Python for Data Science For Dummies (For Dummies (Computer/Tech)) 1st Edition

I was taking Data Mining at my University when I decided that I wanted to get a bit of help and get an edge on the subject so I was ahead of the class. This book helped explain everything and even had the output of the code to show what each code block would do. I ended up searching around on the author's website and he does have all of the source code in a zip file. Easy book to help you start understanding Data Science.

There's nothing dummy about this book. Offers clear and useful explanations and code and gives you extra bowl results.

I guess I've been a data scientist without knowing it because there have so far been very few new things introduced in this bookLots of "padding" chapters. For example-If you know a bit of python, you can skip section I. If you don't know python, you'll need another book for that!The examples in section ii are really useful, but kind of basic. It leaves me wanting moreI skimmed section iii, since it was basically the helpfile to the software used in this book. The types of visuals are important to know, but very basic. I was hoping to encounter something new here!I'm about to start section iv, but I'm not too hopeful.

First chapters are especially not well written. This book suffers from poor explanations and choice of examples. Some good software engineering practices aren't followed either. Later chapters are better, covering important topics, including data transformation and modeling techniques, but the poor treatment persists.This book also has a rather unusual negligence in layout that is bad for your eyes. This book has very small screenshots with font size 8 or 9 in the early chapters. Most of them even totally unnecessary. Font size for the code suddenly changes from size 12 to 10 or 9 in chapters 13 and 14.There are many better books in the market, now that Python has been more and more used in Data Science. You don't have to accept this poor treatment.

Are you a Beginner who would like to learn python, in context with a specific area, and tired of using syntax focused books sans practical examples? ORAre you exploring data science landscape and want to see practical examples of how to actually use machine learning algorithms in data science context?If you answer in the affirmative to either of the questions above, "Python for data science for dummies" is the perfect book for you. Luca Massaron is a practicing data scientist, and a prolific author of several books including Regression Analysis with Python , Machine Learning For Dummies, Python Data Science Essentials, Regression Analysis with Python, and Large Scale Machine Learning with Python. He is also a leading Kaggle enthusiast, and you can see his 'practitioner fingerprints' all over this book; especially in later chapters about data processing, ETL, cleanup, data sources, and challenges.This book starts with the fundamentals of Python data analysis programming, and explains the setup of Python development environment using anaconda with IPython (Jupyter notebooks). Authors start by considering the emergence of data science, outline the core competencies of a data scientist, and describe the Data Science Pipeline before taking a plunge into explaining Python’s Role in Data Science and introducing Python’s Capabilities and Wonders.Once you get your bearings about the IDE setup, chapter 4 focuses on Basic Python before you get your Hands Dirty with Data. What I like about this manuscript is that the writing keeps it real. Instead of giving made up examples, authors talk about items like knowing when to use NumPy or pandas and real world scenarios like removing duplicates, creating a data map and data plan, dealing with Dates in Your Data, Dealing with Missing data, parsing etc; problems which practicing data scientists encounter on a daily basis.Contemporary topics like Text mining are also addressed in the book with enough details of topics such as working with Raw Text, Stemming and removing stop words, Bag of Words Model and Beyond, Working with n‐grams, Implementing TF‐IDF transformations, and adjacency matrix handling. This is also where you start getting a basic understanding of how machine learning algorithms work in practice.Practical aspects of evaluating a data science problem are addressed later, with techniques defined for researching solutions, formulating a hypothesis, data preperation, feature creation, binning and discretization, leading up to vectors and matrix manipulation, and visualization with MatPlotLib. Even though the book does not discuss theano, DL4J, Torch, Caffe or TensorFlow, it still provides an introduction to key python ML library Scikit‐learn. This 400 page book also covers key topics like SVD, PCA, NMF, Recommendation systems, Clustering, Detecting Outliers, logistic Regression, Naive Bayes, Fitting a Model, bias and variance, Support Vector Machines, and Random Forest classifiers to name a few. The resources provided in the end are definitely worth subscribing to for every self-respecting data science enthusiast.I highly recommend this book for those beginners interested in data science and also want to learn and leverage Python skills for this rapidly emerging field.

Nice book to take you step by step, good shipment condition.

Need to explain with examples

An excellent book from beginners to advanced. Python is well explained.Can't find any other book this good at this price.