Minutes

• Course Overview
• Standardizing Data
• Formatting Dates
• Filtering Rows
• Replacing Values
• Counting Matches
• Splitting Columns
• Merging Columns
• Extracting Data
• Conditional Aggregation
• Reshaping Data
• Joining Data
• Exercise: Wrangling Data

MongoDB for Data Wrangling: Querying

Course: 1 Hour, 8 Minutes

• Course Overview
• Introduction to PyMongo
• Document Structure
• CRUD Operations
• ObjectID and Timestamp
• Query Operations
• Projection Queries
• Comparison Operators
• Element Query Operators
• The Regex Operator
• Using the Size and All Operators
• Text Search
• Using mongoimport
• Using mongoexport
• Exercise: Performing a Query

MongoDB for Data Wrangling: Aggregation

Course: 51 Minutes

• Course Overview
• Aggregation Framework
• Using Group
• Using Match
• Using Project
• Using Limit and Sort
• Using Unwind
• Using Lookup
• Using Indexes
• Using Geospatial Indexes
• Exercise: Performing an Aggregate Query

Getting Started with Hive: Introduction

Course: 56 Minutes

• Course Overview
• Hive as a Data Warehouse
• Overview of Relational Databases
• OLTP and OLAP
• Hive and the Hadoop Ecosystem
• Hive Server and The Metastore
• Hive on Cloud Computing Platforms
• Data Types in Hive
• Data and Tables in Hive
• Exercise: Introduction to Hive

Getting Started with Hive: Loading and Querying Data

Course: 1 Hour, 20 Minutes

• Course Overview
• Setting up a Hadoop Cluster on the Google Cloud
• Creating a Hive Table
• Running Simple Queries in Hive
• Executing Hive Queries from the Shell
• Joining Tables in Hive
• Exploring the Hive Warehouse
• External Tables in Hive
• Modifying Tables in Hive
• Temporary Tables in Hive
• Loading Data into Tables in Hive
• Populating Multiple Tables in Hive
• Exercise: Loading and Querying Data in Hive

Getting Started with Hive: Viewing and Querying Complex Data

Course: 1 Hour, 14 Minutes

• Course Overview
• The Array Data Type in Hive
• The Map Data Type in Hive
• The Struct Type in Hive
• The explode and posexplode Functions in Hive
• Lateral Views in Hive
• Multiple Lateral Views in Hive
• Set Operations in Hive
• The IN and EXISTS clauses in Hive
• Creating and Populating Tables in Hive
• Views in Hive6
• Exercise: Viewing and Querying Complex Data

Getting Started with Hive: Optimizing Query Executions

Course: 43 Minutes

• Course Overview
• Hive Queries as MapReduce Jobs
• Techniques to Improve Query Performance in Hive
• Partitioning Tables in Hive
• Bucketing Tables in Hive
• Structuring Join Queries in Hive
• Exercise: Optimizing Query Execution in Hive

Getting Started with Hive: Optimizing Query Executions with Partitioning

Course: 1 Hour, 1 Minute

• Course Overview
• Setting up a Hadoop Cluster on the Google Cloud
• Creating a Partitioned Table in Hive
• Working with Partitions in Hive
• Populating Partitions in Hive
• Partitioning External Tables in Hive
• Modifying Partitions in Hive
• Dynamic Partitions in Hive
• Using Multiple Columns for Partitioning in Hive
• Exercise: Optimize Executions with Partitioning

Getting Started with Hive: Bucketing & Window Functions

Course: 1 Hour, 4 Minutes

• Course Overview
• Apply Bucketing for a Table in Hive
• Using Bucketing and Partitioning Together in Hive
• Sorting a Bucket's Contents in Hive
• Sampling a Table in Hive
• Joining Multiple Tables in Hive
• Introducing Window Functions in Hive
• Windows Functions with Partitions in Hive
• Exercise: Bucketing and Window Functions in Hive

Getting Started with Hadoop: Filtering Data Using MapReduce

Course: 59 Minutes

• Course Overview
• Counting the Data Points in Each Category
• The Reducer and Driver Programs
• Building and Executing the Application
• A Simple Filter Using MapReduce
• Executing and Examining the Output
• Extracting the Unique Values in a Column
• Viewing the Distinct Values Extracted
• Exercise: Filtering Data Using MapReduce

