Tag: Data Collection

Installation and Configuration

• Zabbix architecture
• Installation: Server, WEB interface and agent
• Initial configuration
• Zabbix definitions
• WEB interface overview

Data Collection and Monitoring

• Configuration management by using templates
• Agent-less monitoring
• Data collection – hosts, Zabbix agent checks
• Data visualization – simple graphs, overview
• Problem detection – triggers, less sensitive triggers, dependencies
• Data collection – aggregate and calculated checks
• Data visualization – custom graphs

Events and Alerts

• Events
• Notifications and escalations – media types, actions, operations

Zabbix Administration and Monitoring

• Automatic recovery by using remote commands
• Host and host group level maintenance
• Zabbix Administration – working time, housekeeper, authentication, front-end scripts
• Managing permissions – users and user groups, audit
• Automatic discovery – network discovery, active agent auto-registration
• Command line utilities: zabbix_get, zabbix_sender

Maintenance

• Proxy overview
• Maintenance and operations – best practices, DB sizing, upgrades

Introduction

• The Data Science Process
• Roles and responsibilities of a Data Scientist

Preparing the Development Environment

• Libraries, frameworks, languages and tools
• Local development
• Collaborative web-based development

Data Collection

• Different Types of Data
  • Structured
    • Local databases
    • Database connectors
    • Common formats: xlxs, XML, Json, csv, …
  • Un-Structured
    • Clicks, censors, smartphones
    • APIs
    • Internet of Things (IoT)
    • Documents, pictures, videos, sounds
• Case study: Collecting large amounts of unstructured data continuosly

Data Storage

• Relational databases
• Non-relational databases
• Hadoop: Distributed File System (HDFS)
• Spark: Resilient Distributed Dataset (RDD)
• Cloud storage

Data Preparation

• Ingestion, selection, cleansing, and transformation
• Ensuring data quality – correctness, meaningfulness, and security
• Exception reports

Languages used for Preparation, Processing and Analysis

• R language
  • Introduction to R
  • Data manipulation, calculation and graphical display
• Python
  • Introduction to Python
  • Manipulating, processing, cleaning, and crunching data

Data Analytics

• Exploratory analysis
  • Basic statistics
  • Draft visualizations
  • Understand data 
• Causality
• Features and transformations
• Machine Learning
  • Supervised vs unsurpevised
  • When to use what model
• Natural Language Processing (NLP)

Data Visualization

• Best Practices
• Selecting the right chart for the right data
• Color pallets
• Taking it to the next level
  • Dashboards
  • Interactive Visualizations
• Storytelling with data