Data Science with Python Training.

The Introduction to Data Science class will survey the foundational topics in data science, namely: Data Manipulation. Data Analysis with Statistics and Machine Learning. Data Communication with Information Visualization. Data at Scale -- Working with Big Data. Data science with python training is all the same Python. Just the difference is in the usage of different libraries. If we use a particular library to get the job done, it is then Python for that particular domain. Data is the new Oil. This statement shows how every modern IT system is driven by capturing, storing, and analyzing data for various needs.

Following are the features of the Python language:

• A simple and easy to learn a language that achieves results in fewer lines of code than other similar languages like R. Its simplicity also makes it robust to handle complex scenarios with minimal code and much less confusion in the general flow of the program.
• It is cross-platform, so the same code works in multiple environments without needing any change. That makes it perfect to be used in a multi-environment setup easily.
• It executes faster than other similar languages used for data analysis like R and MATLAB.
• Its excellent memory management capability, especially garbage collection makes it versatile in gracefully managing very large volumes of data transformation, slicing, dicing, and visualization.
• Most importantly Python has got a very large collection of libraries that serve as special-purpose analysis tools. For example – the NumPy package deals with scientific computing and its array needs much less memory than the conventional python list for managing numeric data. And the number of such packages is continuously growing.
• In the subsequent chapters, we will see how we can leverage these features of python to accomplish all the tasks needed in the different areas of Data Science.
• Python has packages that can directly use the code from other languages like Java or C. This helps in optimizing the code performance by using existing code of other languages, whenever it gives a better result.