Getting Started with Hadoop: MapReduce Applications With Combiners

Course: 1 Hour, 24 Minutes

• Course Overview
• Combiners in MapReduce
• Revisiting MapReduce
• Working with Combiners
• Using Combiners for Calculating Averages
• Creating a Project to Calculate Averages
• Coding the Map and Reduce Phases
• Configure the Application in the Driver
• Executing the Application and Examining the Output
• Adding a Combiner to a MapReduce Application
• Conveying a Pair of Numbers from the Mapper
• Running the Fixed Application
• Exercise: Optimizing MapReduce With Combiners

Getting Started with Hadoop: Advanced Operations Using MapReduce

Course: 49 Minutes

• Course Overview
• Defining a User-Defined Type for a PriorityQueue
• Implementing a PriorityQueue in a Mapper
• Using a PriorityQueue in a Reducer
• Running and Verifying the Results
• Building an Inverted Index - Map Phase
• Building an Inverted Index - Reduce Phase
• Executing the Application and Viewing the Index
• Exercise: Advanced Operations Using MapReduce

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

Course: 1 Hour, 12 Minutes

• Course Overview
• Performance Improvements in Spark
• Broadcast Variables and Accumulators
• Loading Data into a DataFrame
• Sampling the Contents of a DataFrame
• Grouping and Aggregations
• Visualizing Data in a DataFrame
• Trimming and Cleaning Data
• User-Defined Functions and DataFrames
• Combining Filters, Aggregations, and Sorting
• Using Broadcast Variables
• Using Accumulators
• Exporting DataFrame Contents
• Custom Accumulators
• Join Operations4
• Exercise: Data Analysis Using the DataFrame API4

Accessing Data with Spark: Data Analysis using Spark SQL

Course: 55 Minutes

• Course Overview
• The Spark Catalyst Optimizer
• Introduction to Spark SQL
• Preparing Data for Analysis
• Running SQL Queries
• Inferred and Explicit Schemas
• Windowing in Spark
• Applying Window Functions
• Exercise: Data Analysis Using Spark SQL

Data Lake: Framework & Design Implementation

Course: 34 Minutes

• Course Overview
• Data Lakes and Data Warehouses
• Data Lake Selection Criteria
• Data Lake and Data Democratization
• Data Lake Design Principles
• AWS Data Lake Architecture
• Implement AWS Data Store
• Data Lake For On-Premise and Multi-Cloud
• Data Processing Frameworks for Data Lake
• Exercise: Implement AWS Data Store

Data Lake: Architectures & Data Management Principles

Course: 35 Minutes

• Course Overview
• Real-Time Big Data Architectures
• Data Lake Reference Architecture
• Data Ingestion and File Formats
• Ingestion Using Sqoop
• Data Processing Strategies
• Deriving Value from Data Lakes
• Data Life Cycle
• S3 and Glacier
• Exercise: Ingest Data and Implement Archival Policy

Data Architecture - Deep Dive: Design & Implementation

Course: 36 Minutes

• Course Overview
• Data Complexity Management Strategies
• Data Modeling Process
• Distributed Data Management
• Partitioning Methods and Criteria
• MongoDB Partitioning
• Hybrid Data Architectures
• Implement Directed Acyclic Graph
• CAP Theorem
• Batch vs. Streaming
• Read and Write Concerns
• Exercise: Implement Serverless Architecture

Data Architecture - Deep Dive: Microservices & Serverless Computing

Course: 26 Minutes

• Course Overview
• Microservices and Data
• Serverless and Lambda Architecture
• Lambda Implementation
• Cluster Benefits
• Data Architecture Types
• Data Discovery Process
• Data Risk Types
• Data POC
• Exercise: Implement Lambda Architecture

Data Science Track 3: Data Ops

Deploying Data Tools: Data Science Tools

Course: 48 Minutes

• Course Overview
• Data Science Platform
• Challenges of Deploying Data Science Tools
• Considerations for Data Science Tools
• Data Science Workflow
• Data Science Analytic Tools
• Data Science Visualization Tools
• Data Science Database Tools
• Benefits of Deploying Cloud-Based Tools
• Challenges of Deploying Cloud-Based Tools
• What is DevOps
• DevOps for Data Science
• Exercise: Identifying Uses of Data Science Tools

