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It's a broad high level overview of how data analytics can support businesses increase their productivity and gives guidance on the correct policies and tactics to adopt towards that end.
Data analysis is at least as much art as it is science. This book is focused on the info of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. I concise introduction and instructions about all stages of data analysis. Each subject can be expanded into a much more deep communication but the suggestions mentioned are very practical. I think it's a amazing starting point if you're a new-comer to data analysis. And it would be helpful to frequently look it up when you're doing the process to create sure you're on the right track.
When trying to learn about a fresh field, one of the most common difficulties is to search books (and other materials) that have the right "depth". All too often one ends up with either a friendly but largely useless book that oversimplifies or a massive academic tome that, though authoritative and comprehensive, is condemned to sit gathering dust in one's shelves. "Data Science for Business" gets it just right.What I mean might become clearer if I point out what this book is *not*:- It is *not* a computer science textbook with a focus on theoretical derivations and algorithms.- It is *not* a "cookbook" that provides "step-by-step" guidance with small to no explanation of what one is doing.- It is *not* your standard "management" title on the cool tech du jour available at airport stands and meant to be read in one sitting (buzzwords, hype and overly enthusiastic statements making up for the dearth of actual content).Instead, it is close to being the excellent tutorial for the smart reader who -- regardless of whether s/he has a tech background -- has a sincere desire to learn how the tools and principles of data science can be used to extract meaningful info from large datasets. Highly recommended.
At it's core, Data science is the elimination of guess, intuition,hunch and decisions backed by Data .Data Science is ranked the Sexiest Job Of 21st Century by Harvard Business Review. Today there is a tremendous demand for everything "Data Science", Companies need "Data scientists", IT resources are refocusing themselves to be the "Data scientists". Contrary to famous beliefs that Marketing benefits a lot from data science, companies are finding benefits across the spectrum of their operations . Example : A leading Trucking company used Data mining skill to predict which part of the truck is going to break next instead of replacing it at specific intervals, a Leading insurer predicted those who will complete their antibiotic course based on their home ownership history. If this type of stories and scope interests you, read the book "Big Data: A Revolution That Will Transform How We Live, Work, and Think".I am an aspiring "Data Scientist" and so this review will have a slight tilt from a "Data Scientist" perspective over the business user.WHAT THIS BOOK IS ?This book is very well written ,but not for the faint heart. It is a text book and authors have taken lot of care so general audience can also benefit from it, and also not to dilute it's textbook value. To obtain the full benefit of the book, read about 50 pages ( Do not flip pages), never more than 10 -15 pages per session. The book is intense so you will need to take a break in between or will lose the thread. Once you are finished with fifteen pages, go to the first page and read , highlight the necessary locations and then go to the next page. So plan to read this book in a span of 2 -3 months. I know it is slow but if you wish to understand the inner workings of "Data science", there is not much other option. Alternative is to flip across several superficial articles that is a staple diet of every blog and magazines.WHAT THIS BOOK WILL DELIVER ?When you are finished with the book, you should have a fairly amazing understanding of data science, For example, what type of analysis that needs to be done to identify A. Will the Customer switch loyalty ? ( Yes / No ) B. What type of customers will cancel my subscription ? ( Ex : Middle Aged male from Manhattan will be 5% more likely to switch) C. What are the methodologies to identify If I can up-sell a customer ( Ex : Someone who bought this book also bought ) D. What is a supervised Segmentation and When will you use it ? ( When the target is clear, if the person will default on his loan) E. What is the significance of entropy in Data Science ? F. Exposure to several formula's ( sleep triggers as I call it). A lot of of the tools have in-built formula's but you still need some idea what these formula's are. G. Don't obtain defensive, be comfortable when your colleague sprinkles words like like Classification ,regression, Similarity Matching, Clustering, Modelling, Entropy etc.WHAT ELSE YOU WILL NEED ?Data Science does not exist in silo. It helps in decision making . So should be your learning, Here are my suggestions:1. First and foremost, you need to spend consistent time. If you are running short of time, don't even bother to start2. For those who are interested in understanding Data science, courseera dot org conducts a free 8 weeks course on "Introduction To Data Science" by an eminent Stanford Professor. It needs time and Commitment3. You can obtain true life examples to work on in coursesolve dot org ( ex: Analyze the sleep cycle)4. As a Data Scientist, you will need to understand "Big Data" . Browse an article and even experts use Data Science and Huge Data interchangeably. Hadoop is the core of Huge Data,but it is a globe of it's own.5. Read and begin experimenting with Hadoop , PIG , HIVE, HBASE and the variations it offers. I did a basics training at edureka dot in , an Indian firm, not a amazing training but enough for you to understand and then go on your own. But if time and cash permits, go to cloudera www service and sign up for training. you will not go wrong6. I signed up for Amazon elastic map reduce which has a higher level abstraction (for developers it is the difference between using sqlplus versus TOAD). It is not free but very cheap.7. Test to be the "umbilical cord that looks for a stomach to plug ", look for a mentor, look for opportunity in your firm or elsewhere to grow your Data scientist r those looking for inspiration , google for Rayid Ghani, Chief Data Scientist at Obama 2012 Campaign.
The institution tactic and goals need to be reflected in the procedures used to analyse the data base of the institution and the determination as to what data is relevant. The book discusses ways to get the data required and the short term volatility in return to the company that can result. But the authors present that this can eventually lead to improved efficiency focus and profitability for the company. The book requires a background in a number of supportive academics for full understanding . The discipline has defined its own language much like most of the technolgical disciplines and is best appreciated by those familiar with the vocabulary. It is a book that warrants study not just as a fast read for introduction. For a person studying or practicing in this zone I highly recommend this book for both its interest and as a reference book. Foster Provost and Tom Fawcett have created a valuable contribution to the understanding of Data Science.
Perfect discussion of data science methods without excessive focus on mathematical elements. These are included at a level that can be understood for the skilled marketer who has background but does not want to go deep into the math. The coverage is broad with both supervised and unsupervised methods in data mining. Subjects cover tree models to logistic regression, to scoring. A discussion of holdout model tests, prediction & validation. Particular emphasis is placed on how to frame questions to apply to the business case so suitable conclusions can tutorial business decisions and strategy. You will obtain the sense that the authors are war tested veterans of the data mining business and have applied their creativity to a broad range of business, data and technical y two caveats to this book. First, as purchaser of the kindle edition, I found the equations included in the text were sometimes very readable and sometimes the type was so little as not to be legible at all. Be warned. If you intend to follow the math that is included, perhaps the paper edition would be best. Second, this book does not dwell on the statistical packages that can be used to help data mining efforts. If you are interested in exploring these methods in practice, you will need to look further.
Foster Provost and Tom Fawcett are known for their work on fraud detection, among others. I have recently read their latest book, Data Science for Business – What you need to know about data mining and data-analytic thinking. No suspense: it’s one of the best data mining book I have ever read. Its style allows the book to be read by beginners, but its wide coverage and detailed case studies makes it a reference for experts as the title suggest, the book has a true focus on business with plenty of industry examples and challenges. The style is very pleasant since authors have created efforts to place the reader in specific situations to better understand a problem. To be noted the very interesting discussion of data mining leaks as well as data mining automation. The book is divided by concepts and provides a focus on them (instead of techniques). Although no exercice is present, the book could easily be used as a resource for a course.Each chapter is clearly divided into primary and advanced topics. The evaluation phase of the data mining standard process is deeply discussed. The section about Bayes rule is very well written. Data Science for Business is also an perfect resource to avoid data mining pitfalls. Chapter 13 is a must-read in order to understand success factor for implementing data mining in a company. To conclude, targeted at both beginners and experts, Data Science for Business is the fresh reference for data mining specialists working in industry.
This book is fantastic. it's a excellent mix of high-level explanation and technical details. There doesn't seem to be much to support one actually execute the methods described, but that does not appear to be the author's intent (which is why there is no negative impact on my rating).I appreciated the accessibility and plain English - albeit thorough - writing (from the perspective of a person who is self-taught in data science and sometimes less acquainted with the terminology).
An perfect resource for the Business Analyst (or the curious executive) looking for a comprehensive understanding of data analytics for business, especially the newer zone of heavy data/big data for business. This book gives a really solid grounding in both the business (strategic) and data (analytic, technical) aspects of modern data analytics. The authors clearly present that data is the next wave of change and that it will require a mindset change across all business functions--a mindset they call data-analytic thinking. If you need to master/improve this thinking skill set--here is a amazing put to begin no matter what your job vost and Fawcett have place a lot of work into the instructional design of this book--you can follow it down to the technical/mathematical level of algorithm design or just read the content concerning business tactic and general data design and use. Either way, you will achieve a satisfactory understanding that serves your purpose--the authors maintain a conceptual continuity at two or three levels of discourse. Very nicely done and very engaging. Five stars.
Note - I was provided an ebook ver in exchange for my review as part of the Library Thing Early Reviewers brief –This is a amazing book for any in the data science field or wanting to just understand “Big Data” or a manager/professional just trying to “get current. “ I have a masters degree in software engineering with a data science background and three years experience in a prior job in Data warehousing. It was a long read, especially with the holidays, but well worth it, and more enjoyable than almost every technical book I have every rengths – Organization, having technical info in a side by side section for those who wish it, covering info from definition, through use and application, as well as doing a amazing job explaining similarities and differences on key topics.Weaknesses – there are a few little locations I wanted more. Meaning if they could have somehow had more examples for the various models, situations, etc., especially as I got into more of the predictive models.
Provost and Fawcett's book is one of the very few in the field that neither condescends nor patronizes the reader as it explores the motivations and machinery behind the most commonly used data analysis techniques in the analytics professional's toolbox. While it stops short of providing detailed instruction on how to use these techniques, it provides the reader a solid foundation for taking this next hands-on step. And for those who are not working directly with data, but are otherwise stakeholders in the use of analytics to drive better organizational outcomes, this book will greatly enable you to understand and add value to the analytical process.---Zain KhandwalaExecutive Director,Institute for Advanced AnalyticsBellarmine UniversityLouisville, KY
Both authors practicing data science professionals. Their book outlines practical considerations, explains available tools and techniques, and shows results of a lot of well-chosen e book is appropriate for all data scientists, regardless of background or education. The math is minimal. There are no computer programs or algorithms.
In Silicon Valley, "ability to code" is now the uber-metric to track. Starting from how engineers are interviewed, actual hands-on work (due to processes that overemphasizes "do" over "think, e.g., everyday stand-ups require you to say what concrete thing you did yesterday), evaluation of work ("move quick and break things") to over-emphasizing on downstream "fixes" (prod-ops culture, 24*7 firefighting heroism) - the top echelon of technology gravitated towards things that it can see, feel, measure. What often gets neglected in this "code be all" culture is deep understanding of fundamental concepts, and how most newer "innovations" are indeed built on a handful time-honored here else perhaps is this more prominent than in data zone that up-levels libraries and frameworks as the conversation starter. That gets in the method of success. It is indeed impossible to model Cassandra "tables" without understanding - at least - quorum, compaction, log-merge data structure. Due to the method the show day solutions are built ("fits one use case perfectly well"), if these solutions are not implemented well to the particular domain, failure is just a release Kleppmann does a amazing job of articulating the "systems" aspects of data engineering. He starts from a functional 4 lines code to build a database to the method how one can interpret and implement concurrency, serializability, isolation and linearizability (the latter for distributed systems). His book also has over 800 pointers to state of the art research as well as some of the computer science's classic papers. The book slows down its pace on the chapter on Distributed System and on the final one. A amazing editor could have trimmed about 120 pages and still retain most value one could obtain from the at said, if you ever worked on data systems, especially across paradigms (IMS -> RDBMS -> NoSQL -> Map-Reduce -> Spark -> Streaming -> Polyglot), this book is beautiful much only resource out there to tie the "loose ends" and paint a coherent narrative. Highly recommended!
Really informative, and not too technical. very amazing to have a general knowledge of all the data technologies that are out there, in order to create better decisions. once a decision is created you'll need to learn the technology chosen for an in depth understanding.
This book is excellent. You will build up a knowledge from the basics to most advanced techniques. Even only the references after each chapter are worth spending cash on the book. Highly recommended.
I've been looking forward to this book since I pre-ordered it latest year. Martin is a thought provoking author, and I pre-ordered this book based on some of his blog posts. I've been working on this field on and off for the past few years. Searching for anything online that provides a comprehensive overview of what the hell has happened in the latest 10 years in Distributed Storage and Systems is hard. This book has tied huge chunks of the bodies of work that stand as pillars for these technologies today. I love that he has structured the content to zoom out from a single host (data structures such as LSM trees, system IO considerations) etc. all the method out to distributed co-ordination (Paxos) and hashing (Virtual nodes). This is the right approach. I've skimmed the chapters, and they include most material you will likely encounter when working with any modern distributed database. A must-have reference for a professional (or a student) interested and/or working in this field.
Awesome book. Read this book cover to cover. He dives deep into topics like Btrees, LSM trees, SSTables, and concepts that would normally seem foreign, but because of the author's understanding he breaks it down into tangible bits. NoSQL, Relational databases have been around for some time. By reading this book, you obtain a clear understanding of true globe huge data architecture and the drawbacks of things like sharding, replication, lag as well as solutions. You really see how key-value data stores are used in the true world. And also the author poses the question: Is NoSQL type data stores a fad, since they've been out since the 70s? Is this just a fresh wave of key-value data stores and will SQL data stores eventually dominate?Configurations like master-to-master, master-slave are all explained and the problems that will arise. All in all amazing read and I think every serious developer should read this. Very theoretical - but the theory is what makes the magic happen.
DDIA is easily one of the best tech books of 2017 (possibly this decade) and is destined to become a classic. The book deals with all the items that happens around data : storage, models, structures, access patterns, encoding, replication, partitioning, distributed systems, batch & stream processing and the future of data systems (don't expect ML because it is a various beast).Kleppman has coherently blended the relevant computer science theory with modern use cases and e focus is primarily on the core principles and thought-processes that one must apply when it comes to building data services. Design concepts don't go out-of-date soon, so the book has very long e high-point of this book is the author's lucid prose, which indicates mastery of the topic matter and clarity of thought. Conceptualizing reality is an art and the author really shines here. Kudos for those understandable diagrams and interesting maps (and also for avoiding mathematical formulas with Greek symbols). The bibliography at the end of each chapter is thorough enough for unending private research.If you are working as or interviewing for huge data engineering, system design, cloud consulting or devops/site reliability engineering, then this book is a keeper for a long-long time.
I'm only 3 chapters into this book and I think it deserves a 5 star already.If you are interested in distributed systems or scalability, this book is a must-read for you. It gives you a high level understanding of various technology, including the idea behind it, the pros and cons, and the issue it is trying to solve. A amazing book for practitioners who wish to learn all the essential concepts quickly.I didn't come from a traditional CS background, but I did have some primary knowledge in hardware and data structure. You will need some of that, such as hard disk versus SSD and AVL tree, to understand the materials. If you are completely fresh to backend or DS, you may wish to begin with another book "Web Scalability for Startup Engineers." After that book, you can read the free article "Distributed Systems for Fun and Profit" and you are amazing to go for this awesome book :D
Love this -- I want I had this book when I started working as a software engineer in a web-services squad a few years ago. It would also be of amazing support to a senior software engineer transferring from another domain to a distributed systems project. Due to its extensive breadth, it will equip you with the vocabulary you need to reason about your design and will create you aware of a lot of design tradeoffs and pitfalls and known solutions to common e breadth of the book and focus on established technologies is also its weakness -- if you already are working in the zone for at least a few months, and have a narrow scope (eg. MapReduce or something similar), it won't be as useful because chances are that you already know the relevant parts and don't really need the irrelevant ones. At the same time, it would give a amazing push to your systems design skills should you wish to transfer to a various project in this field.
I came across this book while searching for a textbook for my introductory course to DB. This book is of an extreme value. It is a comprehensive reference for traditional relational data modeling and SQL and also includes updated advanced material on data mining, natural language processing, visualization and huge data.What I also like about the book is that it blends theory and practice of data modeling and SQL. Each chapter in the first part of the book starts with a data modeling concept (i.e. single entity, one-many, many-many etc...) and then shows how to implement it and perform queries with e companion www service contains all slides, datasets, and partial solutions of the exercises. All of that for $10 with Kindle, I can't ask for more. This is a must for database students and practitioners as well.
This is the worst textbook I've ever had to use. It's unnecessarily wordy, full of grammatical and formatting errors (in one part of the book, the same paragraph was printed twice in a row; in another, the book said that there were three reasons for something and then proceeded to list four - and there are related errors splattered throughout the entire book), and extremely hard to navigate. I have better luck jumping around randomly in the book in find of a section than I do trying to actually use the find function to search it. The author jumps around from topic to topic in a method that makes it hard to understand; it's almost like he place a bunch of SQL concepts in a hat and randomly drew a couple for each chapter he wrote. Furthermore, there is some weird story about a lady named Alice mixed in with the chapters in what I perceive to be an attempt to create the book slightly more interesting. I assume that my professor is private mates with the author, because that's the only reason I could think of for any professor to choose this book as a class textbook. The reason I gave it two stars instead of one is because the book has been useful in the context of my Database Management class, and it has been genuinely entertaining to read a textbook so poorly written. However, unless you are absolutely needed to have this textbook for your class, I'd recommend versus buying it. I'm sure there are much better-written SQL books out there that will teach you a lot more.
Although the info contained is valid and helpful, I can search no logical method to locate that information. The Kindle ver is very difficult to navigate, (location values instead of page numbers). I had a classmate with the printed ver ask me for support in locating a specific exercise in a specific chapter. I couldn't do it because her ver (printed) had page numbers for reference, and my ver (Kindle) had zone values. I have been hesitant to purchase a Kindle product and after seeing how this text is presented in the Kindle application for Windows, I'll pass. The reason I purchased the Kindle ver is because it is "Required" under my educational program. In summary, the info is valid, but the presentation sucks.
I got this book for my college class as needed by the school I'm attending. It is a amazing book with valuable info inside. But the fact that it was a kindle book created it very limited in terms of how I could learn the material. It is written like a novel. A book like this needs more interactive features on a platform like Pearson. I think it would have been much better if we bought a hard copy instead.
I use this book, and have done so for years, in an MSc course for students from all sciences that wish to acquire literacy on database design and SQL. The students like it and it allows them to work quite independently, having both a lesurely pace and enough depth. The author keeps it up to date, 's not at its strongest as a reference; but since SQL versions differ and change, that might be too much to ask.
It's definitely passable, and some of the homework questions were really fun to think about and respond (they were written in such a lighthearted tone), but overall the book organized info in some counter-intuitive ways and was more verbose than it required to be.
I appreciate the info and the context in which it is written. This will support me to be a better analyst.
This is an informative book on data analytics! This is a concise introduction and instructions about all stages of data analysis. Each subject can be expanded into a much more deep communication but the suggestions mentioned are very practical. The directions are simple to follow, and for me this is one of the best books on this subject. I definitely recommend this useful guide.
Latest 4 days ago I got this book and I'm really impressed with the amount of hints that this tutorial book has. More time I am frustrated about my future for that my mate suggests me the book. In this book the info is organized in a logical method that’s simple to access, read and understand. It is indeed a amazing read and I highly recommend this book to everyone.
Best book to read and learn a lot..This book given me a decent outline of the abilities and capacities needed by an info researchers. its a magnificent is is extremely valuable and useful book for novices. I recommend this book because i like this book and i hope this book will support everyone who read this.
Its vital for me to have some info on the data analytics, even when I'm running my little business. The book may be for the Analytics professionals but it has something for the business people too, and this will change your decision making in your business.
I chose to give it five stars because it is a amazing primer for folks who are not familiar with data analytics at all. It is a fast read and written concisely.
However, I was expecting some really amazing contents in the latest few chapters because they were supposed to be the most necessary part of this book. Only a few pages each chap. Makes me wonder why?
I'm loving the mobile application and the web application for having amazing features I've been looking for in a freelancing tool. Their invoicing is better than what I've tried before. I also love the fact that you can make quotes and contracts lighning-fast ⚡They also fix and release fresh features very fast. Highly recommended if you are a freelancer or one-man creative.
I'm so inlove with this app. I'm just currently using the free ver as I am waiting for them to accept Paypal for me to make batter to the gold plan. Very simple to use, super user friendly interface, and very reliable. There help is next to one. I love it when they send me a notification that my contract and invoice has been viewed. Okay, just wish to say that they are AWESOME!
The idea of this is amazing but I've been unable to make a project or client. The application keeps crashing for me. I will test the desktop version. UPDATE: After the latest modernize the application is working and I was able to make a project. I'll test to make my first contract and invoice and see how it goes but I'm much more hopeful now.
It is promising because it has the features I was looking for, however its impossible to accomplish everyday time tracking via their wizard based interface. Just adding a fresh project to track an hours worth of time is incredibly difficult and consists of a lot of steps which have nothing to do with tracking time.
AND CO has save me and my business so much time and cash and the application is so simple and user friendly. I can easily make contracts which have all the T&Cs for Freelancers and once signed by my client my projects are auto created. I simply can begin invoicing and time tracking versus my projects. You can track all of your income and expenses aswell as link your bank acc and set up your very own private PayMe link so your clients can pay you online. I can manage all my contracts, projects and tasks in the app!! Simply Amazing!! Super excited for my business!!!