Data Analytics: 5 Books in 1

Bible of 5 Manuscripts - Beginner's Guide + Tips and Tricks + Effective Strategies + Best Practices to Learn Data Analytics Efficiently + Advanced Strategies
Narrated by: William Bahl
Length: 6 hrs and 59 mins
5 out of 5 stars (61 ratings)

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Publisher's Summary

It doesn’t matter if your business has three employees or 300, you are likely generating far more information that you may realize and certainly far more than you are likely tracking effectively. Understanding what this data truly means starts with managing it successfully, which is where the process of data analytics comes into play. If you like the sound of putting your data to good use but aren’t quite sure what the ins and outs of data analytics entail, then Data Analytics: Bible of 5 Manuscripts - Beginner's Guide + Tips and Tricks + Effective Strategies + Best Practices to Learn Data Analytics Efficiently + Advanced Strategies will be your perfect learning guide.

On average, there are roughly two quintillion bytes worth of new data created each and every day, which means that knowing what to do with it is easily a full-time job. Luckily, there are a wide variety of options out there when it comes to focusing in on the data that you want to use and using it in the most effective way possible. Inside, you will find all the tools you are going to need in order to do just that, regardless of if you are part of multinational conglomerate or are running your own start-up. 

Having the right data means being able to make the right decisions about your future because you know what your customers want, often before they do. Making the right decision in the moment means understanding the potential that this bundle of five audiobooks is offering and making the choice to go ahead and click "Buy Now"! Your future, more successful business will thank you. 

So, what are you waiting for? Grab this powerful pack of audiobooks that teaches you everything about data analytics.

©2018 Daniel Jones (P)2018 K.M. Kassi

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Beyond the big data hype

Don't worry. Big Data doesn't have a certain definition. The best definition I've seen came from an analytical genius, Josh Dreller. He says big data is anything that won't fit into an Excel spreadsheet. For others, it brings up images of a huge server farm where machines are just humming away.

You can find data everywhere. The quantity of digital data that is out there is growing at an alarming rate. There are more than 2.7 zettabytes of data in existence today. By 2025, this is expected to be at 180 zettabytes.

This data including the photos you take with your phone, to the financial stats of the Fortune 500, has just begun to be looked at to figure out the insights that will help businesses improve their organizations. This is why businesses and organizations are looking for professionals that can understand all this data.

Big Data is a lot broader and deeper than any of that. There are several areas that Big Data can be used to help businesses.

25 people found this helpful

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Dipping your toe into the pool of data science!

Data analytics is much more than analyzing data. Especially on advanced projects, most of the required work will be done upfront, in preparing, integrating, and collecting data and again when revising, testing and developing models to make sure that the results they produce are accurate. In addition to data analysts and data scientists, analytic teams will include data engineers. Their job is helping get sets of data ready to be analyzed.

The process begins with collecting the data, then data scientists find what information is needed for a certain application, and then they work by themselves or with data engineers and IT workers to get it ready to be used. Data from many sources might need to be combined through data integration routines like a data warehouse, NoSQL database, or Hadoop cluster. There are other cases where the collection process might need to pull a subset out of the stream of data that goes into Hadoop and putting it into a separate section within the system, so it gets analyzed without hurting the complete data set.

When the needed data is put into place, they will next fix and find quality problems that might affect how accurate the analytic application is. This includes running data cleansing, and data profiling jobs that ensures all the information within the data set is constant, and duplicate entries and errors are gotten rid of. More data work is done to organize and maneuver the data for whatever use has been planned. Data governance policies are added to make sure the data is used in the right way and stays within the corporate standards.

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Data Mining/Business Analytics/Data Science

Big Data is being used to optimize businesses. Retailers can optimize their stock by using predictions that were generated from social media data, weather forecasts, and web search trends.

Warehouse stores like Costco and Sam’s Club have begun using a process that is using Big Data analytics to optimize delivery routes and supply chain stores. Radio frequency identification sensors and geographic positioning get used to tracking vehicles or goods to optimize routes by giving them live traffic data. HR processes are being improved by using Big Data.

Big Data tools can be used to measure staff engagement. Sociometric Solutions put sensors in their employee's name tags that would show social dynamics in the workplace. These sensors reported the employee's movements around the workplace, their tone of voice when communicating, and who they spoke to.

Bank of America, one of Sociometric's clients, saw their top employees in the call centers would take breaks together. Bank of America then instituted a policy to take breaks in a group. Their performance improved by 23 percent.

