This presentation provides a comprehensive overview of Data Science concepts and their practical implementation using Python. It covers the essential stages of a typical data science workflow — from data collection and preprocessing to exploratory data analysis, model building, and result interpretation. The presentation highlights the significance of popular Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and TensorFlow, showcasing how they empower data scientists to handle real-world data-driven problems efficiently. Through illustrative examples, case studies, and hands-on demonstrations, this presentation aims to equip participants with a strong foundation in applying Python for data analysis, visualization, and machine learning tasks. It also emphasizes best practices in data handling and model evaluation to derive meaningful insights and make informed decisions.In the modern, data-driven world, information is being generated at an unprecedented pace. Every click, search, transaction, and interaction contributes to an ever-growing repository of data. From businesses optimizing operations to healthcare systems predicting disease outbreaks and financial firms assessing risks, data is at the core of every significant decision. However, raw data on its own holds limited value. It is the art and science of deriving meaningful insights from this data that fuels innovation and drives impactful decision-making processes. Data Science is the interdisciplinary field that blends statistics, computer science, and domain expertise to extract knowledge and insights from structured and unstructured data. It involves several stages, including data collection, cleaning, exploration, modeling, and visualization, all aimed at making data-driven predictions and decisions. Among the many programming languages available for data science, Python has emerged as one of the most popular and powerful tools due to its simplicity, flexibility, and the vast ecosystem of libraries and frameworks tailored for data-related tasks. This presentation titled "Data Science with Python" serves as an introductory yet comprehensive guide to understanding the fundamental concepts of data science and how Python acts as an effective medium for performing data-centric tasks. It encompasses theoretical explanations, practical implementations, and real-world examples to ensure a well-rounded learning experience. Objectives of the Presentation The primary objective of this presentation is to introduce the audience to the realm of data science while focusing on the practical application of Python as a tool for performing data analysis, visualization, and machine learning. The specific goals include: To provide a clear understanding of what data science is and its significance in modern industries. To introduce the standard workflow followed in data science projects. To familiarize the audience with Python’s libraries and frameworks that facilitate datasci

1. DATA SCIENCE USING PYTHON
Under the guidance of
Dr. M. Krishna
Assistant Professor
Department of Electrical and Electronics Engineering
GAYATRI VIDYA PARISHAD COLLEGE OF ENGINEERING FOR WOMEN (A)
[Approved by AICTE NEW DELHI, Affiliated to Andhra University]
[Accredited by National Board of Accreditation (NBA) for B.Tech. CSE, ECE & IT – Valid from 2019-22 and 2022-
25]
[Accredited by National Board of Accreditation (NBA) for B.Tech. EEE – Valid from 2023-24 and 2025-26]
Kommadi , Madhurawada, Visakhapatnam–530048
2024–2025

2. INTRODUCTION TO AI
• Artificial intelligence (AI) is a set of technologies that enable computers
to perform a variety of advanced functions, including the ability to see,
understand and translate spoken and written language, analyse data, make
recommendations, and more.
• AI is the backbone of innovation in modern computing,
unlocking value for individuals and businesses.
• For example, optical character recognition (OCR) uses AI to
extract text and data from images and documents, turns
unstructured content into business-ready structured data, and
unlocks valuable insights.

3. Types of Artificial Intelligence:
• Reactive machines
• Limited memory
• Theory of mind
• Self-aware
Benefits of AI

4. Data science
 Data Science is a combination of multiple disciplines that uses statistics, data analysis,
and machine learning to analyse data and to extract knowledge and insights from it
Data Science is about data gathering, analysis and decision-making.
 Data Science is about finding patterns in data, through analysis, and make future
predictions.
 By using Data Science, companies are able to make.
 Better decisions.
 Predictive analysis.
 Pattern discoveries.

5. Data Scientist Work
A Data Scientist requires expertise in several backgrounds
 Machine Learning
 Statistics
 Programming (Python or R)
 Mathematics
 Databases
Applications of Data Science

6. Machine learning
 Machine learning (ML) allows computers
to learn and make decisions without being
explicitly programmed.
 It involves feeding data into algorithms to
identify patterns and make predictions on
new data. Machine learning is used in
various applications, including image and
speech recognition, natural language
processing, and recommender systems.
 It drives better decision-making and
tackles intricate challenges efficiently.

7. Python
Python is a programming language widely used by Data
Scientists.
 Python has in-built mathematical libraries and functions,
making it easier to calculate mathematical problems and
to perform data analysis.
Why Python for Data Science?
 Python is favoured in data science due to its readability,
simplicity, and versatility.
 Its extensive libraries and frameworks streamline
complex tasks, allowing data scientists to focus on
problem-solving rather than coding intricacies.

8. Key Libraries and Tools & Benefits on python
NumPy: A fundamental library for numerical operations
in Python, supporting large, multi-dimensional arrays and
matrices.
pandas: A powerful library for data manipulation and
analysis, offering data structures like Data Frames to
handle structured data efficiently.
Scikit-learn: A comprehensive library for machine
learning, providing simple and efficient data mining and
analysis tools.
Matplotlib and Seaborn: Libraries for creating static,
animated, and interactive visualizations, helping to
understand data patterns and trends.

9. Python syntax, Data types & Operators
Python Syntax
Python syntax refers to the set of rules that define how a Python program is written and
interpreted. One of the most distinctive and beginner-friendly aspects of Python is its clean
and readable syntax. Unlike many other programming languages that use curly braces {} to
define code blocks, Python uses indentation (whitespace). This means that consistent
indentation is not just a style, but a requirement in Python—usually 4 spaces are used per
indentation level.

10. Python Data Types
In Python, data types define the kind of value a variable can hold, and they are essential for
writing correct and efficient programs. Python is a dynamically typed language, which
means you don't need to declare the data type of a variable explicitly—Python detects it
automatically at runtime. The built-in data types in Python can be broadly categorized into
several types.
 Numeric Types
 Sequence Types
 Set Types
 Mapping Type
 Boolean Type
 None Type

11. Operators
In Python, operators are special symbols or keywords used to perform operations on
variables and values. Python supports several types of operators that enable arithmetic
operations, comparisons, logical decisions, bitwise manipulations, and more. These
operators make it easier to write expressions that carry out calculations and control flow in
programs.
• Arthamatic Operators
• Comparison (Relational) Operators
• Logical Operators
• Assignment Operators
• Bitwise Operators
• Membership Operators
• Identity Operators

12. THANK YOU