Data for Leaders and Decision Makers - Decision Model and Notation - Bedrijfskundige Processen - Business Process Management (BPM / BPR)

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Data for Leaders and Decision Makers - Decision Model and Notation - Bedrijfskundige Processen - Business Process Management (BPM / BPR)

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Data for Leaders and Decision Makers. 

De Data for Leaders and Decision-makers Learning Kit is ontworpen om managers, leiders en besluitvormers bewust te maken van data en moderne datatechnologieën. Het geeft een uitgebreid overzicht van moderne databronnen, moderne data-infrastructuren en baanbrekende technologieën die in opkomst zijn om een ​​​​breed scala aan zakelijke behoeften aan te pakken. Deze cursus richt zich op algemeen aanvaarde datatechnologieën, tools, frameworks en platforms op een hoog niveau om managers en leiders in staat te stellen comfortabel betrokken te raken bij dataprojecten. Studenten zullen ook alles begrijpen over gegevens, verschillende problemen met gegevenscomp…

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Data for Leaders and Decision Makers. 

De Data for Leaders and Decision-makers Learning Kit is ontworpen om managers, leiders en besluitvormers bewust te maken van data en moderne datatechnologieën. Het geeft een uitgebreid overzicht van moderne databronnen, moderne data-infrastructuren en baanbrekende technologieën die in opkomst zijn om een ​​​​breed scala aan zakelijke behoeften aan te pakken. Deze cursus richt zich op algemeen aanvaarde datatechnologieën, tools, frameworks en platforms op een hoog niveau om managers en leiders in staat te stellen comfortabel betrokken te raken bij dataprojecten. Studenten zullen ook alles begrijpen over gegevens, verschillende problemen met gegevenscompliance, gegevensbeheer en verschillende gegevensstrategieën die moeten worden toegepast om betere gegevensbeheer en verschillende gegevensstrategieën die moeten worden toegepast om betere gegevensbeheer.

Learning Kits zijn gestructureerde leertrajecten, voornamelijk op het gebied van Emerging Tech. Een leerpakket houdt de student werkt aan een algemeen doel, hen te helpen uw loopbaanambities te verwezenlijken. Elk deel leidt de student stap voor stap door een diverse reeks onderwerpen. Leerpakketten zijn:bestaande uit verplichte tracks, die alle beschikbare leermiddelen bevatten, zoals assessments (eindexamens), mentor, oefenlabs en van cursus e-learning. En alle bronnen met 365 dagen toegang vanaf de eerste activering.

Cursusinhoud

Track 1: Data Primer

In this track, the focus will be on the fundamentals of data, traditional data architectures, and new age data infrastructures.
Courses (2 hours +):

Data Nuts & Bolts: Fundamentals of Data

Course: 30 Minutes

  • Course Overview
  • Data, Information, Knowledge, and Wisdom
  • Sources of Data Generation and Data Formats
  • Data Terminologies
  • Data Storage and Backup
  • Data Migration and ETL
  • Advantages of Data Integration
  • Data Visualization and Reporting
  • Data Engineering Languages
  • Course Summary

Traditional Data Architectures: Relational Databases

Course: 35 Minutes

  • Course Overview
  • Different Types of Databases
  • Relational Database Design
  • Normalization and Denormalization
  • Normal Forms and Their Use Cases
  • OLTP Information Systems
  • OLAP Information Systems
  • Common Use Cases of Data Warehousing
  • Traditional Data Architectures
  • How Data Mining and Data Marts Are Used
  • How to Scale a Database
  • Course Summary

Traditional Data Architectures: Data Warehousing and ETL Systems

Course: 40 Minutes

  • Course Overview
  • Data Warehousing for Business Intelligence
  • Data Warehouse Architecture
  • Data Warehousing Schemas
  • Dimension Table Use Cases
  • Fact Tables in a Data Warehouse
  • Keys in Data Warehouse Schemas
  • What Is ETL?
  • What Is ETL, ETL Framework, and Process Flow?
  • Extract, Transform, and Load (ETL) Tools
  • Extract, Transform, and Load (ETL) Best Practices
  • Course Summary