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Like you never thought possible

It is important to keep in mind that data analytics in general, not to mention the bleeding edge deep learning technologies, are an ever-evolving field of study which means that the only way to ensure that you ever grasp everything that it has to teach is if you commit yourself to becoming a lifelong learner. Anything else would ultimately result in a less than optimal use of the skills you have learned, which will lead to inferior data and poorer choices in the long run. The next step is going to be to stop reading already and to get ready to get started using data analytics in the way that is going to benefit you the most in both the short and the long-term. While the steps outlined in the proceeding chapters likely seem complicated now, in time they will become much easier to wrap your head around. Luckily, utilizing data analytics is a skill which means that it will improve every time you use it. This doesn’t mean that these skills will materialize overnight, however, using them effectively is going to be a marathon, not a sprint, which means that slow and steady wins the race.

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For better use!

When it comes to putting data analytics into play in order to complete a task, the first thing you will need to consider is if the process that you are about to utilize is actually going to be worth it in the long run. Data analytics costs are often quite high, especially if you are going either very wide or extremely granular, and it is possible that the costs are going to end up outweighing the benefits. For example, if you plan to go through all of the company’s old sales data in order to find out the most profitable product that you sold last year in order to more effectively realign your business moving forward. This is likely going to be a worthwhile use of your time, assuming you end up with results that you can build on moving forward, even if you find nothing useful, that is still worthwhile information to have as it shows you would need to pivot into products that have a more dramatic following. On the contrary, however, if you spend the same amount of time to determine the number of sales you made on days with either 1 or 0 in the date, you likely could have put that money to better use elsewhere. In the first instance, assuming you found actionable information, you would then be able to look at the market conditions around your best sellers and strive to recreate them in as many different ways as possible. With this clearly outlined you will then be able to shift the direction that your business is going to move in for the better in both the long and the short-term. The first example provides you with greater insight by making it clear what products your customers are interested in, but also those that they are not interested in in the least. This will then provide you with several different alternatives as to how you can more effectively utilize company resources. With the right groundwork laid, regardless of the outcome, you will be virtually assured to cut down on waste in at least one area while also being able to more easily focus on the parts of the business that are going to do the most to increase sales revenue.

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A gateway into the world of data science

Analytics in the modern sense of the word were first refined by a man by the name of Frederick Taylor, an engineer, who created a number of different exercises to encourage time management. He was very interested in the concept of industrial efficiency along with the potential benefits it could bring to businesses of all types. He started off by determining how his team at Midworld Steel Works could work as efficiently as possible without taking any undue safety risks at the same time. The insights he gleaned helped to create the field of scientific management and his most pertinent observations are still in use today. Perhaps most notably used directly by Henry Ford when it came to tuning the speed of his assembly lines, the real potential for analytics was not unlocked until computers became an everyday part of the workplace as a whole. Eventually, however, the computers became powerful enough to understand what truly large amounts of data were really saying, and were able to determine a wide variety of likely outcomes from the results. Thanks to computer science, data analytics has been able to rapidly become a part of several different common applications and regularly shows up in everything from data warehouse to the resource planning systems utilized by major enterprises.

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Excellent Book

The language of this book was a very simple, interesting and eye opening. It covers very broad subjects starting from how data is related to businesses to the upcoming next generation of data science.

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Summary Of Data Science

Understanding what all this data is trying to tell you is the key to getting ahead in the marketplace and understanding the ways big data can help you do just that is key to finding success no matter how fierce the competition. The following chapters will discuss everything you need to know in order to get started preparing yourself for the process of data analytics, starting with explain just what it is all about. From there you will learn all about the many benefits of predictive analytics, and how it can be put to use to help you plan out future business plans with relative certainty. Next you will learn how to set up a discrete choice model and the ways it will help you determine the products you are selling are always going to be a hit. Then you will learn all about the ways in which machine learning is changing the world of big data forever with deep learning and neural networks. There are plenty of books on this subject on the market, thanks again for choosing this one! Every effort was made to ensure it is full of as much useful information as possible.

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Informative Book.

This book is a great introduction to how organizations use data about you, often provided by you, to determine your behavior. It talks about the many different areas that predictive analytics are used in from advertising to health care.

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Nice Book in the field of Big Data Science.

