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  • Data Analytics: 7 Manuscripts

  • Data Analytics Beginners, Deep Learning Keras, Analyzing Data Power BI, Reinforcement Learning, Artificial Intelligence, Text Analytics, Convolutional Neural Networks
  • By: Anthony Williams
  • Narrated by: William Bahl
  • Length: 16 hrs and 24 mins
  • Unabridged Audiobook
  • 4.5 out of 5 stars (38 ratings)

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Summary

Data Analytics - 7-book bundle!

Book 1: Data Analytics for Beginners

  • Putting data analytics to work
  • The rise of data analytics
  • Big data defined
  • Cluster analysis
  • And, of course, much more!

Book 2: Deep Learning with Keras

  • Deep neural network
  • Neural network elements
  • Keras models
  • Sequential model
  • And, of course, much more!

Book 3: Analyzing Data with Power BI

  • Basics of data analysis processes
  • Fundamental data analysis algorithms
  • Basic of data and text mining, data visualization, and business intelligence
  • Techniques used for analyzing quantitative data
  • And, of course, much more!

Book 4: Reinforcement Learning with Python

  • Types of fundamental machine learning algorithms in comparison to reinforcement learning
  • Essentials of reinforcement learning process
  • Marko decision processes and basic parameters
  • And much, much more....

Book 5: Artificial Intelligence Python

  • Different artificial intelligence approaches and goals
  • How to define AI system
  • Basic AI techniques
  • And much, much more....

Book 6: Text Analytics with Python

  • Text analytics process
  • How to build a corpus and analyze sentiment
  • Named entity extraction with Groningen meaning bank corpus
  • And much, much more....

Book 7: Convolutional Neural Networks in Python

  • Architecture of convolutional neural networks
  • Solving computer vision tasks using convolutional neural networks
  • Python and computer vision
  • And, of course, much more!
©2017 Anthony Williams (P)2017 Anthony Williams

What listeners say about Data Analytics: 7 Manuscripts

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Well sequenced 07 book bundle

It is very helpful for people who are new to Python and also new to Programming.

The Author's command over subject is vast and he explains the topics from basics so any layman can able to understand Python.

The communicating language used by Author is very simple and understandable.

These audios help me overcome my fear over programming and also over Python language.

20 people found this helpful

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Learning Data Science from the Ground Up

One of the most common buzzwords floating around online today is data analysis, and while you may have heard of it, figuring out exactly what it means might be more difficult than you might expect. The reason for this is that there are several different definitions for the phrase depending on who you ask. While it can mean more specific things in context, in general, a definition that you can work with is that it is the process by which data is modeled, transformed, cleaned and inspected by businesses, with the ultimate goal being its use in the decision-making process. As such, this makes a data analyst the person whose job it is to find the best answers to the questions that businesses come up with. They take the lines and lines of data that they find and paint a clear picture of just what it means so that those without the skills to see the pictures in the data still have a firm grasp on what is going on in the market or even with their very own businesses. The data that is analyzed varies radically based on the business that is looking and what it is they are looking for, so much so that it is currently created at a rate of more than 2 quintillion bytes each day worldwide. This dramatic influx in what is available has not gone unnoticed, and businesses everywhere are getting in on the mega trend of collecting data and analyzing it as quickly and effectively as possible. When it comes to finding the truth at the heart of the data, the deeper and more accurate of an insight you can find, the more easily you will be able to discover the hidden trends that are hiding and use them as effectively, and profit from them. It is especially useful as it can be used equally effectively by both automated and human-driven decisions. What it all boils down to, is that traditional business intelligence can often be used to determine what a specific problem is, how often it occurs, where an issue is located and, even, how it can be fixed, but good analytics can determine the source of the problem in the first place as well as what is likely going to happen if the current trends continue.

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Everything comes under the topic

Data visualization or the art of representing data in visual form is crucial to transforming raw data into a sensible data that can be used by the business.

To achieve real learning from data, business executives should know how to use visualization techniques to explore data and communicate the meaning of data to the rest of the business. In this Chapter, we will examine the process of visualization, as well as the people and technological skills needed for the business to make sense of data. Businesses today are capable of capturing data at a fast rate mainly for reporting, compliance, and visualization.

For many businesses, the development of data visualization technology followed a familiar journey: basic charts and tables done manually were replaced by Excel or Numbers, which was then succeeded by conventional business intelligence systems such as databases that can present information easily and as needed. These presentation features started as reports and were soon replaced by interactive platforms. But with the rise of big data conventional business intelligence technologies may fall short if analysis, discovery, and visualization capacities are subtler. The market for data visualization has increased remarkably in the past few years as a way to provide insights into complex and large-scale datasets.

To put it simply, visualization capacities will enable the business to interact easily and understand big data. Data visualization is very effective in business because people are naturally attracted to visual analysis. We are highly-suited for identifying visual patterns and our brains are hard-wired for what we see to process better understanding.

Through data visualization, the business can also integrate large-scale of information in a single place, which allows people to make sense of numbers and text better.

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Connecting dots.