New Age Data Infrastructures: Factors Driving Data Infrastructures

Course: 38 Minutes

  • Course Overview
  • Traditional Data Architecture
  • Limitations of Traditional Data Architecture
  • Limitations of Traditional ETL Systems
  • Compare ETL and ELT Systems
  • Demand for Multi-model Data Platforms
  • Multi-model Databases
  • Commonly Used Data Sources
  • Real-time Data Processing
  • Traditional and New Age Business Intelligence
  • The Evolution of Analytics
  • Course Summary

Assessment:

  • Data Primer

Track 2: Big Data Infrastructures

In this track, the focus will be on big data concepts, non-relational data, and big data analytics.
Courses (3 hours +)

Big Data Concepts: Getting to Know Big Data

Course: 43 Minutes

  • Course Overview
  • What Is Big Data?
  • Sources of Big Data
  • Characteristics of Big Data
  • Structured and Unstructured Data
  • Big Data Analytics
  • Advantages of Big Data Analytics
  • Big Data Analytics: Domain Use Cases
  • Big Data Analytics: Netflix Use Case
  • Big Data Analytics: Amazon Use Case
  • Major Challenges in Big Data
  • Course Summary

Big Data Concepts: Big Data Essentials

Course: 46 Minutes

  • Course Overview
  • Raw Data and Big Data
  • Data Warehousing and Big Data
  • Big Data Computing Systems
  • Horizontal and Vertical Scaling
  • Features, Benefits, and Use Cases of Hadoop
  • Hadoop: Components
  • Hadoop: Migration to the Cloud
  • Hadoop and Cloud Computing
  • Features of Big Data Storage Systems
  • In-memory Storage Systems
  • Course Summary

Non-relational Data: Non-relational Databases

Course: 52 Minutes

  • Course Overview
  • Non-relational Databases
  • The NoSQL Approach
  • Benefits of NoSQL
  • Document Databases
  • Key-value Data Stores
  • Graph Databases
  • Columnar Databases
  • HBase Architecture
  • Multi-model Databases
  • Next Generation NewSQL Databases
  • Course Summary

Big Data Analytics: Techniques for Big Data Analytics

Course: 39 Minutes

  • Course Overview
  • Big Data Analytics Challenges
  • Big Data Analytics Stack Layers
  • Big Data Ingestion
  • The Data Processing Layer
  • The Data Storage Layer
  • Pillars of Big Data Architecture
  • Batch Processing and Big Data
  • Stream Processing and Big Data
  • Lambda Architecture and Use Cases
  • Kappa Architecture
  • Course Summary

Big Data Analytics: Spark for High-speed Big Data Analytics

Course: 51 Minutes

  • Course Overview
  • The Core Characteristics of Apache Spark
  • Components of the Apache Spark Architecture
  • Apache Spark Use Case: Uber Using Spark
  • Apache Spark Use Case: Alibaba Using Spark
  • Apache Spark Use Case: The Healthcare Industry
  • Apache Spark vs. Hadoop
  • Top Apache Spark Use Cases
  • Apache Spark's Main Features
  • Apache Spark Performance Optimization Techniques
  • Apache Spark Best Practices
  • Course Summary

Assessment:

  • Big Data Infrastructures

Track 3: Raw Data to Insights

In this track, the focus will be on data mining and decision making.
Courses (3 hours +)

Data Mining and Decision Making: Modern Data Science Lifecycle

Course: 54 Minutes

  • Course Overview
  • Data Science vs. Data Analysis
  • Data Science Project Steps and Processes
  • Establishing Data Science Project Business Value
  • Data Preparation Processes
  • Using Descriptive Analysis to Drive Decision-making
  • Using Predictive Analytics to Drive Decision-making
  • Interpreting Predictive Models in a Business Context
  • Machine Learning: Model Validation
  • Machine Learning: Model Implementation
  • Case Study: Data-driven Decision-making
  • Course Summary