This book is a very sensible and easy approach for anyone interested in the field of data analytics. I covers the broad areas that Data analytics such as tools that can be used and the overall application of data analytics.

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  • Mary Stirling
  • 06-23-20

The perfect balance

Most jobs out there will use some form of data analytics to help them make better decisions to improve their business. If you understand how to analyze data before you get hired, then you will end up being one step ahead of everybody else.

You don’t have to worry about a bunch of algorithms through this book, nor is this meant to replace a book on algorithms. This book is here to help you with the fundamental principles or concepts of data analytics. You will also learn how you can build up a foundation for the more difficult algorithms. Accordingly, this book is organized around general principles instead of certain algorithms.

In this audiobook, you will find a collection of the important fundamentals of data analytics. The concepts start with the very basics of what data analytics is to applying data analytics techniques to help you improve your decision-making in business matter like what products to place on sale and when.

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  • Andrew Miura
  • 06-25-20

Access codes for courses

Big Data is used a lot in High-Frequency Trading. Algorithms are used in making decisions about trading. Data algorithms are being used in the trading business by utilizing news websites, and social media networks to sell, buy, and make decisions in just a matter of seconds.

Complex algorithms are programmed into computers that scan the stock market for certain conditions and search for opportunities to trade. These programs are designed to work with or without humans. It all depends on the needs of their client.

The more sophisticated of these are now designed to change as the market changes instead of being hardcoded.

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  • Gary Barbara
  • 06-27-20

Thank ya

The purpose of an artificial neural network is to learn to recognize patterns. The machine then uses the patterns that it has learned to make classifications of different things. For example, if an artificial neural network has been trained to recognize what a car is, based on hundreds or even thousands of pictures of cars, it will be able to determine what is a car and what is not. Given three pictures of a car and one picture of a truck, it will be able to determine which images are of a car and which one is not.

Once an artificial neural network has learned to detect patterns, it can use those patterns to make predictions, which detect similar patterns in future data. For example, a deep-learning program may have learned rates at which mosquitoes have plagued a particular urban area for the past 50 years, as well as the prevalence of mosquito-related diseases that resulted. It can then use that data to determine particular patterns in the mosquito population and even predict how bad the mosquitoes will be in upcoming years. It can also make predictions about how mosquito-related diseases will affect the human population of that particular area. These predictions can then be used to take safety precautions to protect the general population from being harmed and minimize the effect of the mosquito infestation.

Once the neural network has then reached a level where it clearly understands all of the parameters it has to work with, it can then start adding in new layers of data as well. This can be done by putting the network into what is called propagation mode. This mode will allow for new parameters to be added to the network and to let the network know that it is supposed to think about these new things through the filter of everything it has already learned about a specific topic. It is crucial that this is done before adding new parameters otherwise the old ones will simply be erased, and everything will have to start over again from the beginning.

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  • Leslie Hummer
  • 06-27-20

Analytics and Applications

If you aren’t careful, you could easily find yourself taking on too much data to start in an effort to come up with one specific formula that is right for absolutely every situation. This is going to be an impossible task, however, as there is always going to be a certain level of variance that needs to be taken into account when using big data and using too much is going spread the variance wider than you can realistically compensate for. Don’t forget, big data can determine with a relative degree of accuracy the reason that a specific event occurred previously and also determine the odds that a specific outcome is going to happen in the future. It cannot, however, determine how things are right now in the present because there are simply always going to be too many variables to account for. Also, don’t make the mistake of thinking big data can predict the future, all it can do is provide likely odds for various scenarios. Likewise, when you are first starting out it will likely be tempting to think of big data as little more than its title. Big and uniform and best in a one-size-fits-all scenario. The truth of the matter is that big data is actually going to come in a wide variety of different sizes and shapes in addition to being broken down into three distinct groups. You will find that each of the different classifications relates neatly to a specific sector of business which means choosing the right ones is going to be crucial to your long-term success.

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  • Cynthia Johnson
  • 06-28-20

More than perfect!

In order to ensure that the data you are looking at is going to be the data that is the most useful at the moment, the first thing you will want to consider is the type of data that is going to be the most relevant when it comes to unstructured and structured data. The structured data category includes things like standard databases and unstructured data is considered things like data pulled from things like social media applications and data that is sent through applications that are connected to the internet. As this alone, amounts to a practically unimaginable amount of data, you will most likely find it extremely helpful to use automatic collection methods as a means of keeping your output to manageable levels. This will make it much easier to manage at a future point when it is time to compare what is now historical data to newer data that comes straight from the source. In order to ensure you are doing so as effectively as possible, you are going to want to decide on the most effective database structure as possible in order to make getting back to the data when you need it in the future as easy as possible. Making the right choice when it comes to the database options that your implement could be crucial to the long-term success of your business which is why you will see it come up several times throughout the next few chapters. However, if you are more curious in finding out what the current level of public sentiment is like then social media options are likely going to be your best bet instead. The best currently accepted ways to go about doing so are also going to be covered in detail later on, though new and improved options are coming along all the time.

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  • Ronal
  • 06-29-20

Let Me Guess ...

What is big data? A simple definition of Big Data is: “Big Data is high-variety, high-velocity, and high-volume information that demands innovative, cost-effective ways to process information that allow for enhanced insight, process automation, and decision making.” Big Data analytics will find wisdom that can help businesses make better decisions. It can be used to explain huge amounts of unstructured and structured data. Big Data can overwhelm organizations no matter their size daily. Big Data is any amount of data that can’t be processed by normal applications. Big Data processing starts with raw data that hasn’t been organized or aggregated. It is usually impossible to store this memory on one computer. It can be used to explain huge amounts of unstructured and structured data. Big Data can overwhelm organizations no matter their size daily. Big Data is any amount of data that can’t be processed by normal applications. Big Data processing starts with raw data that hasn’t been organized or aggregated. It is usually impossible to store this memory on one computer.

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  • Efren Bright
  • 07-02-20

How Data Science Applies to Our Emerging Big Data

Big data can be unstructured and come in video, image or audio forms primarily, though poorly structured text can be considered unstructured as well. One the other end of the spectrum is structured data, which also includes actuarial modes, sensor data, financial models, mathematical models, risk models and machine data. In between the two you will find what is known as semi-structured data which includes things like software modules, spreadsheets, earnings reports and email. Furthermore, as big data is currently in the public mindset, it can be easy to get focused on gathering everything you can find without thinking about its individual quality. This will soon prove to be a mistake, however, as using subpar data is only going to skew the data in unpredictable ways. As such, you are going to not only want to consider the process you use when it comes to gathering big data effectively, while also ensuring that you have a way of ensuring that the incoming data is as relevant as possible. It is extremely important to also keep in mind the ways you can improve the quality of the data you procure whenever possible. When it comes to dealing with unstructured data effectively, it is crucial that you keep in mind the fact that if you are trying to increase the general quality of your data then the first thing you are going to want to do is to begin by improving the libraries that are going to be used for correcting language. If translation is going to be a key part of the collection process then you are going to need to ensure that not only is there a human in charge of the process, but that they speak the language in question to ensure they catch the true nuance of what is being said. When it comes to semi-structured data, that includes either numeric values or simple text, it is crucial that you allot the time required in order to ensure it runs through the same correction process that you put all of your other text files through. Additionally, you are going to need to be prepared for additional user input as a means of ensuring that the data that is ultimately generated is going to be as useful as possible.

6 people found this helpful

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  • Trumble
  • 07-04-20

Torture the data enough and it will confess to any

This course was very short and sweet. I must admit, before I joined the course I was not sure about the course quality and its worth because very few people had taken this course compared to other courses under the data science category. But I was completely surprised. I found the course very interesting, informative and fun. The content quality was amazing and worth every penny I paid. It has definitely piqued my interest to go deeper in this field. It is really sad that this course has not yet been discovered by many people. I would definitely recommend this course to anyone with some existing data science knowledge and wants to get an insight on a complex, yet highly important process, i.e Data Mining.
This is an excellent start!

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  • Bonnie Sutton
  • 07-04-20

With Lots of Added Value

The biggest benefit to big data is that, if it is used correctly it can lead to a wide variety of serious advantages based on the results that will be created from the software you choose when dissecting it. This will, in turn, make it easier to get a better understanding of the asymmetrical elements that relate to either your marketing or production plans.

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  • Jean
  • 07-04-20

The critical space between data science and busine

Today, virtually every type of business has some type of data that is crucial to the understanding of the way their operations run and is used to ensure they keep doing so as efficiently as possible. Knowing what this data is, along with the ways to utilize it effectively, are two dramatically different things, however, which is where data analytics comes in. While rarely discussed, proper data analytic technique is one of the most important facets of the long-term success of a business.