Delivering Dashboards: Management Patterns

Course: 34 Minutes

• Course Overview
• Analytical Visualization
• Dashboard Types
• Data Management
• Dashboard Components
• Dashboard Best Practices
• Dashboard Using ELK
• Dashboard Using Power BI
• Chart Selection Criteria
• Leaderboards and Scorecards
• Scorecard Types
• Exercise: Create Dashboards with PowerBI and ELK

Delivering Dashboards: Exploration & Analytics

Course: 31 Minutes

• Course Overview
• Data Exploration Using Charts
• Analytical Visualization Tools
• Bar and Line Charts
• Dashboarding with Kibana
• Dashboard Sharing with Kibana
• Dashboarding with Tableau
• Dashboarding with Qlikview
• Data Ingest and Dashboards
• Dashboard Patterns
• Monitoring Dashboards
• Exercise: Create Dashboards Using Kibana and Tableau

Cloud Data Architecture: DevOps & Containerization

Course: 45 Minutes

• Course Overview
• Containerization on the Cloud
• Benefits of Containers
• Serverless Computing
• DevOps in the Cloud
• AWS OpsWorks
• Storage Classification
• Cloud and Machine Learning
• Cloud and BI Analytics
• Exercise: Containerization and Serverless Computing

Compliance Issues and Strategies: Data Compliance

Course: 44 Minutes

• Course Overview
• Data Compliance Issues
• Data Regulations
• The Importance of Global Standards
• Risk and Company Standards
• Myths and Facts of Data Compliance
• Compliance Training for Users
• Compliance Training for Management
• The Benefits of a Data Compliance Program
• Elements of a Good Compliance Strategy
• Building a Compliance Strategy
• Reporting and Response Procedures
• Exercise: Explain the Importance of Data Compliance

Implementing Governance Strategies

Course: 46 Minutes

• Course Overview
• Governance and its Relationship with Big Data
• Why Big Data Requires Governance
• Requirements for Big Data Governance
• Why is Big Data Different?
• Identifying Data
• Identifying Stakeholders
• Cloud Technologies and Data Governance
• Designing a Data Governance Process
• Managing a Data Governance Strategy
• Monitoring a Data Governance Strategy
• Maintaining a Data Governance Strategy
• Exercise: Defining Data Governance Strategies

Data Access & Governance Policies: Data Access Oversight and IAM

Course: 59 Minutes

• Course Overview
• Data Access Governance
• Risk and Data Safety Compliance
• Data Access Patterns
• Data Breach Prevention
• Least Privilege
• Assign and View Effective File System Permissions
• Identity and Access Management
• Create an AWS IAM User and Group
• Assign AWS IAM Group Permissions
• Vulnerability Assessments
• Implement Effective Security Controls
• Exercise: Implement Data Access Governance Solutions

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

Course: 1 Hour, 19 Minutes

• Course Overview
• Data Classification
• Classify Data Using Microsoft FSRM
• Data Encryption
• Encrypt Data at Rest
• Encrypt Data in Motion
• Implement Security Compliance Checking
• Examine Data Access Trends
• Data Access Monitoring Solutions
• Logging, Auditing, and Data Analytics
• Configure a Custom Filtered Log View
• Enable Windows Data Access Auditing
• Exercise: Implement Data Confidentiality

Streaming Data Architectures: An Introduction to Streaming Data

Course: 51 Minutes

• Course Overview
• Introduction to Streaming data
• The Stream Processing Model
• The Message Transport
• Stream Processing with RDDs
• Structured Streaming for Continuous Applications
• Streaming vs Structured Streaming
• Triggers and Output Modes
• Exercise: Working with Streaming Data

Streaming Data Architectures: Processing Streaming Data

Course: 53 Minutes

• Course Overview
• PySpark Setup
• Setting Up a Socket Stream with Netcat
• The Update Output Mode
• Using a File Input Stream
• The Append Output Mode
• The Complete Output Mode
• Aggregations on Streaming Data
• SQL Operations on Streaming Data
• User-Defined Functions (UDFs)
• Exercise: Processing Streaming Data