Data Mining and Decision Making: Data Preparation & Predictive Analytics

Course: 41 Minutes

  • Course Overview
  • Common Industrial and Commercial Data Sources
  • Data Collection Skills and Methods
  • Data Validation Best Practices
  • Data Cleaning Techniques
  • Data Exploration: Summary Statistics
  • Data Exploration: Summary Statistics II
  • Data Exploration: The Power of Visualization
  • Data Exploration: Advanced Visualization
  • Feature Engineering: Feature Generation
  • Feature Engineering: Feature Reduction
  • Course Summary

Data Mining and Decision Making: Data Mining for Answering Business Questions

Course: 1 Hour, 3 Minutes

  • Course Overview
  • Data Science, Data Analytics, and Machine Learning
  • Machine Learning Engineer vs. Data Science
  • Types of Machine Learning
  • How Do Machine Learning Algorithms Work
  • Data Mining: Association Rules
  • Data Mining: Anomaly Detection
  • Data Mining: Customer Segmentation
  • Case Study: Walmart and Market Basket Analysis
  • Case Study: Customer Segmentation Analysis
  • Importance of Predictive Analysis in Business
  • Course Summary

Data Mining and Decision Making: Predictive Analytics for Business Strategies

Course: 57 Minutes

  • Course Overview
  • Data, Deep Learning, and Artificial Neural Networks
  • Artificial Neural Networks
  • Business Problems: Regression
  • Business Problems: Classification
  • Business Problems: Time Series
  • Business Problems: Actionable Recommender Systems
  • Recurrent and Convolutional Neural Networks
  • Natural Language Processing (NLP)
  • Computer Vision
  • World of Analytics: Integrated Futuristic Vision
  • Course Summary

Assessment:

  • Raw Data to Insights

Track 4: Emerging New Age Architectures

In this track, the focus will be on cloud data platforms, data lakes, and modern warehouses.
Courses (5 hours +)

Cloud Data Platforms: Cloud Computing

Course: 52 Minutes

  • Course Overview
  • Cloud Computing and Its Characteristics
  • Cloud Computing: Use Cases and Benefits
  • Cloud Computing Services: Storage and Compute Power
  • Types of Cloud Compute Power
  • Types of Cloud Storage
  • Cloud Computing Models: PaaS, IaaS, SaaS, and FaaS
  • Cloud Computing Model Comparison
  • Components of Cloud Computing Architectures
  • Cloud Service Provider Comparison
  • Cloud Elasticity and Scalability
  • Course Summary

Cloud Data Platforms: Cloud-based Applications & Storage

Course: 53 Minutes

  • Course Overview
  • Deploying Applications on Cloud Platforms
  • Characteristics of Cloud-ready Applications
  • Types of Cloud Deployment Models
  • Cloud Deployment Tools
  • Considerations for Cloud Application Deployment
  • CPU Virtualization, Memory, and I/O Devices
  • Cloud Storage Platforms
  • Cloud Storage Technologies
  • HDFS and Amazon S3
  • Types of Data Centers
  • Course Summary

Cloud Data Platforms: AWS, Azure, & GCP Comparison

Course: 56 Minutes

  • Course Overview
  • Cloud Data Platforms: Amazon Web Services
  • Cloud Data Platforms: Microsoft Azure
  • Cloud Data Platforms: Google Cloud Platform
  • Cloud Analytics
  • Popular Cloud Analytics Tools
  • Cloud Computing Challenges: Security
  • Cloud Computing Challenges: Compliance
  • Cloud Computing Challenges: Cost Management
  • Cloud Computing Challenges: Governance
  • Future of Cloud Computing
  • Course Summary

