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Data Science is an interdisciplinary field that focuses on extracting knowledge from data sets which are typically huge in amount. The field encompasses analysis, preparing data for analysis, and presenting findings to inform high-level decisions in an organization. As such, it incorporates skills from computer science, mathematics, statistics, information visualization, graphic, and business.

Data is everywhere and is one of the most important features of every organization that helps a business to flourish by making decisions based on facts, statistical numbers, and trends. Due to this growing scope of data, data science came into picture which is a multidisciplinary IT field, and data scientists jobs are the most demanding in the 21st century. Data analysis/ Data science helps us to ensure we get answers for questions from data. Data science, and in essence, data analysis plays an important role by helping us to discover useful information from the data, answer questions, and even predict the future or the unknown. It uses scientific approaches, procedures, algorithms, the framework to extract the knowledge and insight from a huge amount of data.Data science is a concept to bring together ideas, data examination, Machine Learning, and their related strategies to comprehend and dissect genuine phenomena with data. It is an extension of data analysis fields such as data mining, statistics, predictive analysis. It is a huge field that uses a lot of methods and concepts which belong to other fields like in information science, statistics, mathematics, and computer science. Some of the techniques utilized in Data Science encompasses machine learning, visualization, pattern recognition, probability model, data engineering, signal processing, etc.Few important steps to help you work more successfully with data science projects:

Data scientists straddle the world of both business and IT and possess unique skill sets. Their role has assumed significance thanks to how businesses today think of big data. Business wants to make use of the unstructured data which can boost their revenue. Data scientists analyze this information to make sense of it and bring out business insights that will aid in the growth of the business.

Now, lets get started with the foremost topic i.e., Python Packages for Data Science which will be the stepping stone to start our Data Science journey. A Python library is a collection of functions and methods that allow us to perform lots of actions without writing any code.1. Scientific Computing Libraries:

2. Visualization Libraries:

3. Algorithmic Libraries:

{data: array([[ 0., 0., 5., , 0., 0., 0.],[ 0., 0., 0., , 10., 0., 0.],[ 0., 0., 0., , 16., 9., 0.],,[ 0., 0., 1., , 6., 0., 0.],[ 0., 0., 2., , 12., 0., 0.],[ 0., 0., 10., , 12., 1., 0.]]), target: array([0, 1, 2, , 8, 9, 8]), target_names: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), images: array([[[ 0., 0., 5., , 1., 0., 0.],[ 0., 0., 13., , 15., 5., 0.],[ 0., 3., 15., , 11., 8., 0.],,[ 0., 4., 11., , 12., 7., 0.],[ 0., 2., 14., , 12., 0., 0.],[ 0., 0., 6., , 0., 0., 0.]],[[ 0., 0., 0., , 5., 0., 0.],[ 0., 0., 0., , 9., 0., 0.],[ 0., 0., 3., , 6., 0., 0.],,[ 0., 0., 1., , 6., 0., 0.],[ 0., 0., 1., , 6., 0., 0.],[ 0., 0., 0., , 10., 0., 0.]],[[ 0., 0., 0., , 12., 0., 0.],[ 0., 0., 3., , 14., 0., 0.],[ 0., 0., 8., , 16., 0., 0.],,[ 0., 9., 16., , 0., 0., 0.],[ 0., 3., 13., , 11., 5., 0.],[ 0., 0., 0., , 16., 9., 0.]],,[[ 0., 0., 1., , 1., 0., 0.],[ 0., 0., 13., , 2., 1., 0.],[ 0., 0., 16., , 16., 5., 0.],,[ 0., 0., 16., , 15., 0., 0.],[ 0., 0., 15., , 16., 0., 0.],[ 0., 0., 2., , 6., 0., 0.]],[[ 0., 0., 2., , 0., 0., 0.],[ 0., 0., 14., , 15., 1., 0.],[ 0., 4., 16., , 16., 7., 0.],,[ 0., 0., 0., , 16., 2., 0.],[ 0., 0., 4., , 16., 2., 0.],[ 0., 0., 5., , 12., 0., 0.]],[[ 0., 0., 10., , 1., 0., 0.],[ 0., 2., 16., , 1., 0., 0.],[ 0., 0., 15., , 15., 0., 0.],,[ 0., 4., 16., , 16., 6., 0.],[ 0., 8., 16., , 16., 8., 0.],[ 0., 1., 8., , 12., 1., 0.]]]), DESCR: .. _digits_dataset:nnOptical recognition of handwritten digits datasetnnn**Data Set Characteristics:**nn :Number of Instances: 5620n :Number of Attributes: 64n :Attribute Information: 88 image of integer pixels in the range 0..16.n :Missing Attribute Values: Nonen :Creator: E. Alpaydin (alpaydin @ boun.edu.tr)n :Date: July; 1998nnThis is a copy of the test set of the UCI ML hand-written digits datasetsnhttps://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+DigitsnnThe data set contains images of hand-written digits: 10 classes whereneach class refers to a digit.nnPreprocessing programs made available by NIST were used to extractnnormalized bitmaps of handwritten digits from a preprinted form. From antotal of 43 people, 30 contributed to the training set and different 13into the test set. 3232 bitmaps are divided into nonoverlapping blocks ofn4x4 and the number of on pixels are counted in each block. This generatesnan input matrix of 88 where each element is an integer in the rangen0..16. This reduces dimensionality and gives invariance to smallndistortions.nnFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, n1994.nn.. topic:: Referencesnn C. Kaynak (1995) Methods of Combining Multiple Classifiers and Theirn Applications to Handwritten Digit Recognition, MSc Thesis, Institute ofn Graduate Studies in Science and Engineering, Bogazici University.n E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.n Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.n Linear dimensionalityreduction using relevance weighted LDA. School ofn Electrical and Electronic Engineering Nanyang Technological University.n 2005.n Claudio Gentile. A New Approximate Maximal Margin Classificationn Algorithm. NIPS. 2000.}

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What is Data Science? - GeeksforGeeks