Data Science Programming All-In-One for Dummies
- 12h 30m
- John Paul Mueller, Luca Massaron
- John Wiley & Sons (US)
- 2020
Your logical, linear guide to the fundamentals of data science programming
Data science is exploding―in a good way―with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models.
Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time.
- Get grounded: the ideal start for new data professionals
- What lies ahead: learn about specific areas that data is transforming
- Be meaningful: find out how to tell your data story
- See clearly: pick up the art of visualization
Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life―and everyone else’s!
About the Authors
John Mueller has produced 114 books and more than 600 articles on topics ranging from functional programming techniques to working with Amazon Web Services (AWS). Luca Massaron, a Google Developer Expert (GDE), interprets big data and transforms it into smart data through simple and effective data mining and machine learning techniques.
In this Book
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Introduction
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Considering the History and Uses of Data Science
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Placing Data Science within the Realm of AI
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Creating a Data Science Lab of Your Own
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Considering Additional Packages and Libraries You Might Want
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Leveraging a Deep Learning Framework
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Manipulating Raw Data
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Using Functional Programming Techniques
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Working with Scalars, Vectors, and Matrices
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Accessing Data in Files
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Working with a Relational DBMS
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Working with a NoSQL DMBS
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Working with Linear Regression
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Moving Forward with Logistic Regression
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Predicting Outcomes Using Bayes
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Learning with K-Nearest Neighbors
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Leveraging Ensembles of Learners
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Building Deep Learning Models
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Recognizing Images with CNNs
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Processing Text and other Sequences
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Making Recommendations
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Performing Complex Classifications
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Identifying Objects
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Analyzing Music and Video
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Considering other Task Types
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Developing Impressive Charts and Plots
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Locating Errors in Your Data
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Considering Outrageous Outcomes
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Dealing with Model Overfitting and Underfitting
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Obtaining the Correct Output Presentation
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Developing Consistent Strategies