Scalable Data Architectures: Introduction

Course: 53 Minutes

• Course Overview
• Scalable Architectures with Distributed Computing
• Introducing Data Warehouses
• Contrasting Warehouses with Relational Databases
• Data Warehouses for Analytical Processing
• Data Warehouse Architectural Components
• Amazon Redshift - A Data Warehouse on the Cloud
• Exercise: Scalable Data Architectures

Scalable Data Architectures: Introduction to Amazon Redshift

Course: 55 Minutes

• Course Overview3
• Provisioning a Redshift Cluster Using Quick Launch
• Creating a Redshift Cluster With Additional Detail
• Exploring the Redshift Configs and Metrics
• Attaching an IAM Role to a Redshift Cluster
• Creating an AWS User to Work With Redshift
• Installing and Configuring the AWS CLI8
• Running Queries from the Redshift Query Editor
• Exercise: An Introduction to Amazon Redshift

Scalable Data Architectures: Working with Amazon Redshift & QuickSight

Course: 1 Hour, 18 Minutes

• Course Overview
• Loading Data from Amazon S3 to a Redshift Cluster
• Running Queries and Evaluating Their Execution
• Querying a Redshift Cluster Using a SQL client
• Working with Automated Snapshots
• Restoring Tables from a Snapshot
• Horizontal Scaling of a Redshift Cluster
• Vertical and Horizontal Scaling of a Cluster
• Configuring Access from Quick Sight to Redshift
• Loading a Dataset to Quick Sight
• Creating Visualizations with Quick Sight
• Exercise: Working with Redshift and Quick Sight

Building Data Pipelines

Course: 1 Hour, 10 Minutes

• Course Overview
• Data Pipelines Overview
• Traditional ETL Pipeline with Batch Processing
• Data Pipeline Tools
• Setup and Install Airflow
• Apache Airflow
• Airflow Workflows
• Airflow Tasks
• Airflow Dependencies
• ETL Pipeline with Airflow
• Automated Pipeline without ETL
• Airflow Command Line Testing
• Exercise: Using Apache Airflow

Data Pipeline: Process Implementation Using Tableau & AWS

Course: 39 Minutes

• Course Overview
• Data Pipeline
• Data Pipeline Processes
• Data Pipeline Stages
• Data Pipeline Technologies
• Data Source Types
• Scheduled Data Pipeline
• Tableau Server and Utilities
• Data Pipeline Using Tableau
• Data Pipeline on AWS
• Exercise: Build Data Pipelines with Tableau

Data Pipeline: Using Frameworks for Advanced Data Management

Course: 33 Minutes

• Course Overview
• Celery and Luigi
• Data Pipeline with Python Luigi
• Working with Dask Library
• Dask Arrays
• Data Exploration and Visualization Frameworks
• Spark and Tableau
• Streaming Data Visualization with Python
• Data Pipeline Open Source Tools
• Exercise: Implement Data Pipelines with Luigi

Data Sources: Integration

Course: 40 Minutes

• Course Overview
• Elements of IoT Solutions
• Service Categories in IoT
• IoT Capabilities and Maturity Model
• IoT Design Principles
• IoT Cloud Architectures
• MQTT and XXMP
• IoT Controllers
• IoT Data Management
• Securing IoT
• Exercise: Generating Data Streams

Data Sources: Implementing Edge on the Cloud

Course: 31 Minutes

• Course Overview
• AWS IoT Greengrass
• GCP IoT Edge
• AWS IoT over WebSockets
• IoT Device Simulator
• Generating Streams of Data Using MQTT
• Exercise: Working with IoT Device Simulators

Securing Big Data Streams

Course: 1 Hour, 3 Minutes

• Course Overview
• Big Data Security Concerns
• Streaming Data Security Concerns
• NoSQL Database Security Concerns
• Distributed Processing Security Risks
• Data Mining and Analytics Privacy Flaws
• End-Point Device Tampering Risks
• Secure Big Data
• Secure Data Streams
• Secure Data In Motion
• End-Point Input Validation and Filtering
• Secure Data at Rest with Symmetric Ciphers
• Exercise: Securing Big Data Streams

