Recently, more and more companies are learning that they need to make DATA-DRIVEN decisions. And with big data and data science on the rise, we now have more data than we know what to do with.
In fact, without a doubt, you have already experienced data science in one way or another. Obviously, you are interacting with data science products every time you search for information on the web by using search engines such as Google, or asking for directions with your mobile phone.
Data science is the science and technology focused on collecting raw data and processing it in an effective manner. It is the combination of concepts and methods that make it possible to give meaning and understandability to huge volumes of data.
Data science has been the force behind resolving some of our most common daily tasks for several years. In nearly all of our daily work, we directly or indirectly work on storing and exchanging data. With the rapid development of technology, the need to store data effectively is also increasing. That's why it needs to be handled properly. Basically, data science unearths the hidden insights of raw-data and uses them for productive output.
Python is often used in data science today because it is a mature programming language that has excellent properties for newbie programmers. Some of the most remarkable of these properties are its easy to read code, suppression of non-mandatory delimiters, dynamic typing, and dynamic memory usage. Python is an interpreted language, and it can be executed in the Python console without any need to compile to machine language.
βPython Data Scienceβ teaches a complete course of data science, including key topics like data integration, data mining, python etc. We will explore NumPy for numerical data, Pandas for data analysis, IPython, Scikit-learn and Tensorflow for machine learning and business.
Each of the chapters in this book is devoted to one of the most interesting aspects of data analysis and processing. The following are some of the major topics covered in Python Data Science:
Understanding Data Science
Getting Started with Python for Data Scientists
Descriptive statistics
Data Analysis and Libraries
NumPy Arrays and Vectorized Computation
Data Analysis with Pandas
Data Visualization
Data Mining
Classifying with Scikit-learn Estimators
Giving Computers the Ability to Learn from Data
Training Machine Learning Algorithms
The Python ecosystem for data science discussed within Python Data Science includes SciPy, NumPy, Matplotlib, Pandas, and Scikit-learn, which provides all of the data science algorithms.
Data processing and analysis is one of the hottest areas of IT, where developers who can handle projects of any level, from social networks to trained systems, are constantly required. We hope this book will be the starting point for your journey into the fascinating world of Data Science.