Foundation of Data Science
Data science entails a wide range of disciplines and skill areas to deliver a comprehensive, systematic, and advanced examination of raw data. Data scientists rely heavily on artificial intelligence to construct models and make predictions using algorithms and other techniques, especially its subfields of machine learning and deep learning.
Five-Stage Lifecycle of Data Science
Data collection, data entry, signal reception, and data extraction are all data capture examples.
Data cleansing, data staging, data analysis, and data engineering.
Data mining, clustering/classification, data modeling, and data summarization.
Data visualization, data reporting, business intelligence, and decision-making.
Qualitative analysis, exploratory/confirmatory, predictive analysis, regression, text mining. All five stages necessitate unique strategies, services, and, in some instances, skill sets.
Python with Data Science
Data science consulting firms are encouraging their developers and data scientists to use Python as a programming language. Python has become the most common and essential programming language in a brief time.
Data scientists must process vast amounts of data, which is referred to as big data. Python has become a popular choice for dealing with big data due to its ease of use and extensive library of python libraries.
It’s ideal for programmers with experience in application and web creation. It’s no wonder that most Data Scientists prefer this to the other programming options on the market.
Python is essential for data scientists because it includes many valuable and easy-to-use libraries such as Pandas, Numpy, Scipy, Tensorflow.
Useful Python Libraries
Python has several packages that help data scientists create deep learning algorithms, such as Tensorflow, Keras, and Theano. Python provides superior assistance with deep learning algorithms. Some of the essential libraries are:
NumPy is an acronym that stands for Numerical Python. The n-dimensional array is NumPy’s most crucial function. This library also includes simple linear algebra functions, Fourier transforms advanced random number capabilities, and integration tools for Fortran, C, and C++.
SciPy is an abbreviation for Scientific Python. NumPy serves as the foundation for SciPy. It is an instrumental library for a wide range of high-level science and engineering modules such as discrete Fourier transform, linear algebra, optimization, and sparse matrices.
Matplotlib can be used to build a wide range of graphs, from histograms to line plots to heat maps. To use these plotting features inline, use the Pylab option in ipython notebook (ipython notebook –pylab = inline).
Scrapy is a web crawling tool. It is an instrumental framework for obtaining complex data trends. It can start at a website’s home page and then digging through web pages inside the website to gather details.
Python in data science has allowed data scientists to do more in less time. Python is a universal programming language that is both easy to learn and highly efficient.
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