Harnessing Data Volume & Velocity: Big Data to Smart Data

Course: 39 Minutes

• Course Overview
• Comparing Big Data and Smart Data
• Smart Data and Edge Technologies
• Big Data to Smart Data Formation
• Smart Data and Smart Processes
• Smart Data Use Cases
• Smart Data Life Cycle
• Big Data to Smart Data Using k-NN
• Smart Data Frameworks
• Smart Data to Business
• Clustering Smart Data
• Smart Data Integration
• Exercise: Transform Big Data to Smart Data

Data Rollbacks: Transaction Rollbacks & Their Impact

Course: 36 Minutes

• Course Overview
• Rollback Process
• State of Transactions
• Transaction Types
• SQL Transaction Management
• Transaction Log Operations
• Deadlock Management
• SQL Server Rollback Mechanism
• SQL Server Rollback Mechanism Implementation
• Exercise: Implement Transactions with SQL Server

Data Rollbacks: Transaction Management & Rollbacks in NoSQL

Course: 29 Minutes

• Course Overview
• NoSQL and SQL Transaction Management
• MongoDB Transactions
• Manage Multi-Document Transactions in MongoDB
• Change Data Capture
• Change Stream in MongoDB
• MongoDB Change Stream Implementation
• Exercise: MongoDB Transactions and Change Streams

Data Science Track 4: Data Scientist

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

Course: 40 Minutes

• Course Overview
• Overview of the Four Vs
• The Importance of Volume
• The Importance of Variety
• The Importance of Velocity
• The Importance of Veracity
• The Relationship Between the Four Vs
• Variety and Data Structure
• Validity and Volatility
• Finding Balance in the Four Vs
• Use Cases
• Extracting Value from the Four Vs
• Exercise: Describe the Four Vs of Big Data

Data Driven Organizations

Course: 1 Hour, 15 Minutes

• Course Overview
• Data Driven Organizations
• Decision Making
• Analytic Maturity
• Analytic Roles
• Data Source Priority
• Facets of Data Quality
• Power BI Data Visualization
• Missing Data
• Duplicate Data
• Truncated Data
• Data Provenance
• Exercise: Use Informatica Data Quality

Raw Data to Insights: Data Ingestion & Statistical Analysis

Course: 54 Minutes

• Course Overview
• Statistical Analysis
• Data Correction
• Outlier Detection
• Data Architecture Pattern
• Data Ingestion Tools
• Kafka and Apache NiFi
• Apache Sqoop Ingest
• Ingest Using WaveFront
• Exercise: Detecting Outliers and Ingesting Data

Raw Data to Insights: Data Management & Decision Making

Course: 57 Minutes

• Course Overview
• Data-driven Decision Making Framework
• Loading Data into R
• Preparing Data
• Data Correction Approach
• Data Correction Using Simple Transformation
• Data Correction Using Deductive Correction
• Distributed Data Management
• Data Analytics
• Data Analytics Using R
• Predictive Modeling
• Exercise: Correcting and Modelling Data

Tableau Desktop: Real Time Dashboards

Course: 1 Hour, 8 Minutes

• Course Overview
• Introducing Real Time Dashboards
• Creating Real Time Dashboards with Tableau
• Build a Tableau Dashboard
• Real Time Dashboard Updates in Tableau
• Organizing Your Tableau Dashboard
• Formatting Your Tableau Dashboard
• Interactive Tableau Dashboard
• Tableau Dashboard Starters
• Tableau Dashboard Extensions
• Tableau Dashboards and Story Points
• Sharing your Tableau Dashboard
• Exercise: Creating a Tableau Dashboard Starter

Storytelling with Data: Introduction

Course: 47 Minutes

• Course Overview
• Storytelling Process
• Interpreting Context
• Analysis Types
• Who, What, and How of Storytelling
• Visualization for Storytelling
• Graphical Tools for Data Elaboration
• Storytelling Scenarios
• Storyboarding
• Exercise: Visualization and Graphical Tool

