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Unleash the power of Python for your data analysis projects with For Dummies!
Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide.
- Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models
- Explains objects, functions, modules, and libraries and their role in data analysis
- Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib
Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.
- Sales Rank: #290115 in Books
- Published on: 2015-07-07
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x .80" w x 7.40" l, 1.47 pounds
- Binding: Paperback
- 432 pages
From the Back Cover
Learn to:
- Take advantage of Python data analysis programming
- Work with Python objects, functions, modules, and libraries
- Apply statistical concepts such as probability and random distributions
- Use NumPy, SciPy, Scikit-learn, and Pandas libraries
Wow 'em with your mastery of Python for managing and analyzing data!
Python is a programming language perfectly suited for data science. Even if it's brand new to you, this straightforward guide will help you learn to use Python programming to acquire, organize, process, and analyze large amounts of information and identify trends and patterns. From installing Python to performing cross-validation, learn with this book!
- See why Python works for data science — tour the data science pipeline and learn about Python's basic capabilities
- Get set up — install Python, download datasets and example code, and start working with numbers and logic, creating functions, and storing and indexing data
- Make it visual — explore MatPlotLib and create graphs, pie and bar charts, histograms, and advanced scatterplots
- Delve deeper — venture into classes and multiprocessing, define descriptive statistics for numeric data, and use applied visualization
- Advanced data wrangling — examine solutions to dimensionality reduction, perform hierarchical clustering, and learn to detect outliers in your data
- Make data tell you something — work with linear models and perform cross-validation, selection, and optimization
Open the book and find:
- Fundamentals of Python data analysis programming
- All about the Python development environment
- How to use random distributions and regression models
- Advice on accessing data from the web
- What to do with NumPy, pandas, and SciPy
- Tips on working with HTML pages
- How to create interactive graphical representations
- Ten essential data resources
To download the source code files for the examples in this book, go to
www.Dummies.com/extras/pythonfordatascience
About the Author
John Paul Mueller, consultant, application developer, writer, and technical editor, has written over 600 articles and 97 books. His topics range from programming to home security. Luca Massaron is a data scientist and a research director specializing in multivariate statistical analysis, machine learning, and customer insight. He is a pioneer of Web audience analysis in Italy and was named one of the top ten data scientists at competitions by kaggle.com.
Most helpful customer reviews
2 of 2 people found the following review helpful.
Outstanding Getting started Guide for Python, Data Science and Machine Learning for wanna be practitioners & hobbyists
By Adnan Masood, PhD
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? OR
Are 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.
4 of 5 people found the following review helpful.
Easy book to help you start understanding Data Science
By Bryan Nosar
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.
4 of 5 people found the following review helpful.
Five Stars
By John A. Quandt
It really helped me get up to speed quickly on numerical processing in Python 2.7
See all 12 customer reviews...
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