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Data Science: 2 Manuscripts in 1
- Complete Guide to Learning Data Science Process & What You Need to Know About Machine Learning with Python
- ナレーター: Matthew Kinsey Shane Makena
- 再生時間: 6 時間 42 分
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あらすじ・解説
Data science is the application of a combination of mathematical, statistical, analytical, and programming skills for the collection, organization, and interpretation of data to allow effective and proper management of the business whose data it is.
The job of such a scientist is trending all over the world. The demand for such scientists is huge, more than the number of available candidates. A recent report explained that the need for these scientists has increased by more than 50 percent since last year. These scientists often referred to as big data wranglers, are a perfect blend of mathematician and computer scientist.
Data science is a field of study that is growing at a fast pace. From big tech companies to E-commerce companies to websites and many others are now relying on data science. Amount of data that is collected by these companies are without any bounds. Semi-structure to big unstructured data is stored in large frameworks of these companies. Now the question is how to use this.
What you will gain as knowledge in this audiobook:
- Why Is Data Science Widely Used?
- Why Should You Study Data Science?
- Why Should One Consider Data Science as a Career?
- Data Science: An Exciting Career Option
- Types of Data Loss and Recovery Options
- Data Science and Its Wide Range of Applications
- What Are the Programming Languages Required for Data Science?
- Meaning of Data Science in Depth
- 4 Weird Ways How Data Is Used around the World
- 5 Reasons Why Data Science Could Be the Advertising Wave of the Future
It is a field where one should be trained and practiced. Without proper training and applicative skills, one cannot be as successful as a data scientist. There is a lot to learn about various data science tools and techniques. Getting certified will not only help you hone your skills but also will confirm your future as a data scientist.