WA3169 Data Science and Data Engineering in 2022 Training
Data Science techniques like ML and AI insights are leading to new opportunities for businesses and public sector when they have quality data. Data governance frameworks help with management plans for Master Data. Managing todays “Big Data” means the Data Engineer and Data Scientist needs an ever increasing array of tools and scripts to provide that quality master data.
In this data science and data engineering course investigate how your organization can take advantage of the latest data opportunity trends through both technical and management perspectives.
Topics
• The Rise of Data
• Tools and Scripting
• Data Management and Technical Tools
• Future trends
Audience
Business Analysts, Developers, IT Architects, and Technical Managers
Prerequisites
Basic awareness of computing and Internet concepts, and an interest in extracting insights from data.
Duration
1 day
Outline for Data Science and Data Engineering in 2022 Training
Chapter 1. The Rise of Data
- Data Collection
- Different Types of Data
- Gartner's Definition of Big Data
- Veracity
- A Practical Definition of Big Data
- Challenges Posed by Big Data
- Enter Distributed Computing
- What is Data Engineering
- The Team Players
- The Data Scientist
- The Data Engineer
- Data-Related Roles
- Knowledge Requirements
- The Overlapping Data Fields
- The Data Engineer (DE) Role
- The Data Scientist (DS) Role
- DE/DS Core Skills and Competencies
- An Example of a Data Product
- What is Data Wrangling (Munging)?
- The Data Exchange Interoperability Options
- APIs
- Summary
Chapter 2. Tools and Scripting
- Data Storage
- Data Storage Technologies
- Apache Hadoop
- Hadoop Ecosystem Projects
- Storing Raw Data in HDFS and Schema-on-Demand
- Apache Spark
- Storing Data in Spark
- Apache Cassandra
- MongoDB
- Talend Data Fabric
- Databricks
- Amazon SageMaker
- Snowflake
- Qubole
- Apache Kafka
- Apache Airflow
- Apache Storm
- TensorFlow
- Terraform by Hashicorp
- PySpark
- Amazon Cloud Development Kit (CDK)
- Summary
Chapter 3. Data Management and Technical Tools
- DAMA International
- Data Governance
- Data Governance Strategy
- Data Governance Framework
- Master Data
- Data Governance Models
- Data Governance Tools
- Comparison Table For Data Governance Tools
- Data Quality
- The 6C Data Quality Framework
- Data Integrity
- Disparate Databases Integration
- Business Intelligence
- The Five Key Stages of Business Intelligence
- The Five Key Stages of Business Intelligence (cont)
- Tasks Where BI is Used
- Analytics Software
- Analytics Software (cont)
- Four Types of Data Analysis
- Four Types of Data Analysis (cont)
- What is DataOps
- Summary
Chapter 4. Future Trends
- Big Trends in 2021
- MLOps
- DataOps
- Computer Vision(CV)
- Natural Language Processing (NLP)
- Trends in 2022
- DATAOps 2.0
- Data Fabric
- Cloud-Native platforms
- Hybrid Forms of Automation
- AI-As-a-Service Platforms
- Augmented Data Management
- Business Intelligence for Performance
- Data Mesh
- AI Engineering
- Machine Learning Services
- NLP for Smaller Languages
- Fairness and Privacy as a Mega-trend
- Computer Vision - 3D
- MLOps in 2022
- Summary