An operational definition of a cluster is that, given a representation of n objects, find K groups based on a measure of similarity, such that objects within the same group are alike but the objects in different groups are not alike. However, the notion of similarity can be interpreted in many ways. Clusters can differ in terms of their shape, size, and density. Clusters are patterns, and there can be many kinds of patterns. Some clusters are the traditional types, such as data points hanging together. However, there are other clusters, such as all points representing the circumference of a circle. There may be concentric circles with points of different circles representing different clusters. The presence of noise in the data makes the detection of the clusters even more difficult. An ideal cluster can be defined as a set of points that is compact and isolated. In reality, a cluster is a subjective entity whose significance and interpretation requires domain knowledge.

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Bible of 7 Manuscipts

If you plan on mining data on a regular basis, then the first thing you will need to do is to ensure that your data warehouse is in order as previously discussed. Additionally, you are going to want to find data analysis tools that are easy enough to use and comprehend that you don’t need someone whose entire purpose is to know how to use them. Finally, you are going to want to ensure that the information that you do generate is going to be compatible with numerous different systems. When it comes to deciding what tools you are going to want to use, you may find it useful to determine how they are going to be used in a conventional decision-making process. The first step of the decision-making process is to develop a style of reporting that is standardized. Next, it will be important to take note of any instances that might be an exception to the rule you have created. These exceptions could be positive and lead to advantages, or they could be negative and give you an insight into potential problems. Once exceptions have been noted you will want to determine important causes before looking into alternatives and determining the overall of what it is that you have decided. Standard reports are any results that you pull using database queries, and they can determine how a business is performing as well as shed light on several other important business factors. When exceptions occur, however, then you want to know that the details will be easy to retrieve when needed.

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Getting at the guts of Data Sciences

Predictive analytics is the use of data, machine learning techniques, and statistical algorithms to determine the likelihood of future results based on historical data. The primary goal of predictive analytics is to help you go beyond just what has happened and provide the best possible assessment of what is likely to occur in future. Predictive models use recognizable results to create a model that can predict values for different type of data or even new data. Modeling of the results is significant because it provides predictions that represent the likelihood of the target variable—such as revenue—based on the estimated significance from a set of input variables. Classification and regression models are the most popular models used in predictive analytics. Predictive analytics can be used in banking systems to detect fraud cases, measure the levels of credit risks, and maximize the cross-sell and up-sell opportunities in an organization. This helps to retain valuable clients to your business.

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Buy it, you'll like it!

Prior to the recent rise in analytics, businesses and organizations did not have the capacity to analyze a great deal of data, so a relatively small amount was maintained. In today’s data-driven world, anything and everything may have significance, so there has been an attempt to record and keep virtually any data that we have the capacity to collect; and we have a great deal of capacity. Beyond the quantity of data that we are gathering and storing is the quality of the data. That is to say, data has grown beyond basic facts and figures to encompass media files. Video, audio, and presentations have all become units of data for possible analysis. A major concern with regards to data analytics is how to store and maintain all of these rapidlyincreasing piles of data. The data science community has begun to rely more heavily upon the software engineering community, in order to find solutions to our overabundance of data. Not all data is necessarily valuable. Society now has advanced data analytics that allows us to glean useful and important information from even the smallest bits of data. Such information, when reconciled with other groups of information, can (and has often) resulted in breakthrough of modern science, business, and economy. As we consider our need to increase the role of data analytics in the ways that we function as organizations, we should keep in mind that data does not contain all of the answers to our growth and advancement. Data provides us with the building material with which we can create new understanding and innovation. The other part of the process is distinctively human. This part includes creativity, risk taking, and cooperation. It appears as though the less we have of one, the more we need of the other. The more intellectual rigor and collaboration between various fields of science the more that we seem to benefit for even limited amounts of data. Conversely, the less of those things that we have, the more data we need in order to learn, grow, and innovate. Perhaps, the solution to our looming problem with big data is to reduce our need for so much of it.

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Impressive!

When it comes to analyzing the data that you are presented with, the first thing you are going to want to do is determine if the data that you are preparing to analyze is going to add real value to your business as a whole or if the costs are not going to be worth the time and effort needed to gather them properly. For example, going through all of your sales data in order to determine the most popular product or service you provide, as well as which is the most profitable, will provide you with clear pillars of your business to focus on in order to ensure you are as successful as possible in both the short and the long term. This activity is a productive use of time because it can help you to accurately predict what the future of your business could look like under certain market conditions. Once those conditions have been properly pinpointed, they can then be used as a direction for the business to move in the short term. By default, the above example also serves another, potentially more important, purpose; it shows you what products or services that you are offering that absolutely no one is interested in taking you up on. As such, you would then be able to more accurately determine if there might not be a better use of your company’s resources than the underperforming products. Either way, a byproduct of the process is a reduction of waste as well as an increase in sales revenue.

