Data Analytics for Absolute Beginners: a Deconstructed Guide to Data Literacy

2019-07-21

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Author: Oliver Theobald
Publisher:
ISBN: 9781081762469
Category :
Languages : en
Pages : 88

While exposure to data has become more or less a daily ritual for the rank-and-file knowledge worker, true understanding-treated in this book as data literacy-resides in knowing what lies behind the data. Everything from the data's source to the specific choice of input variables, algorithmic transformations, and visual representation shape the accuracy, relevance, and value of the data and mark its journey from raw data to business insight. It's also important to grasp the terminology and basic concepts of data analytics as much as it is to have the financial literacy to be successful as a decisionmaker in the business world. In this book, we make sense of data analytics without the assumption that you understand specific data science terminology or advanced programming languages to set you on your path. Topics covered in this book: Data Mining Big Data Machine Learning Alternative Data Data Management Web Scraping Regression Analysis Clustering Analysis Association Analysis Data Visualization Business Intelligence

Introduction to Google Analytics

2017-06-19

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Author: Todd Kelsey
Publisher: Apress
ISBN: 1484228294
Category : Business & Economics
Languages : en
Pages : 148

Develop your digital/online marketing skills and learn web analytics to understand the performance of websites and ad campaigns. Approaches covered will be immediately useful for business or nonprofit organizations. If you are completely new to Google Analytics and you want to learn the basics, this guide will introduce you to the content quickly. Web analytics is critical to online marketers as they seek to track return on investment and optimize their websites. Introduction to Google Analytics covers the basics of Google Analytics, starting with creating a blog, and monitoring the number of people who see the blog posts and where they come from. What You’ll Learn Understand basic techniques to generate traffic for a blog or website Review the performance of a website or campaign Set up a Shopify account to track ROI Create and maximize AdWords to track conversion Discover opportunities offered by Google, including the Google Individual Qualification Who This Book Is For Those who need to get up to speed on Google Analytics tools and techniques for business or personal use. This book is also suitable as a student reference.

Data Analytics for Beginners

2016-11-03

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Author: Paul Kinley
Publisher:
ISBN: 9781539896739
Category :
Languages : en
Pages : 86

DATA ANALYTICS FOR BEGINNER: IN ORDER TO SUCEED IN TODAYS'Ss FAST PACE BUSINESS ENVIRONEMNT, YOU NEED TO MASTER DATA ANALYTICS. Data Analytics is the most powerful tool to analyze today's business environment and to predict future developments. Is it not the dream of every business owner to know exactly what the customer will buy in 6 months or what the new product hype will look like in your OWN industry? Data Analytics is the tool that will bring you answers to these questions. Here's why Data Analytics for Beginners will bring your business to a complete new level: How you can use data analytics to improve your business How to plan data analysis to know exactly what your target group wants How to implement descriptive analysis You will learn the exact techniques that are required to master Data Analytics Our customer's feedback I am the owner of a home supplies shop with 15 employees and this book improved the sales by 18,5% during the last 3 months. Richard S., Boston. Data Analytics for Beginners was a eye opener for me and my business. With this book I research all of my products on sale and my skills about the market I am in enhanced drastically. I can recommend this book to everyone that is planning to improve the business. Anamda R., Sacramento. During my IT studies this book supported me a lot with anaylsis about future business trends. This book has a easy to understand writing style without any expert language. In other words: every beginner can work with this book right away.Thomas E., Baltimore. Here's what you will get Planning a Study Surveys Experiments Gathering Data How to select useful samples Avoiding Bias in Data Sets Descriptive Analysis Mean Median Mode Variance Standard Deviation Coefficient of Variation Pie Charts How to create Pie Charts in Excel Bar Graphs How to Create Bar Charts in Excel Time Charts and Line Charts How to create a time chart in excel How to create a line chart in excel Histograms How to create a histogram in Excel Scatter Plots How to create a Scatter Chart in Excel Business Intelligence Data Analytics in Business and Industry

Data Analytics for Beginners

2017-10

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Author: Robert J. Woz
Publisher: Createspace Independent Publishing Platform
ISBN: 9781977843135
Category : Big data
Languages : en
Pages : 112

