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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.
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.
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- SeriesFor Dummies (Wiley)