Data Analytics Made Easy: Use machine learning and data storytelling in your work without writing any code

by Andrea De Mauro

Length: 358 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2021-08-30
ISBN-10: 1801074151
ISBN-13: 9781801074155
Sales Rank: #1079434 (See Top 100 Books)

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Print Book Look Inside

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.

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

Preface
    Who this book is for
    What this book covers
    To get the most out of this book
        Download the data files
        Download the color images
        Conventions used
    Get in touch
    Share your thoughts
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
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
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
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
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
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
Visualizing Data Effectively
    What is data visualization?
    A chart type for every message
        Bar charts
        Line charts
        Treemaps
        Scatterplots
    Finalizing your visual
    Summary
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
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
    Why subscribe?
Other Books You May Enjoy
    Share your thoughts
Index

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