2. Department of Computer Science and Engineering(Artificial Intelligence)
Presented By
P.DILEEP KUMAR :20AR1A4322
Under the esteemed guidance of
Ms.B.JAYA VARDHANI,M.Tech
Assistant professor
SAI TIRUMULA NVR ENGINEERING COLLEGE
(affiliated to J.N.T.University ,KAKINADA,Approved by AICTE ,Accredited by NAAC)
Jonnalagadda,GunturRoad,Narasaraopeta(MD),Palnadu(DIST),AndhraPradesh-522601
2020-2024
3. Department of Computer Science&Engineering
(ARTIFICIAL INTELLIGENCE)
Presented by:
P.DILEEP KUMAR 20AR1A4322
Under the esteemed guidance of
Internship Co-Ordinator Head of the Department
Ms.B.Jayavardhani, M.Tech Dr.K.Prasada Rao,M.Tech,Ph.D,
Assistant Professor Professor
4. CONTENTS
• Introduction to Data Science
• Key Components of Data Science
• The Data Science Process
• Tools and Technologies
• Applications of Data Science
• Impact of Data Svcience
• Future Trends
• Q&A
5. Introduction To Data science
• Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and
systems to extract knowledge and insights from structured and unstructured data.
• It combines techniques from statistics, mathematics, computer science, and domain-specific
knowledge to analyze and interpret complex data sets.
• Importance of Data Science in Today's World:
• Data-driven Decision Making: Empowers organizations to make informed decisions based on
evidence and analysis rather than intuition.
• Business Insights: Helps businesses gain a competitive edge by uncovering patterns, trends, and
opportunities hidden in large volumes of data.
• Innovation and Research: Drives innovation in various fields by providing a systematic approach to
analyzing and interpreting data.
• Predictive Analytics: Enables organizations to anticipate trends and make proactive decisions.
6. ABSTRACT
• Data science, at its core, is an interdisciplinary field that harnesses scientific
methodologies to derive meaningful insights from diverse datasets, whether
structured or unstructured. Comprising a fusion of statistics, programming
acumen, domain knowledge, and advanced analytical techniques, data science
serves as a pivotal force in contemporary decision-making processes. The data
science workflow navigates through crucial stages, encompassing data
collection, cleaning, exploratory data analysis, feature engineering, model
development, evaluation, and eventual deployment. Supported by a plethora of
tools and technologies, including Python, R, Pandas, NumPy, and machine
learning frameworks like TensorFlow and PyTorch, data scientists craft actionable
narratives through visualizations using tools such as Matplotlib and Tableau
7. Key Components of Data Science
• Statistics and Mathematics:
• Foundation: Fundamental understanding of
statistical concepts such as probability,
hypothesis testing, and regression analysis.
• Descriptive Statistics: Summarizing and
interpreting data using measures like mean,
median, and standard deviation.
• Inferential Statistics: Drawing conclusions
about a population based on a sample and
estimating uncertainties.
• Programming Skills:
• Core Languages: Proficiency in
programming languages such as Python
and R, commonly used for data analysis
and machine learning.
• Data Manipulation: Ability to work with
libraries like Pandas and NumPy for efficient
data manipulation and analysis.
• Scripting: Writing scripts to automate
repetitive tasks and streamline data
processes.
8. The Data Science Process
Data Collection
Source Identification: Identifying and selecting
relevant data sources based on the problem at hand.
Data Retrieval: Extracting data from databases,
APIs, files, or other storage systems.
Data Integration: Combining data from different
sources to create a unified dataset.
Data Cleaning and Preprocessing:
Handling Missing Data: Identifying and dealing
with missing values through imputation or removal.
Outlier Detection: Identifying and addressing
outliers that may distort analysis or modeling.
Exploratory Data Analysis (EDA):
Summary Statistics: Generating descriptive statistics
to understand the basic characteristics of the data.
Data Visualization: Creating visual representations
to explore patterns, trends, and relationships.
Feature Engineering:
Variable Creation: Creating new features from
existing ones to enhance model performance.
Dimensionality Reduction: Reducing the number of
features while retaining essential information.
9. Tools and Technologies
• Programming Languages:
• Python:
• General-purpose language with extensive libraries for data
science and machine learning.
• Widely used for its readability, versatility, and a large, active
community.
• R:
• Statistical programming language commonly used for data
analysis and visualization.
• Preferred for its robust statistical packages and visualization
capabilities.
• Programming Languages:
• Python:
• General-purpose language with extensive libraries for data science
and machine learning.
• Widely used for its readability, versatility, and a large, active
community.
• R:
• Statistical programming language commonly used for data analysis
and visualization.
• Preferred for its robust statistical packages and visualization
capabilities.
10.
11.
12.
13. Future Trends
Artificial Intelligence and Machine Learning
Advancements
Explainable AI
Automated Machine Learning (AutoML)
Edge Computing
Continued Growth of Big Data
Integration with IoT
14.
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16.
17.
18. CONCLUSION
• Data science is an interdisciplinary field that extracts insights from data
through scientific methods and processes.
• Key components include statistics, programming skills, domain
knowledge, data wrangling, machine learning, and data visualization.
• The data science process involves data collection, cleaning, exploratory
data analysis, feature engineering, model building, model evaluation, and
deployment.
• Essential tools and technologies include Python, R, Pandas, NumPy,
Scikit-Learn, TensorFlow, PyTorch, Matplotlib, Seaborn, Tableau, Hadoop,
and Spark.
