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Data analytics made easy : use machine learning and data storytelling in your work without writing any code / Andrea de Mauro, Francesco Marzoni, Andy Walter.
Author
Mauro, Andrea de
[Browse]
Format
Book
Language
English
Published/Created
Birmingham, England ; Mumbai : Packt Publishing, [2021]
©2021
Description
1 online resource (407 pages)
Availability
Available Online
O'Reilly Online Learning: Academic/Public Library Edition
Details
Subject(s)
Data mining
[Browse]
Machine learning
[Browse]
Author
Walter, Andy
[Browse]
Marzoni, Francesco
[Browse]
Series
Expert Insight
Summary note
This book takes away the fear of working with, analyzing, and visualizing data. Understand the key concepts involved with data analytics while working with real-world business examples. You are introduced to two fantastic tools to cleanse and analyze data (KNIME) and visualize your insights (Microsoft Power BI), but the principles from this.
Source of description
Description based on print version record.
Contents
Intro
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
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)
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
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
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
Chapter 7: Visualizing Data Effectively
What is data visualization?
A chart type for every message
Bar charts
Line charts
Treemaps
Scatterplots
Finalizing your visual
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
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
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.
Show 155 more Contents items
ISBN
1-80107-458-5
OCLC
1267766552
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