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Have a clear concept of Data Science with these Books
- Extensive sample code and tutorials using Python along with its technical libraries
- Core technologies of â??Big Data,â?? including their strengths and limitations and how they can be used to solve real-world problems
- Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity
- A wide variety of case studies from industry
- Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed
- Learn the basics of linear algebra, statistics, and probability-and understand how and when they're used in data science
- Collect, explore, clean, munge, and manipulate data
- Dive into the fundamentals of machine learning
- Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases