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Python for MBAs

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Member Reviews

Erratum: "We're return to this in a second" (We'll return...)

This was an impressive introduction to Python and quite the polar opposite of the confusing and unhelpful volume on Python which I reviewed earlier this month. This one did the job right and led into the topic with clear and simple terminology and examples. It explained everything as it went and offered lots of support and practice without overdoing it.

It's split into two sections. The first, by Griffel, in an admirable introduction that gets you up and running with Python. The second, by Guetta, is a workmanlike tour that takes you into real-world-inspired examples where you work business data sets and learn how to set them up, manipulate them, and employ them to extract the information you need. I was impressed by this book - by how simple and clear it was and by how well things are explained.

The only issues I ran into were those of a formatting nature which I seem to encounter quite often in Kindle-format ebooks. I did not encounter these with the PDF format version which I also checked out - it was just in Kindle. This is one reason I detest Amazon, because if your text isn't plain vanilla, their conversion process will turn it into kindling - hence the name of their format, no doubt!

The weird effects I saw in this version I have seen in other books too. It seemed like this effect, whatever it was, affected instances where the letter 'F' was combined with another letter - another 'F', or with an 'I', an 'L', or a 'T', or where a capital 'T' appeared with a lower-case 'H'. In the 'F' cases, that letter and its partner letter were omitted; sometimes a space appeared in their stead, other times the word just contracted like there were no missing letters. For example, 'overflow' would turn into 'overow', 'different' would become 'dierent', 'often' for some reason maintained a space and would read as 'o en', or sometimes 'oen'. 'Fifty-Five' would read as 'ty-ve' LOL!

Here are some examples:
"A survey by Stack Overow found that almost percent of programmers are self-taught..." (that's Stack Overflow)
"...even professional programmers constantly come across new topics and concepts that don't know but have to gure out how to learn." (That's 'they don't know' and 'figure out how')
"Yet the average uent adult knows only twenty to thirty-ve thousand words." (That's fluent, and thirty-five)
"...some of the Python data types (e.g., oats, integers, and strings)..." - yes, it was quite amusing reading about how much oats plays into the Python language. Of course, it's floats!
"A lot of what you're learning when you rst learn a programming language..." (first)
"...(think Microso Word..." (Microsoft - but insult them all you want, I'm not a fan!) "Note that the so ware we use..." (software) "...end. e front end..." (The front end)

Another problem is that sometimes numbers, written as numbers, were missing, so I'd read, "According to the Global Language Monitor, the English language currently has , ,. words. (Ever stop to think about what a . word is?" All the figures are missing, but the letters remain!

That aside, I consider this a worthy read and a great start for anyone wanting to get into python programming whether your ultimate aim is business use or not.
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The author went into great detail about the python programming. You could be an absolute beginner and will still feel comfortable learning this language from the book.
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Python is a programming language I know a little about and was able to hold my own when it came up in my Cybersecurity courses but is one that is on top of my list to learn some more about.  While I am not an MBA student nor do I anticipate being one, I figured this book was as good as any to teach me a bit more about Python.  And teach me it did - I did not realize that it was named after Monty Python and not the snake! (A snake is its icon on computers.)  The first part of the book was a lot of the basic commands, most of this I knew, but it never hurts to give yourself a refresher to make sure that your syntax is correct so you're not constantly editing what you're writing after it errors out.   I found it amusing it also mentioned stackoverflow - a website I am on a lot and that my software engineer coworkers recommend for coding help in pretty much all languages!  Each of the parts of this book are broken down into easy to digest sections and even though I was familiar with the beginning - parts in the end that were all new to me never seemed to overwhelming to tackle.  The second part o the book worked more with data and Jupyter Notebook which I was not really familiar with.  Again, I am not an MBA student so none of the code in this book would 100% apply to me - but the functions and techniques used could certainly be applied to other things to use python for as well.  I thought this book could be stuffy, overwhelming and boring - but it was nothing of the sort! Definitely recommended if you are an MBA student or if you're like me and just trying to learn everything you can about Python.

I received a free e-copy of this book from NetGalley in order to write this review, I was not otherwise compensated.
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This is a great book.  Very good introduction to Python.  The only thing I would say is that the first exercise given is  difficult for a first time programmer and could be offputting.

Other than that - excellent.
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Python is probably the most versatile of the modern programming languages. In my research institutions I see it used from engineers working on supercomputers to archaeologists cataloguing ancient artifacts. Therefore, I think it should be must too for economists/entrepreneurs to have the basics of this programming  language to manage numbers and simulations in a much more powerful way that Excel for instance may provide. This book then covers a perceived gap. The volume is easy to follow. It requires however some familiarity with how to use computers.
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Python for MBAs is aimed at business people who have never coded before, on the grounds that learning to code enables “all kinds of useful things, like automating repetitive tasks to save yourself time and money and performing data analyses to answer important business questions on files far too large and complex to be handled in a spreadsheet.”

The authors teach at Columbia Business School in New York. Mattan Griffel describes himself as an “entrepreneur, teacher and writer” and is a Professor of Business. Daniel Guetta is Associate Professor of Professional Practice at the Decision Risk and Operations division, and formerly worked at Palantir, the controversial data analysis company.

The remark about spreadsheets in the introduction is very much on point. The goal is to encourage business users to shift away from Microsoft Excel in favour of Python, Panda (a data analysis library for Python) and Jupyter Notebooks, an interactive Python-based tool that is great for analysing and visualising data. They are right in that Python/Jupyter is a more flexible and powerful tool than Excel for data analysis, even if some of the criticisms of Excel are not really valid; Excel can handle very large and complex datasets when used with query tools, and the million row limit is not really relevant as sane users would not attempt to load so much data at once.

The book is in two parts. Part one, by Griffel, is a Python tutorial, from Hello World to variables, Math, string handling, logic operators, loops, functions and so on. Part two, by Guetta, is about data analysis, and covers Python, Panda and Jupyter Notebooks using data from a fictional chain of restaurants for its examples and tutorials.

There are a few problems with this book. It feels more like two short books bound together than a single book, the first part is rather dry, and very little of what is taught in the first part is actually used in the second part. One could justify this on the grounds that learning the essentials of Python is worth doing regardless, but there must be a risk that a reader who dutifully begins at the beginning of the book will get bored and give up before reaching the more compelling second part.

I do not think it is easy to teach programming and it is often a dry subject especially in the early lessons – a shame as coding is to my mind an exciting and creative topic if only one can covey its potential. I do wonder whether it would have been better to focus on the data analysis from the beginning and teach the necessary bits of Python as and when they come up; I think that would have been a more satisfactory approach.

That said, I did enjoy the data analysis chapters as we learn how to explore a dataset and use it as a guide to business decisions, something which is of enormous practical value.
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_Python for MBAs_ by Mattan Griffel and Daniel Guetta is a high-level introduction to the Python programming language with special care taken to describe aspects of the language that will be of interest to business students and users. The book provides a good overview to both the Python programming language, the use of the Pandas data analytics module, and additional content customized for business needs, including data aggregation and advanced analytics.

The first part of the book, comprising chapters 1-4, serves as an introduction to the Python programming language. The introduction is both basic and complete, one that new users will find useful and advanced users will want to merely skim through. The introduction is well written, but contains nothing that isn't in many other Python language introductions.

The next three chapters, numbers 5-7, serve as an overview of the Python Pandas data analytics module, including how to load, examine, modify, and plot data using this library. This is the beginning of the part of the book that will be interesting to business students and users, as many of their interests will involve this type of activity. Again, the description is solid, but contains nothing that might not be found in other places, for example Joel Grus's _Data Science from Scratch_, or Jake VanderPlas's _Python Data Science Handbook_.

The final two chapters focus on data aggregation and advanced analytic techniques, and these are both useful and specialized to the interest of business students.

