Data Analytics Made Easy

Book description

Make informed decisions using data analytics, machine learning, and data visualizations

Key Features

  • Take raw data and transform it to add value to your organization
  • Learn the art of telling stories with your data to engage with your audience
  • Apply machine learning algorithms to your data with a few clicks of a button

Book Description

Data analytics has become a necessity in modern business, and skills such as data visualization, machine learning, and digital storytelling are now essential in every field. If you want to make sense of your data and add value with informed decisions, this is the book for you.

Data Analytics Made Easy is an accessible guide to help you start analyzing data and quickly apply these skills to your work. It focuses on how to generate insights from your data at the click of a few buttons, using the popular tools KNIME and Microsoft Power BI.

The book introduces the concepts of data analytics and shows you how to get your data ready and apply machine learning algorithms. Implement a complete predictive analytics solution with KNIME and assess its level of accuracy. Create impressive visualizations with Microsoft Power BI and learn the greatest secret in successful analytics – how to tell a story with your data. You'll connect the dots on the various stages of the data-to-insights process and gain an overview of alternative tools, including Tableau and H20 Driverless AI.

By the end of this book, you will have learned how to implement machine learning algorithms and sell the results to your customers without writing a line of code.

What you will learn

  • Understand the potential of data and its impact on any business
  • Influence business decisions with effective data storytelling when delivering insights
  • Use KNIME to import, clean, transform, combine data feeds, and automate recurring workflows
  • Learn the basics of machine learning and AutoML to add value to your organization
  • Build, test, and validate simple supervised and unsupervised machine learning models with KNIME
  • Use Power BI and Tableau to build professional-looking and business-centric visuals and dashboards

Who this book is for

Whether you are working with data experts or want to find insights in your business' data, you'll find this book an effective way to add analytics to your skill stack.

No previous math, statistics, or computer science knowledge is required.

Publisher resources

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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, Francesco Marzoni, Andrew J. Walter
  • Release date: August 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781801074155