Data Lakes and Modern Data Warehouses: Data Lakes

Course: 1 Hour, 19 Minutes

  • Course Overview
  • Data Lake Evolution
  • Modern Data Lake Architecture
  • Data Lakes: Key Concepts
  • Data Lake Maturity Stages
  • Data Swamps
  • Data Lake Platforms
  • Data Lake Platforms
  • Governed Data Lakes
  • Data Lakes: Risks and Challenges
  • Data Lakes vs. Data Warehouses
  • Course Summary

Data Lakes and Modern Data Warehouses: Modern Data Warehouses

Course: 1 Hour, 10 Minutes

  • Course Overview
  • Data Warehouses and Its Characteristics
  • Modern Data Warehouses: Key Concepts and Stages
  • Amazon Redshift
  • Google BigQuery
  • Modern Data Warehouses: Architecture and Processes
  • Modern Data Warehouses: Techniques
  • Data Warehouse Solutions: Batch Processing
  • Data Warehouse Solutions: Real-time Processing
  • Data Warehouse Solutions: Streaming Analytics
  • Hybrid Modern Data Warehouse
  • Course Summary

Data Lakes and Modern Data Warehouses: Azure Databricks & Data Pipelines

Course: 1 Hour, 2 Minutes

  • Course Overview
  • Azure Databricks: Features and Architecture
  • Azure Databricks: Pros and Cons
  • Snowflake Data Warehouses: Features and Architecture
  • Snowflake Data Warehouses: Pros and Cons
  • Data Pipelines
  • Components of a Data Pipeline
  • Advantages of a Data Pipeline
  • Types of Data Pipeline Tools
  • Comparing Data Pipeline Tools
  • Building a Data Pipeline
  • Course Summary

Assessment:
Emerging New Age Architectures

Track 5: Data Governance and Management

In this track, the focus will be on modern data management.
Courses (3 hours +)

Modern Data Management: Data Management Systems

Course: 1 Hour, 3 Minutes

  • Course Overview
  • Data Management Strategies
  • Mastering Raw Data
  • Identifying Domains and Data Sources
  • Data Integration across Domains
  • Transactional and Non-transactional Data
  • Data Management Architectures
  • Technical Implementation Considerations
  • Data System Alignment
  • State of Maturity across Domains
  • Metadata Management
  • Course Summary

Modern Data Management: Data Governance

Course: 1 Hour, 6 Minutes

  • Course Overview
  • Data Stewardship
  • Establishing Governance across Domains
  • Data Compliance - Issues and Strategies
  • Data Integration Life Cycle
  • Data Risks and Security
  • Data Protection, Privacy, and Compliance
  • Improving Data Quality through Governance
  • The Entity Resolution Process
  • CRUD Basic Functions
  • Building a Data Governance Business Case
  • Course Summary

Modern Data Management: Data Quality Management

Course: 1 Hour, 8 Minutes

  • Course Overview
  • Data Quality Management in a Business Environment
  • The Data Quality Improvement Cycle
  • Data Quality Management Activities
  • The Importance of Reference Data
  • Improving Data Quality with Data Compliance
  • Data Performance Measurements
  • Continuous Improvement of Data Management
  • Data Management, Governance, and Compliance
  • Solving Data Governance and Compliance Issues
  • Next-generation Cloud Data Management Solutions
  • Course Summary

Assessment:

  • Data Governance and Management

Practice Lab: Data for Leaders and Decision-makers
In this lab, explore technologies, tools, frameworks, and platforms at a high level for enabling the managers and leaders to comfortably get engaged in data projects. Learners will also explore various data compliance issues, data governance, and various data strategies to be adopted for making better data-driven decisions that are critical for the business. Tasks performed in this lab include:
Loading, cleaning, preprocessing and visualization of data using Python Libraries Designing Data Governance Strategy for better data compliance Creating data lake infrastructure for banking company Creating Big Data Architecture for streaming Creating architecture for ecommerce company on Azure cloud Formulating data quality and management steps for manufacturing company Creating predictive model to predict churning of customers Creating time series model for US Stocks with feature engineering

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