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.
Table of Contents
What is Data Analytics?
Getting Started with KNIME
Transforming Data
What is Machine Learning?
Applying Machine Learning at Work
Getting Started with Power BI
Visualizing Data Effectively
Telling Stories with Data
Extending Your Toolbox
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Author(s): De Mauro, Andrea
Publisher: Packt Publishing
Year: 2021
Language: English
Commentary: Make informed decisions using data analytics, machine learning, and data visualizations
Pages: 407
Tags: Make informed decisions using data analytics, machine learning, and data visualizations
Cover
Copyright
Contributors
Table of Contents
Preface
Chapter 1: What is Data Analytics?
Three types of data analytics
Descriptive analytics
Predictive analytics
Prescriptive analytics
Data analytics in action
Who is involved in data analytics?
Technology for data analytics
The data analytics toolbox
From data to business value
Summary
Chapter 2: Getting Started with KNIME
KNIME in a nutshell
Moving around in KNIME
Nodes
Hello World in KNIME
 CSV Reader
 Sorter
 Excel Writer
Cleaning data
 Excel Reader
 Duplicate Row Filter
 String Manipulation
 Row Filter
 Missing Value
 Column Filter
 Column Rename
 Column Resorter
 CSV Writer
Summary
Chapter 3: Transforming Data
Modeling your data
Combining tables
 Joiner
Aggregating values
 GroupBy
 Pivoting
Tutorial: Sales report automation
 Concatenate
 Number To String
 Math Formula
 Group Loop Start
 Loop End
 String to Date&Time
 Date&Time-based Row Filter
 Table Row to Variable
 Extract Date&Time Fields
 Line Plot
 Image Writer (Port)
Summary
Chapter 4: What is Machine Learning?
Introducing artificial intelligence and machine learning
The machine learning way
Scenario #1: Predicting market prices
Scenario #2: Segmenting customers
Scenario #3: Finding the best ad strategy
The business value of learning machines
Three types of learning algorithms
Supervised learning
Unsupervised learning
Reinforcement learning
Selecting the right learning algorithm
Evaluating performance
Regression
Classification
Underfitting and overfitting
Validating a model
Pulling it all together
Summary
Chapter 5: Applying Machine Learning at Work
Predicting numbers through regressions
 Statistics
 Partitioning
Linear regression algorithm
 Linear Regression Learner
 Regression Predictor
 Numeric Scorer
Anticipating preferences with classification
Decision tree algorithm
 Decision Tree Learner
 Decision Tree Predictor
 Scorer
Random forest algorithm
 Random Forest Learner
 Random Forest Predictor
 Moving Aggregation
 Line Plot (local)
Segmenting consumers with clustering
K-means algorithm
 Numeric Outliers
 Normalizer
 k-Means
 Denormalizer
 Color Manager
 Scatter Matrix (local)
 Conditional Box Plot
Summary
Chapter 6: Getting Started with Power BI
Power BI in a nutshell
Walking through Power BI
Loading data
Transforming data
Defining the data model
Building visuals
Tutorial: Sales Dashboard
Summary
Chapter 7: Visualizing Data Effectively
What is data visualization?
A chart type for every message
Bar charts
Line charts
Treemaps
Scatterplots
Finalizing your visual
Summary
Chapter 8: Telling Stories with Data
The art of persuading others
The power of telling stories
The data storytelling process
Setting objectives
Selecting scenes
Evolution
Comparison
Relationship
Breakdown
Distribution
Applying structure
Beginning
Middle
End
Polishing scenes
Focusing attention
Making scenes accessible
Finalizing your story
The data storytelling canvas
Summary
Chapter 9: Extending Your Toolbox
Getting started with Tableau
Python for data analytics
A gentle introduction to the Python language
Integrating Python with KNIME
Automated machine learning
AutoML in action: an example with H2O.ai
Summary
And now?
Useful Resources
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
PacktPage
Other Books You May Enjoy
Index