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Data Mining made easy

Due to the often fluid nature of the data analytics field, it is perfectly natural for certain myths to crop up surrounding its specifics. The biggest and most outlandish of these is that data analytics are only useful for large corporations or businesses with more data coming in than they can analyze. In reality, however, it is important to understand that analyzing the data that you do have available is an excellent choice regardless of the size of your business or the amount of data you can access. Focusing on the size of your business or the limited nature of the data that you can find means focusing on the wrong issues. Instead, it is important that you look to determine if the data that you do have access to can actually be useful in a real and meaningful way. If you have access to data that you think can be useful then, it is important to seek out ways to utilize it, to your benefit. These myths are often further segmented once concepts like Big Data enter the conversation. While the term ‘big data’ is new, however, the data that it represents has been around for nearly 20 years if not longer. Big Data can essentially be thought of as all of the data that is owned by a company and also what the company does with that data, which can be scrutinized for relative trends. The problem with Big Data, however, is in the name, which means that there is just so much of it to go through that it can be difficult to see the big picture without organizing it.

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Clean Up the Data

When used in reference to data, the term cleaning refers to the action of eliminating data points that are invalid from a given set of data so that the remaining data can be utilized as effectively as possible. Invalid points of data can either be those that are only partially available, are corrupted, or do not factor into the hypothesis that is being used to analyze the data at this point. It is difficult to remove the cleaning step from the realm of human judgment as the various variables that are being utilized to determine if a piece of data is relevant or not are often less black and white than those that a computer program could determine. Furthermore, the points that are subject to cleaning are typically dramatic outliers of the data that you have collected. This means that they do not fit the flow of the other data that you have collected, often by being at one far end of the spectrum of data or the other. Determining which points are outliers is as easy as plotting the data and then looking for the points that are far away from the majority of the spread of data points. Alternatively, you can first run an analysis on the data in question before cutting out those points that are outside of the control limits that were set during the analysis. You can then remove those points and redo the analysis in order to get more accurate results. The importance of having the cleanest, and therefore the most useful data possible cannot be overstated. It is common for analysts, especially those new to the field, to become somewhat lost in the complexity of the data that they are working with and the methods that are being used to analyze them. This, in turn, can easily lead to results that point in a misleading direction, causing hardship and potential financial ruin in the process. A good rule of thumb is that when you are analyzing statistics you are going to want to spend about 80 percent of your time ensuring that the data has been cleaned properly and the remaining 20 percent actually doing analysis.

1 person found this helpful

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  • Roberta
  • 23-06-19

Lays a solid foundation

A very comprehensive introduction on getting started with world of Data. This lays a solid foundation for one to get started with other data aspects such as Data Analytics, Deep Learning with Keras, Analyzing Data with Power BI, Reinforcement Learning with Python and this is overall 7 book bundle.

44 people found this helpful

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  • Darnell
  • 23-06-19

This course was a great into a developing career

The information was clear, concise and easy to understand. I learned a lot of valuable information! My only constructive feedback would be that I would have liked it to be a little bit more engaging as I caught my mind wandering a few times throughout the course. Even a narrative style of the person is superb, which would have helped keep my attention focused

42 people found this helpful

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  • Mandy Palmer
  • 23-06-19

Lot's of valuable information!

It was good learning experience of understand what is analytics and where to put our focus

41 people found this helpful

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  • Marisol
  • 23-06-19

So many options

Great beginner's audiobook to understand how data really works. There are so many options here, to-do's, and what to learn next out there but this audiobook really gives you a concise understanding of many general terms, how to put data analytics to work,big data defined, the rise of data analytics and the main tools they use in their day to day work. The lectures also summarizes the people you'll be working with, the different functions of Python and many more in the data world, and related key functions of how you get your data and why it matters.

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  • Elizabeth
  • 23-06-19

This is a golden ticket for data science.

TAKE THIS COURSE. You'll be happy you did. The instructors really took the time to explain the information in a clear, concise way. I had questions answered that I've been trying to understand myself for a long time and many others I didn't even think about... This is a golden ticket for data science.

38 people found this helpful

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  • Joyce Simmons
  • 23-06-19

Love it!

This course was exactly what I needed in order to get an broad overview of the field and how different roles differ from each other. Like the course promises, I now have a pretty good idea about what my next steps for learning should be based on the role I occupy at the moment.

36 people found this helpful

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  • Sean Johnson
  • 23-06-19

Start to implementing them

This is an completely new filed with zero knowledge on the concept. Now i am looking forward for other courses and start implementing them

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  • Gillian
  • 23-06-19

Newbie here. So far so good!

I was looking more for real world applications, there were a few provided but the instructor had so much experience to share that he made a few suggestions and stopped. I enjoyed the course but not fully satisfied. I think the instructor could actually make a follow up course and take more of a mentor type of roll to give real world life projects from his experiences. ... Just a thought.

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  • Joseph
  • 23-06-19

Good content, good pace, easy to follow.

Good course overall for people who aren't familiar and venturing into Data Analytics and this one have many more to follow.

34 people found this helpful

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  • Amber Beran
  • 23-06-19

This is an excellent start!

Absolutely it is really a perfect match for me. Anyone want to learn Python. Have got started. It is good so far.

34 people found this helpful