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+46 (0)8 506 668 00
+46 (0)8 506 668 00

Introduction to Data Analytics

Course 1290

  • Duration: 3 days
  • Language: English
  • Level: Foundation

As data evolves for organisations, employees must understand the value of the data they hold. This Data Analytics Introduction provides a clear understanding of data analytics's purpose, tools, and techniques. In addition, it will help attendees to plan the data and digital strategy for their organisations.

Data Analytics Introduction Delivery Methods

  • In-Person

  • Online

Data Analytics Introduction Training Information

Back at work, attendees will be able to:

  • Define what Data Analytics is and how it helps with business-focused decision-making
  • Understand the fundamentals of pattern recognition
  • Differentiate between data roles such as Data Analyst, Data Scientist, Data Engineer, Business Analyst, and Business Intelligence Analyst.
  • Recognise the value, terminology, and challenges of Business Intelligence
  • Understand how Data Mining builds knowledge, insights, patterns, & data advantages
  • Appreciate the usefulness of data visualisation, visual patterns, and Infographics for stakeholder communication
  • Improve awareness of the value of the data your organisation holds and how to manipulate it
  • Have excellent fundamental knowledge of data, how it is captured, and how it is visualised for us in the business
  • Position Data Warehouses as data management facilities that help to:
    • Create reports and analysis
    • Support managerial decision making
    • Engineered for efficient reporting and querying

    Training Prerequisites

    A basic understanding of what data is and the function of data analysis

    Certification Information

    Learning Tree Exam included

    Data Analytics Introduction Training Outline

    Business Intelligence

    • Example: MoneyBall: Data Mining in Sports

    Pattern Recognition

    • Types of Patterns
    • Finding a Pattern
    • Uses of Patterns

    The Data Processing Chain

    • Data Database
    • Data Warehouse
    • Data Mining
    • Data Visualisation

    Data Analytics Terminology and Careers

    Review Wheel

    Introduction

    • Example: Schools and Academies
    • BI in Education

    BI for Better Decisions

    Decision types

    • BI Tools
    • BI Skills

    BI Applications

    • Customer Relationship Management
    • Healthcare and Wellness
    • Education
    • Retail Banking
    • Financial Services
    • Insurance Manufacturing
    • Supply Chain Management
    • Telecom
    • Public Sector

    Conclusion

    Review Wheel

    Case Study Exercise

    Introduction

    • Example: University Health System – BI in Healthcare

    Design Considerations for DW

    DW Development Approaches

    • DW Architecture
    • Data Sources
    • Data Loading Processes

    Data Warehouse Design

    • DW Access
    • DW Best Practices
    • Data Lakes

    Conclusion

    Review Wheel

    Case Study Exercise: Step 2

    Introduction

    • Example: Target Corp – Data Mining in Retail

    Gathering and selecting data

    • Data cleansing and preparation
    • Outputs of Data Mining
    • Evaluating Data Mining Results

    Data Mining Techniques

    • Tools and Platforms for Data Mining
    • Data Mining Best Practices
    • Myths about data mining
    • Data Mining Mistakes

    Conclusion

    Review Wheel

    Case Study Exercise: Step 3

    Introduction

    • Example: Dr Hans Gosling - Visualising Global Public Health

    Excellence in Visualisation

    • Types of Charts
    • Visualisation Example

    Tips for Data Visualisation

    Conclusion

    Review Wheel

    Case Study Exercise: Step 4

    Decision Trees

    • Introduction
    • Example: Predicting Heart Attacks using Decision Trees
    • Decision Tree problem
    • Decision Tree Construction

    Regression and Time Series Analysis

    • Correlations and Relationships
    • A visual look at relationships
    • Regression
    • Non-linear regression
    • Logistic Regression
    • Advantages and Disadvantages of Regression
    • Time Series Analysis

    Artificial Neural Networks

    • Introduction
    • Example: IBM Watson - Analytics in Medicine
    • Principles of an Artificial Neural Network
    • Business Applications of ANN Design
    • Representation of a Neural Network
    • Architecting a Neural Network
    • Developing an ANN
    • Advantages and Disadvantages of using ANNs
    • Conclusion
    Get This Course 19 530 kr For Team Prices Call: +46 (0)8 506 668 00

    • 3-day instructor-led training course

    • Hands-on labs included

    • Learning Tree Exam included

    • After-course coaching available

    #1290
    • jun 3 - 5 10:00 - 17:30 CEST
      London or Virtual
    • jun 3 - 5 9:00 - 16:30 CEST
      Stockholm or Virtual
    • jun 16 - 18 15:00 - 22:30 CEST
      Ottawa or Virtual
    • aug 27 - 29 15:00 - 22:30 CEST
      Herndon, VA or Virtual
    • sep 3 - 5 10:00 - 17:30 CEST
      London or Virtual
    • sep 3 - 5 9:00 - 16:30 CEST
      Stockholm or Virtual
    • sep 24 - 26 15:00 - 22:30 CEST
      Ottawa or Virtual
    • nov 12 - 14 15:00 - 22:30 CET
      Herndon, VA or Virtual
    • dec 10 - 12 9:00 - 16:30 CET
      Stockholm or Virtual
    • dec 10 - 12 10:00 - 17:30 CET
      London or Virtual
    • dec 17 - 19 15:00 - 22:30 CET
      Ottawa or Virtual
    • feb 11 - 13 15:00 - 22:30 CET
      Herndon, VA or Virtual
    • mar 25 - 27 14:00 - 21:30 CET
      Ottawa or Virtual

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    • Bring this or any training to your organisation

    • Full-scale programme development

    • Delivered when, where, and how you want it

    • Blended learning models

    • Tailored content

    • Expert team coaching

    #1290
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    Data Analytics Introduction FAQs

    40% of the course time is spent in hands-on exercises

    Yes, this course is a perfect starting point for someone entering Data Analytics

    No, this course is technology neutral

    • This course applies to anyone looking for an introduction to data analytics, including data warehousing, data mining, and data visualisation.
    • Senior Managers, Business Analysts, Data Analysts, Data Scientists, Database Administrators
    • They come from all types of industries, with data being the common element, i.e., Any industry that needs help leveraging the advantages of data analytics and the data analytics industry itself.
    • Senior Managers, Business Analysts, Data Analysts, Data Scientists, and Database Administrators.
      • Senior managers will find it helpful to understand data analytics's advantages for the business.
      • Business analysts will find this course helpful in understanding the eventual output of requirements and modelling.
      • Students wishing to pursue a course in Data Science or Data Analysis will find this course useful as it gives an overview of the Data Analytics field.

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