Storytelling with Data: Tableau & PowerBI

Course: 57 Minutes

• Course Overview
• Visual Selection
• Slopegraphs
• Bar Charts and Types of Bar Charts
• Clutter and Clutter Elimination
• Gestalt Principle
• Story Design Best Practices
• Tools for Storytelling
• Decluttering
• Crafting Visual Data
• Visual Design Concerns
• Storytelling with Power BI
• Model Visual and Tableau
• Exercise: Storytelling with Power BI

Python for Data Science: Basic Data Visualization Using Seaborn

Course: 1 Hour, 7 Minutes

• Course Overview
• Introduction to Seaborn
• Install Seaborn
• Simple Univariate Distributions
• Configure Univariate Distribution Plots
• Simple Bivariate Distributions
• Explore Different Types of Bivariate Distributions
• Analyze Multiple Variable Pairs
• Regression Plots
• Themes and Styles in Seaborn
• Exercise: Basic Data Visualization Using Seaborn

Python for Data Science: Advanced Data Visualization Using Seaborn

Course: 1 Hour, 4 Minutes

• Course Overview
• Searching for Patterns in a Dataset
• Configuring Plot Aesthetics
• Normal Distribution and Outliers
• Distributions Within Categories - Part 1
• Distributions Within Categories - Part 2
• Analyzing Categories with Facet Grids - Part 1
• Analyzing Categories with Facet Grids - Part 2
• Introducing Color Palettes
• Using Color Palette8
• Exercise: Advanced Data Visualization Using Seaborn

Data Science Statistics: Using Python to Compute & Visualize Statistics

Course: 1 Hour, 16 Minutes

• Course Overview
• An Introduction to Matplotlib
• Analyzing Data Using NumPy and Pandas
• Visualizing Univariate and Bivariate Distributions
• Summary Statistics Using Native Python Functions
• Summary Statistics Using NumPy
• Summary Statistics Using the SciPy Library
• Correlation and Covariance
• Z-score
• Exercise: Compute and Visualize Statistics6 MinutesCompletedActions

R for Data Science: Data Visualization

Course: 33 Minutes

• Course Overview
• Using Scatter Plots
• Using Line Graphs
• Using Bar Charts
• Using Box and Whisker Plots
• Using Histograms
• Using a Bubble Plot
• Exercise: Data Visualization

Advanced Visualizations & Dashboards: Visualization Using Python

Course: 38 Minutes

• Course Overview
• Relevance of Data Visualization for Business
• Libraries for Data Visualization in Python
• Python Data Visualization Environment Configuration
• Matplotlib Libraries for Visualization
• Bar Chart Using ggplot
• Bokeh and Pygal
• Select Visualization Libraries
• Interactive Graphs and Image Files
• Plot Graphs
• Multiple Lines in Graphs
• Exercise: Create Line Charts with Pygal

Advanced Visualizations & Dashboards: Visualization Using R

Course: 35 Minutes

• Course Overview
• Chart Types
• Stacked Bar Plot
• Animate Plots with Matplotlib
• Plotting in Jupyter Notebook
• Graphics in R
• Heat Map and Scatter Plot in R
• Correlogram and Area Chart in R
• ggplot2 Capabilities
• Customize ggplot2 Graphs
• Exercise: Creating Heat Maps and Scatter Plots

Powering Recommendation Engines: Recommendation Engines

Course: 1 Hour, 5 Minutes

• Course Overview
• Describing Recommendation Engines
• Comparing the Types of Recommendation Engines
• Collecting and Manipulating Data
• Manipulating Data in R
• Describing Similarity and Neighborhoods
• Creating a Recommendation Engine
• Recommending Another Item
• Finding Items to Recommend
• Recommending Items Based on Other Items
• Evaluating a Recommendation System
• Validating a Recommendation System
• Exercise: Creating a Recommendation Engine

Data Insights, Anomalies, & Verification: Handling Anomalies

Course: 46 Minutes

• Course Overview
• Data and Anomaly Sources
• Decomposition and Forecasting
• Examine Data Using Randomization Tests
• Anomaly Detection
• Anomaly Detection Techniques
• Anomaly Detection with scikit-learn
• Anomaly Detection Tools
• Anomaly Detection Rules
• Exercise: Detecting Anomalies

