Confident Data Skills 2nd Edition by Kirill Eremenko 1789664393 9781789664393

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Product details:

ISBN-10 :  1789664393 

ISBN-13 :  9781789664393

Author:  Kirill Eremenko 

Data has dramatically changed how our world works. Understanding and using data is now one of the most transferable and desirable skills. Whether you’re an entrepreneur wanting to boost your business, a jobseeker looking for that employable edge, or simply hoping to make the most of your current career, Confident Data Skills is here to help. This updated second edition takes you through the basics of data: from data mining and preparing and analysing your data, to visualizing and communicating your insights. It now contains exciting new content on neural networks and deep learning. Featuring in-depth international case studies from companies including Amazon, LinkedIn and Mike’s Hard Lemonade Co, as well as easy-to understand language and inspiring advice and guidance, Confident Data Skills will help you use your new-found data skills to give your career that cutting-edge boost. About the Confident series… From coding and web design to data, digital content and cyber security, the Confident books are the perfect beginner’s resource for enhancing your professional life, whatever your career path.

Confident Data Skills 2nd Table of contents:

PART ONE ‘What is it?’ Key principles
Striding out
The future is data
Arresting developments
01 Defining data
Data is everywhere
Big (data) is beautiful
Storing and processing data
Data can generate content
Using data
Why data matters now
Worrying achieves nothing
References
Notes
02 How data fulfils our needs
The permeation of data
Data science and physiology
Data science and safety
Data science and belonging
Data science and esteem
Data science and self-actualization
Some final thoughts
References
Notes
03 AI and our future
What is artificial intelligence?
Artificial general intelligence
Narrow AI
Robotic process automation
Computer vision
Natural language processing
Reinforcement learning and deep learning
The dark side of AI
Prepare for Part Two
References
Notes
PART TWO ‘When and where can I get it?’ Data gathering and analysis
The Data Science Process
Getting started
04 Identify the question
Look, ma, no data!
How do you solve a problem like…
Timekeeping
The art of saying no
Onward!
References
Notes
05 Data preparation
Encouraging data to talk
With great power comes great responsibility
Preparing your data for a journey
Reference
Notes
06 Data analysis: Part 1
Don’t skip this step
Classification and clustering
Classification
Decision trees
Random forest
K-nearest neighbours (K-NN)
Naive Bayes
Classification with Naive Bayes
Logistic regression
Clustering
K-means clustering
Hierarchical clustering
References
Notes
07 Data analysis: Part 2
Reinforcement learning
The multi-armed bandit problem
Upper confidence bound
Thompson sampling
Upper confidence bound vs Thompson sampling: Which is preferable?
Deep learning
Weight training: How neural networks learn
The future for data analysis
References
Notes
PART THREE ‘How can I present it?’ Communicating data
Looking good
You’re not finished yet!
The career shaper
08 Data visualization
What is visual analytics?
What is data visualization?
Speaking a visual language
Steps to creating compelling visuals
Final thoughts
References
Notes
• Looking further: Types of chart
09 Data presentation
The importance of storytelling
Creating data advocacy
How to create a killer presentation
The end of the process
References
Notes
10 Your career in data science
Breaking in
Applying for jobs
Preparing for an interview
Interviewing them
Nurturing your career within a company

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