presentation on data science with python

presentation on data science with python

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  GLOBAL INSTITUTE OFTECHNOLOGY Department of Artificial Intelligence and Data Science A SEMINAR TRAINING ON DATA SCIENCE WITH PYTHON Submitted to: Mr. Pradeep Jha Head of Dept. CS/IT/AIDS Presented By: Student Name: Khushbu Jain Reg. No.: 20EGJAD014 Semester and Year – 5th Sem (3rd Year) Session: 2022-23
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  TABLE OF CONTENT: •WHAT IS DATA SCIENCE? • WHAT IS PYTHON? • WHY WE USE DATA SCIENCE WITH PYTHON? • DATA TYPES IN PYTHON • PYTHON BASICS : LOOPING • PYTHON LIBRARY: NUMPY AND PANDAS • APPLICATION OF DATA SCIENCE
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  • Data scienceis the process of finding insights/trends/ intelligence that supports the business leaders to make the better decision. • Data science is a relatively new field and deeply rooted to Statistics and Decision Support System. • It is a Multidisciplinary field ( Domain Knowledge, Tools & technology, Mathematics & Statistics, Problem Solving Skills). What is Data Science ?
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  WHAT IS PYTHON: •A high-level general-purpose programming language. • A very popular Data Science tool for data analysis, data visualization and Machine Learning tasks • It is a open source and free tool
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  WHY WE USEDATA SCIENCE WITH PYTHON ? • Python is object-oriented • The following primary factors cited by Python users seem to be these: • Structure supports such concepts as polymorphism, operation overloading, and multiple inheritance. • .It's free (open source) • Downloading and installing Python is free and easy Source code is easily accessible .
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  • It's powerful -Dynamic typing - Built-in types and tools - Library utilities - Third party utilities (e.g. Numeric, NumPy, SciPy) - Automatic memory management • It's portable - Python runs virtually every major platform used today -As long as you have a compatible Python interpreter installed, Python programs will run in exactly the same manner, irrespective of platform.
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  DATA TYPES INPYTHON: • Python has many native data types. Here are the important ones: • Booleans are either True or False. • Numbers can be integers (1 and 2), floats (1.1 and 1.2), fractions (1/2 and 2/3), or even complex numbers. • Strings are sequences of Unicode characters, e.g. an HTML document. • Bytes and byte arrays, e.g. a JPEG image file. • Lists are ordered sequences of values. • Tuples are ordered, immutable sequences of values. • Sets are unordered bags of values.
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  LIST • Collection comma-separatedvalues (items) between square brackets • Contain same or different types • Mutable behavior Values can add, remove, update/replace the value, slice and dice the members Example: list1 = ['physics', 'chemistry', 1997, 2000]; list2 = [1, 2, 3, 4, 5 ];
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  TUPLE • A tupleis very similar to List A collection of items inside the parenthesis() • Tuple is Immutable ( The value cannot be changed) • Can slice and dice add elements and Delete the entire tuple • Example: • tup2 = (1, 2, 3, 4, 5 ); • tup3 = ("a", "b", "c", "d“); • Accessing Values: print "tup2[1:5]: “ Output: • tup2[1:5]: [2, 3, 4, 5]
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  DICTIONARY: • A collectionof unordered data values • A dictionary holds key value pairs of data • The items are separated by commas, and the whole thing is enclosed in curly braces • Keys are immutable but the values are mutable - can add modify and Delete values • Example: Dictionary. capitals = {"USA":"Washington D.C.", "France":"Paris", "India":"New Delhi"}
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  PYTHON BASIC: LOOPING
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  PYTHON LIBRARY: NUMPY Ituses multidimensional arrays and matrices, as well as functions to perform the computation • Allow to perform advanced mathematical and statistical operations on the above objects • It provides vectorization of mathematical operations on arrays and matrices which significantly improves the performance many other python libraries are built on the top of NumPy library • EXAMPLE:
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  PYTHON LIBRARY :PANDAS • It adds data structures and tools designed to work with table-like data (similar to table in SQL Server environment) • It provides tools for data manipulation: selecting, reshaping, merging, sorting, slicing, aggregation etc. • It also handles missing data • EXAMPLE: import numpy as np #importing numpy import pandas as pd #importing pandas arr=np.array([1,3,5,7,9]) #create arr array s2=pd.Series(arr) #create pandas series s2 print(s2) #print s2 print(type(s2)) #print type of s2 Output: 0 1 1 3 2 5 3 7 4 9 dtype: int64 <class 'pandas.core.series.Series'>
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  APPLICATION OF DATASCIENCE: