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presentation on data science with python
1. GLOBAL INSTITUTE OF TECHNOLOGY
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
2.
3. 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
4. • Data science is 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 ?
5. 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
6. WHY WE USE DATA 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
.
7. • 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.
8. DATA TYPES IN PYTHON:
• 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.
9. LIST
• Collection comma-separated values (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 ];
10. TUPLE
• A tuple is 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]
11. DICTIONARY:
• A collection of 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"}
13. PYTHON LIBRARY: NUMPY
It uses 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:
14. 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'>
