Data Science Programming All-In-One For Dummies

Data Science Programming All-In-One For Dummies
  • eBook:
    Data Science Programming All-In-One For Dummies
  • Author:
    John Paul Mueller, Luca Massaron
  • Edition:
    1 edition
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Book Description

Data Science Programming All-In-One For Dummies by John Paul Mueller, Luca Massaron

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!


Book 1: Defining Data Science
Chapter 1: Considering the History and Uses of Data Science
Chapter 2: Placing Data Science within the Realm of AI
Chapter 3: Creating a Data Science Lab of Your Own
Chapter 4: Considering Additional Packages and Libraries You Might Want
Chapter 5: Leveraging a Deep Learning Framework

Book 2: Interacting with Data Storage
Chapter 1: Manipulating Raw Data
Chapter 2: Using Functional Programming Techniques
Chapter 3: Working with Scalars, Vectors, and Matrices
Chapter 4: Accessing Data in Files
Chapter 5: Working with a Relational DBMS
Chapter 6: Working with a NoSQL DMBS

Book 3: Manipulating Data Using Basic Algorithms
Chapter 1: Working with Linear Regression
Chapter 2: Moving Forward with Logistic Regression
Chapter 3: Predicting Outcomes Using Bayes
Chapter 4: Learning with K-Nearest Neighbors

Book 4: Performing Advanced Data Manipulation
Chapter 1: Leveraging Ensembles of Learners
Chapter 2: Building Deep Learning Models
Chapter 3: Recognizing Images with CNNs
Chapter 4: Processing Text and Other Sequences

Book 5: Performing Data-Related Tasks
Chapter 1: Making Recommendations
Chapter 2: Performing Complex Classifications
Chapter 3: Identifying Objects
Chapter 4: Analyzing Music and Video
Chapter 5: Considering Other Task Types
Chapter 6: Developing Impressive Charts and Plots

Book 6: Diagnosing and Fixing Errors
Chapter 1: Locating Errors in Your Data
Chapter 2: Considering Outrageous Outcomes
Chapter 3: Dealing with Model Overfitting and Underfitting
Chapter 4: Obtaining the Correct Output Presentation
Chapter 5: Developing Consistent Strategies

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