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

Course: 51 Minutes

• Course Overview
• Machine Learning Anomaly Detection Techniques
• Comparing Anomaly Detection Algorithms
• Anomaly Detection Using R5
• Online Anomaly Detection Components
• Online Anomaly Detection Approaches
• Anomaly Detection Use Cases
• Anomaly Detection with Visualization Tools
• Anomaly Detection with Mathematical Approaches
• Cluster-Based Anomaly Detection
• Exercise: Detecting Anomalies

Data Science Statistics: Applied Inferential Statistics

Course: 1 Hour, 19 Minutes

• Course Overview
• The One-Sample T-test
• Independent and Paired T-tests
• Testing Hypotheses with T-tests
• Loading and Analyzing a Skewed Dataset
• Measuring Skewness and Kurtosis
• Preparing a Dataset for Regression
• Simple Linear Regression
• Multiple Linear Regression
• Exercise: Applied Inferential Statistics

Data Research Techniques

Course: 33 Minutes

• Course Overview
• Data Research Fundamentals
• Data Research Steps
• Values, Variables, and Observations
• JMP Scale of Measurement
• Non-experimental and Experimental Research
• Descriptive and Inferential Statistical Analysis
• Inferential Tests
• Case Study of Clinical Data Research
• Data Research in Sales Management
• Exercise: Implement Data Research

Data Research Exploration Techniques

Course: 50 Minutes

• Course Overview
• Fundamentals of Exploratory Data Analysis
• Data Exploration Types
• Working with R
• Data Exploration in R
• Data Exploration Using Plots
• Python Packages for Data Exploration
• Data Exploration Using Python
• Data Research Using Linear Algebra
• Linear Algebra for Data Research
• Exercise: R and Python for Data Exploration

Data Research Statistical Approaches

Course: 43 Minutes

• Course Overview
• Role of Statistics in Data Research
• Discrete vs. Continuous Distribution
• PDF and CDF
• Binomial Distribution
• Interval Estimation
• Point and Interval Estimation
• Data Visualization Techniques
• Data Visualization Using R
• Data Integration Techniques
• Creating Plots
• Missing Values and Outliers
• Exercise: Statistical Methods for Data Research

Machine & Deep Learning Algorithms: Introduction

Course: 46 Minutes

• Course Overview
• Machine Learning Algorithms
• How Machine Learning Works
• Introduction to Pandas ML
• Support Vector Machines
• Overfitting
• Exercise: Machine Learning and Classification

Machine & Deep Learning Algorithms: Regression & Clustering

Course: 49 Minutes

• Course Overview
• The Confusion Matrix
• An Introduction to Regression
• Applications of Regression
• Supervised and Unsupervised Learning
• Clustering
• Principal Component Analysis
• Exercise: Regression and Clustering

Machine & Deep Learning Algorithms: Data Preperation in Pandas ML

Course: 1 Hour, 4 Minutes

• Course Overview
• Data Preparation in scikit-learn
• Training and Evaluating Models in scikit-learn
• Introducing the Pandas ML ModelFrame
• Training and Evaluating Models in Pandas ML
• Preparing Data for Regression
• Evaluating Regression Models
• Preparing Data for Clustering
• The K-Means Clustering Algorithm
• Exercise: Regression, Classification, and Clustering

Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML

Course: 1 Hour, 24 Minutes

• Course Overview
• Analyzing an Imbalanced Dataset
• The RandomOverSampler
• The SMOTE Oversampler
• Undersampling Using imbalanced-learn
• Ensemble Classifiers for Imbalanced Data
• Combination Samplers
• Finding Correlations in a Dataset
• Building a Multi-Label Classification Model
• Dimensionality Reduction with PCA
• Imbalanced Learn and PCA

Creating Data APIs Using Node.js

Course: 1 Hour, 31 Minutes

• Course Overview
• API Prerequisites
• Building a RESTful API Using Node.js and Express.js
• RESTful API with OAuth
• HTTP Server with Hapi.js
• API Modules
• Returning Data with JSON
• Nodemon for Development Workflow
• API Requests
• POSTman for API
• Deploying APIs
• Social Media APIs
• Exercise: Building RESTful APIs