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

Author: Oliver Theobald

Publisher:

Published: 2019-07-21

Total Pages: 88

ISBN-13: 9781081762469

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

Data Analytics for Absolute Beginners

Author: O. Theobald

Publisher:

Published: 2017-04-30

Total Pages: 109

ISBN-13: 9781521159453

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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 Analytics for Beginners

Author: Paul Kinley

Publisher:

Published: 2016-11-03

Total Pages: 86

ISBN-13: 9781539896739

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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

Introduction to Google Analytics

Author: Todd Kelsey

Publisher: Apress

Published: 2017-06-19

Total Pages: 148

ISBN-13: 1484228294

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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

Author: Robert J. Woz

Publisher: Createspace Independent Publishing Platform

Published: 2017-10

Total Pages: 112

ISBN-13: 9781977843135

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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...

Predictive Analytics For Dummies

Author: Anasse Bari

Publisher: John Wiley & Sons

Published: 2014-03-06

Total Pages: 371

ISBN-13: 1118729412

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Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions. Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more. Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.

Python Machine Learning for Beginners

Author: Leonard Deep

Publisher:

Published: 2019-05-13

Total Pages: 236

ISBN-13: 9781097858309

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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 Science For Dummies

Author: Lillian Pierson

Publisher: John Wiley & Sons

Published: 2021-08-20

Total Pages: 436

ISBN-13: 1119811619

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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.

Introduction to Data Science

Author: Rafael A. Irizarry

Publisher: CRC Press

Published: 2019-11-20

Total Pages: 794

ISBN-13: 1000708039

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Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Machine Learning with Python

Author: Oliver Theobald

Publisher: Packt Publishing Ltd

Published: 2024-03-06

Total Pages: 146

ISBN-13: 1835462073

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Unlock the secrets of data science and machine learning with our comprehensive Python course, designed to take you from basics to complex algorithms effortlessly Key Features Navigate through Python's machine learning libraries effectively Learn exploratory data analysis and data scrubbing techniques Design and evaluate machine learning models with precision Book DescriptionThe course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling. The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts. Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.What you will learn Analyze datasets for insights Scrub data for model readiness Understand key ML algorithms Design and validate models Apply Linear and Logistic Regression Utilize K-Nearest Neighbors and SVMs Who this book is for This course is ideal for aspiring data scientists and professionals looking to integrate machine learning into their workflows. A basic understanding of Python and statistics is beneficial.