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)

Details

Subject(s)
Author
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&amp
  • Time
  • Date&amp
  • Time-based Row Filter
  • Table Row to Variable
  • Extract Date&amp
  • 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.
ISBN
1-80107-458-5
OCLC
1267766552
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