What is Data Science?
Data science uses techniques and theories within the context of mathematics, statistics, computer science, domain knowledge, and information science by unifying the concepts of data analysis, machine learning, and their related methods to understand and analyze data to extract meaningful insights.
Organizations around the globe are relying on Data science which blends various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the ‘Raw Data’ to provide descriptive analytics ‘what happened?’ from the information available, further diagnostic analytics provide ‘why did it happen?’ and finally with predictive analytics provides information as to ‘what could happen’ in the future.
Data science with Python and Machine learning
Python has emerged over the last several years as the first choice tool for scientific computing of the tasks including the analysis and visualization of large datasets.
The use of Python for data science stems primarily from the large and active ecosystem of third-party packages :
NumPy for manipulation of the homogeneous array-based data
Pandas for manipulation of heterogeneous and labeled data.
SciPy for common scientific computing tasks.
Matplotlib for publication-quality visualizations.
Interactive execution and sharing of the code.
Scikit- learn for machine learning and much more.