On the whole, the book is competent, well-written, and interesting. Most of the topics are covered in other sources, many of them freely available, but what is unique in this book is their summary here. One imagines that the book arose from a programming class for business students, and that it would serve similar niches well.
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A readable, clear, not too dry introductory python text.  I've read a few recently as I've gone from an organization that uses R to one that uses python, and this was quite good (and I loved that they told you to look at stack overflow in the intro chapters!).  One thing that was missing was a more extensive argument (beyond what is covered in a few paragraphs in chapter 5) for why to use python instead of Excel.  Excel is used by pretty much every business type and even accountants of my acquaintance, and in my experience it would take a LOT to get most of them trusting code over their vlookups and investing the time to learn a new way of doing things.
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I went through this book and found it easy to follow. I've got some slight familiarity with Python, so I was able to think through some of the issues I encountered when I entered something incorrectly.

I'd say that this book is for beginners with some familiarity with how to use computers. If you're expecting to go from zero computer literacy to being able to code, this isn't the book for you.
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There is no question that the ability to harness technology has become an essential skill in business today.  It is also the case that Python has become one of the most widely adopted programming languages, largely due to its versatility in solving problems and its user-friendly accessibility.  So, despite the many self-help texts the already exist in this area, there is always room for another, especially one that seeks to acclimate beginners to the field quickly and competently.  That is exactly what <i>Python for MBAs</i> attempts to accomplish and, for the most part, it succeeds in doing just that.

According to the authors, two business school faculty with considerable experience teaching the subject, the book is aimed at people who have never written computer code before, but feel the need to understand at least some part of this rapidly expanding area before the world passes them by altogether.  In particular, the authors want to show readers how they can use Python to automate some of their daily tasks—such as financial record-keeping or responding to messages—as well as condensing large, unwieldly databases into more useable forms.  To do this, they divide the writing into two distinct parts: Basic Python Skills and Dataset Management.

As promised, the book really does start at the beginning with an explanation of how to download the Python program and set up a home computer to facilitate the best programming environment.  That is followed in Part 1 by three fairly compact chapters in which the basics of the language itself are presented, including how variables are created and used, conditional (i.e., True or False) statements, data lists, simple loop structures, and basic functions.  All of this information is quite well explained and nicely illustrated with several examples.  In fact, readers are repeatedly encouraged to work through these exercises on their own to gain confidence in each of the skills.  If there is a shortcoming in this part of the text it would be that it seemed a little too terse and limited in presenting Python’s many features, although the discussion may be <i>just</i> enough to accomplish the authors’ goals.

In Part 2, the tone of the project changes considerably as the topic shifts from developing basic programming skills to learning to organize and manage large databases.  The motivation for the entire second half of the book is the analysis of a case study the authors have written on Dig, a New York-based restaurant chain, and how the company used data analytics to improve its operations.  Essentially, each of the five chapters in this part is devoted to acquainting the reader with various aspects of Pandas, which a Python-compatible software library used for manipulating large data bases and performing statistical and graphical analysis on that information.  Once again, the authors do an excellent job of explaining each new topic that arises and applying case-based problems as examples.

Overall, I liked <i>Python for MBAs</i> and I think it will be a useful tool to introduce some essential material.  However, I also found the book’s title to be a little misleading in that it promises to be more about developing Python programming skills than it actually delivers.  In reality, the text is more centered on database management, with only as much Python knowledge as necessary to facilitate that aim; a title such as <i>Managing Databases With Python </i> might have been more appropriate.  Also, the book may be difficult to use in a classroom setting because its exposition is so heavily tied to the authors’ embedded case study and it lacks the type of end-of-chapter problems and exercises found in traditional texts.  Still, this is a worthy effort that should meet the needs of a sizeable audience of business professionals.
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I have always hope to explore the areas of learning some simple programme language but it always look very intimidating. Appreciate the work of this book to make Python accessible to non-programmers.
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Python for MBAs is an excellent book for introducing this very useful programming language to business people who don't have programming experience.  In today's world it is a useful skill to acquire, and this versatile language can be very useful in many areas of business.  The case studies and exercises are all relevant and based on real world  examples so you can see exactly how you could make use of the language.  The book is accessible, easy to read and easy to understand.  Overall a great book and skill to learn.
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