If you are convinced that the world today is producing more data than the previous decades, then you understand that processing yesterday's data for today's use at times is not enough. The level of data analysis that is needed in highly competitive business environment needs to be processed, analyzed and used immediately for businesses to be ahead of their competition. Having this in mind, you need to understand from the ground up, what data is, the different types of data and how you should identify the right data for your business. To help you understand the simple basics of data and how it needs to be analyzed, then Data Analytics for Beginners is the book that you have been waiting for. The size and type of business you are running doesn't matter because after all, it will depend on your ability to understand the data that your business is exposed to so as to make better business decisions for the current working environment and the future. Are there patterns in your business that you cannot see? Do you want to make sense of the shopping trends of your clients to better enrich their experience? Do you want to know your target market even more? Do you want to better derive insights from the feedback your clients give you? These questions can only be answered when you perform a data analysis for your business. Collecting the data is one thing, analyzing them is another matter entirely as it is not something that can be done haphazardly by just looking at the data. If you hope to understand your data well, you need to understand the data you are collecting, the methods to use and the right tools to use when analyzing the data. Inside you will find valuable steps and tools that will help make your information work for you. Do not let yourself get complacent, stop looking at the data that you collect each day and start analyzing your data to move your business up. Get started by buying this book today! Inside you will find How data should be understood? Terms and concepts used in data analysis. Data mining and the different kinds of databases used to store data. How information can be retrieved and manipulated in the database to create a visual representation of what you want to know? The life cycle of data analysis. And more...

Python Machine Learning for Beginners

2019-05-13

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Author: Leonard Deep
Publisher:
ISBN: 9781097858309
Category :
Languages : en
Pages : 236

Are you interested to get into the programming world? Do you want to learn and understand Python and Machine Learning? Python Machine Learning for Beginners is the guide for you. Python Machine Learning for Beginners is the ultimate guide for beginners looking to learn and understand how Python programming works. Python Machine Learning for Beginners is split up into easy to learn chapters that will help guide the readers through the early stages of Python programming. It's this thought out and systematic approach to learning which makes Python Machine Learning for Beginners such a sought-after resource for those that want to learn about Python programming and about Machine Learning using an object-oriented programming approach. Inside Python Machine Learning for Beginners you will discover: An introduction to Machine Learning The main concepts of Machine Learning The basics of Python for beginners Machine Learning with Python Data Processing, Analysis, and Visualizations Case studies and much more! Throughout the book, you will learn the basic concepts behind Python programming which is designed to introduce you to Python programming. You will learn about getting started, the keywords and statements, data types and type conversion. Along with different examples, there are also exercises to help ensure that the information sinks in. You will find this book an invaluable tool for starting and mastering Machine Learning using Python. Once you complete Python Machine Learning for Beginners, you will be more than prepared to take on any Python programming. Scroll back up to the top of this page and hit BUY IT NOW to get your copy of Python Machine Learning for Beginners! You won't regret it!

Data Analytics for Absolute Beginners

2017-04-30

PDF Data Analytics for Absolute Beginners Download

Author: O. Theobald
Publisher:
ISBN: 9781521159453
Category :
Languages : en
Pages : 109

Start here if you are: A marketing professional, financial analyst, politician, CEO, professional coach, student or a decision maker in an organization. This book is the start of the road to becoming a data scientist or data literate professional. In today's modern world it's vital t to understand data analytics. This includes the various processes, resources, advantages and limitations of data analytics. It's important that you can grasp the terminology and basic concepts of data analytics just as much as you need to understand basic accounting and financial literacy to be a successful decision maker in the business world. This book is ideal for anyone who is interested in making sense of data analytics without the assumption that you understand specific data science terminology or advanced programming languages. Topics covered: Regression AnalysisData MiningMachine Learning Data ReductionClusteringAnomaly DetectionText MiningAssociation AnalysisData Visualization

Data Science For Dummies

2021-08-20

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Author: Lillian Pierson
Publisher: John Wiley & Sons
ISBN: 1119811619
Category : Computers
Languages : en
Pages : 436

Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today.

Blockchain Data Analytics For Dummies

2020-09-02

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Author: Michael G. Solomon
Publisher: John Wiley & Sons
ISBN: 1119651751
Category : Computers
Languages : en
Pages : 355

Get ahead of the curve—learn about big data on the blockchain Blockchain came to prominence as the disruptive technology that made cryptocurrencies work. Now, data pros are using blockchain technology for faster real-time analysis, better data security, and more accurate predictions. Blockchain Data Analytics For Dummies is your quick-start guide to harnessing the potential of blockchain. Inside this book, technologists, executives, and data managers will find information and inspiration to adopt blockchain as a big data tool. Blockchain expert Michael G. Solomon shares his insight on what the blockchain is and how this new tech is poised to disrupt data. Set your organization on the cutting edge of analytics, before your competitors get there! Learn how blockchain technologies work and how they can integrate with big data Discover the power and potential of blockchain analytics Establish data models and quickly mine for insights and results Create data visualizations from blockchain analysis Discover how blockchains are disrupting the data world with this exciting title in the trusted For Dummies line!

Getting Started with Data Science

2015-12-14

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Author: Murtaza Haider
Publisher: IBM Press
ISBN: 0133991237
Category : Business & Economics
Languages : en
Pages : 942

Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.

Deep Learning

2019-09-10

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Author: John D. Kelleher
Publisher: MIT Press
ISBN: 0262537559
Category : Computers
Languages : en
Pages : 298

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.