Data Analytics Made Easy

Book description

Learn how to gain insights from your data as well as machine learning and become a presentation pro who can create interactive dashboards

Key Features

  • Enhance your presentation skills by implementing engaging data storytelling and visualization techniques
  • Learn the basics of machine learning and easily apply machine learning models to your data
  • Improve productivity by automating your data processes

Book Description

Data Analytics Made Easy is an accessible beginner’s guide for anyone working with data. The book interweaves four key elements:

Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience.

Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components.

Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own.

Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.

What you will learn

  • Understand the potential of data and its impact on your business
  • Import, clean, transform, combine data feeds, and automate your processes
  • Influence business decisions by learning to create engaging presentations
  • Build real-world models to improve profitability, create customer segmentation, automate and improve data reporting, and more
  • Create professional-looking and business-centric visuals and dashboards
  • Open the lid on the black box of AI and learn about and implement supervised and unsupervised machine learning models

Who this book is for

This book is for beginners who work with data and those who need to know how to interpret their business/customer data. The book also covers the high-level concepts of data workflows, machine learning, data storytelling, and visualizations, which are useful for managers. No previous math, statistics, or computer science knowledge is required.

Table of contents

  1. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
      1. Download the data files
      2. Download the color images
      3. Conventions used
    4. Get in touch
    5. Share your thoughts
  2. What is Data Analytics?
    1. Three types of data analytics
      1. Descriptive analytics
      2. Predictive analytics
      3. Prescriptive analytics
      4. Data analytics in action
    2. Who is involved in data analytics?
    3. Technology for data analytics
    4. The data analytics toolbox
    5. From data to business value
    6. Summary
  3. Getting Started with KNIME
    1. KNIME in a nutshell
    2. Moving around in KNIME
      1. Nodes
    3. Hello World in KNIME
      1. CSV Reader
      2. Sorter
      3. Excel Writer
    4. Cleaning data
      1. Excel Reader
      2. Duplicate Row Filter
      3. String Manipulation
      4. Row Filter
      5. Missing Value
      6. Column Filter
      7. Column Rename
      8. Column Resorter
      9. CSV Writer
    5. Summary
  4. Transforming Data
    1. Modeling your data
    2. Combining tables
      1. Joiner
    3. Aggregating values
      1. GroupBy
      2. Pivoting
    4. Tutorial: Sales report automation
      1. Concatenate
      2. Number To String
      3. Math Formula
      4. Group Loop Start
      5. Loop End
      6. String to Date&Time
      7. Date&Time-based Row Filter
      8. Table Row to Variable
      9. Extract Date&Time Fields
      10. Line Plot
      11. Image Writer (Port)
    5. Summary
  5. What is Machine Learning?
    1. Introducing artificial intelligence and machine learning
    2. The machine learning way
      1. Scenario #1: Predicting market prices
      2. Scenario #2: Segmenting customers
      3. Scenario #3: Finding the best ad strategy
      4. The business value of learning machines
    3. Three types of learning algorithms
      1. Supervised learning
      2. Unsupervised learning
      3. Reinforcement learning
      4. Selecting the right learning algorithm
    4. Evaluating performance
      1. Regression
      2. Classification
      3. Underfitting and overfitting
      4. Validating a model
      5. Pulling it all together
    5. Summary
  6. Applying Machine Learning at Work
    1. Predicting numbers through regressions
      1. Statistics
      2. Partitioning
      3. Linear regression algorithm
      4. Linear Regression Learner
      5. Regression Predictor
      6. Numeric Scorer
    2. Anticipating preferences with classification
      1. Decision tree algorithm
      2. Decision Tree Learner
      3. Decision Tree Predictor
      4. Scorer
      5. Random forest algorithm
      6. Random Forest Learner
      7. Random Forest Predictor
      8. Moving Aggregation
      9. Line Plot (local)
    3. Segmenting consumers with clustering
      1. K-means algorithm
      2. Numeric Outliers
      3. Normalizer
      4. k-Means
      5. Denormalizer
      6. Color Manager
      7. Scatter Matrix (local)
      8. Conditional Box Plot
    4. Summary
  7. Getting Started with Power BI
    1. Power BI in a nutshell
    2. Walking through Power BI
      1. Loading data
      2. Transforming data
      3. Defining the data model
      4. Building visuals
    3. Tutorial: Sales Dashboard
    4. Summary
  8. Visualizing Data Effectively
    1. What is data visualization?
    2. A chart type for every message
      1. Bar charts
      2. Line charts
      3. Treemaps
      4. Scatterplots
    3. Finalizing your visual
    4. Summary
  9. Telling Stories with Data
    1. The art of persuading others
    2. The power of telling stories
    3. The data storytelling process
      1. Setting objectives
      2. Selecting scenes
        1. Evolution
        2. Comparison
        3. Relationship
        4. Breakdown
        5. Distribution
      3. Applying structure
        1. Beginning
        2. Middle
        3. End
      4. Polishing scenes
        1. Focusing attention
        2. Making scenes accessible
      5. Finalizing your story
      6. The data storytelling canvas
    4. Summary
  10. Extending Your Toolbox
    1. Getting started with Tableau
    2. Python for data analytics
      1. A gentle introduction to the Python language
      2. Integrating Python with KNIME
    3. Automated machine learning
      1. AutoML in action: an example with
    4. Summary
  11. And now?
  12. Useful Resources
    1. Chapter 1
    2. Chapter 2
    3. Chapter 3
    4. Chapter 4
    5. Chapter 5
    6. Chapter 6
    7. Chapter 7
    8. Chapter 8
    9. Chapter 9
    10. Why subscribe?
  13. Other Books You May Enjoy
  14. Index

Product information

  • Title: Data Analytics Made Easy
  • Author(s): Andrea De Mauro
  • Release date: August 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781801074155