A Practical Introduction to Data Science Training Course

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

Summary and Conclusion