Data Science Programming All-in-One For Dummies
John Paul Mueller, Luca Massaron
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Data Science Programming All-in-One For Dummies
John Paul Mueller, Luca Massaron
About This Book
Your logical, linear guide to the fundamentals of data science programming
Data science is explodingâin a good wayâwith 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 lifeâand everyone else's!
Frequently asked questions
Information
Defining Data Science
Contents at a Glance
- Chapter 1: Considering the History and Uses of Data Science
- Considering the Elements of Data Science
- Defining the Role of Data in the World
- Creating the Data Science Pipeline
- Comparing Different Languages Used for Data Science
- Learning to Perform Data Science Tasks Fast
- Chapter 2: Placing Data Science within the Realm of AI
- Seeing the Data to Data Science Relationship
- Defining the Levels of AI
- Creating a Pipeline from Data to AI
- Chapter 3: Creating a Data Science Lab of Your Own
- Considering the Analysis Platform Options
- Choosing a Development Language
- Obtaining and Using Python
- Obtaining and Using R
- Presenting Frameworks
- Accessing the Downloadable Code
- Chapter 4: Considering Additional Packages and Libraries You Might Want
- Considering the Uses for Third-Party Code
- Obtaining Useful Python Packages
- Locating Useful R Libraries
- Chapter 5: Leveraging a Deep Learning Framework
- Understanding Deep Learning Framework Usage
- Working with Low-End Frameworks
- Understanding TensorFlow
Considering the History and Uses of Data Science
Considering the Elements of Data Science
Considering the emergence of data science
Outlining the core competencies of a data scientist
- Data capture: It doesnât matter what sort of math skills you have if you canât obtain data to analyze in the first place. The act of capturing data begins by managing a data source using database-management skills. However, raw data isnât particularly useful in many situations; you must also understand the data domain so that you can look at the data and begin formulating the sorts of questions to ask. Finally, you must have data-modeling skills so that you understand how the data is connected and whether the data is structured.
- Analysis: After you have data to work with and understand the complexities of that data, you can begin to perform an analysis on it. You perform some analysis using basic statistical tool skills, much like those that just about everyone learns in college. However, the use of specialized math tricks and algorithms can make patterns in the data more obvious or help you draw conclusions that you canât draw by reviewing the data alone.
- Presentation: Most people donât understand numbers well. They canât see the patterns that the data scientist sees. Providing a graphical presentation of these patterns is important to help others visualize what the numbers mean and how to apply them in a meaningful way. More important, the presentation must tell a specific story so that the impact of the data isnât lost.