Prashant Yadav presented on data science and analysis at Babasaheb Bhimrao Ambedkar University in Lucknow, Uttar Pradesh. The presentation introduced data science, discussed its applications in various fields like business and healthcare, and covered key topics like open source tools for data science, common data analysis methodologies and algorithms, using Python for data analysis, and challenges in the field. The presentation provided an overview of data science from introducing the concept to discussing real-world applications and issues.
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...Simplilearn
This presentation on Introduction to Machine Learning will explain what is Machine Learning and how does Machine Learning works. By the end of this presentation, you will be able to understand what are the types of Machine Learning, Machine Learning algorithms and some of the breakthroughs in Machine Learning industry. You will also learn what Machine Learning has to offer to us in terms of career opportunities.
This Machine Learning presentation will cover the following topics:
1. Real life applications of Machine Learning
2. Machine Learning Challenges
3. How did Machine Learning evolve?
4. Why Machine Learning / Machine Learning benefits
5. What is Machine Learning?
6. Types of Machine Learning ( Supervised, Unsupervised & Reinforcement Learning )
7. Machine Learning algorithms
8. Breakthroughs in Machine Learning
9. Machine Learning Future
10. Machine Learning Career
11. Machine Learning job trends
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
How to validate a model?
What is a best model ?
Types of data
Types of errors
The problem of over fitting
The problem of under fitting
Bias Variance Tradeoff
Cross validation
K-Fold Cross validation
Boot strap Cross validation
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Python for Data Science | Python Data Science Tutorial | Data Science Certifi...Edureka!
( Python Data Science Training : https://www.edureka.co/python )
This Edureka video on "Python For Data Science" explains the fundamental concepts of data science using python. It will also help you to analyze, manipulate and implement machine learning using various python libraries such as NumPy, Pandas and Scikit-learn.
This video helps you to learn the below topics:
1. Need of Data Science
2. What is Data Science?
3. How Python is used for Data Science?
4. Data Manipulation in Python
5. Implement Machine Learning using Python
6. Demo
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...Simplilearn
This presentation on Introduction to Machine Learning will explain what is Machine Learning and how does Machine Learning works. By the end of this presentation, you will be able to understand what are the types of Machine Learning, Machine Learning algorithms and some of the breakthroughs in Machine Learning industry. You will also learn what Machine Learning has to offer to us in terms of career opportunities.
This Machine Learning presentation will cover the following topics:
1. Real life applications of Machine Learning
2. Machine Learning Challenges
3. How did Machine Learning evolve?
4. Why Machine Learning / Machine Learning benefits
5. What is Machine Learning?
6. Types of Machine Learning ( Supervised, Unsupervised & Reinforcement Learning )
7. Machine Learning algorithms
8. Breakthroughs in Machine Learning
9. Machine Learning Future
10. Machine Learning Career
11. Machine Learning job trends
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
How to validate a model?
What is a best model ?
Types of data
Types of errors
The problem of over fitting
The problem of under fitting
Bias Variance Tradeoff
Cross validation
K-Fold Cross validation
Boot strap Cross validation
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Python for Data Science | Python Data Science Tutorial | Data Science Certifi...Edureka!
( Python Data Science Training : https://www.edureka.co/python )
This Edureka video on "Python For Data Science" explains the fundamental concepts of data science using python. It will also help you to analyze, manipulate and implement machine learning using various python libraries such as NumPy, Pandas and Scikit-learn.
This video helps you to learn the below topics:
1. Need of Data Science
2. What is Data Science?
3. How Python is used for Data Science?
4. Data Manipulation in Python
5. Implement Machine Learning using Python
6. Demo
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
Machine Learning and Data Mining: 04 Association Rule MiningPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. This lecture introduces association rule mining and the Apriori algorithm
Presentation at Data ScienceTech Institute campuses, Paris and Nice, May 2016 , including Intro, Data Science History and Terms; 10 Real-World Data Science Lessons; Data Science Now: Polls & Trends; Data Science Roles; Data Science Job Trends; and Data Science Future
The Basics of Statistics for Data Science By StatisticiansStat Analytica
Want to learn data science, but don't know how to start learn data science from scratch? Here in this presentation you will going to learn the basics of statistics for data science. Start learn these basic statistics to get the good command over data science.
This presentation will present topics such as "What is Anomaly Detection? What are the different types of Data that may be used? What are the popular techniques may be used to identify anomalies. What are the best practices in anomaly detection? What is the Value of Anomaly Detection?
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Statistics And Probability Tutorial | Statistics And Probability for Data Sci...Edureka!
YouTube Link: https://youtu.be/XcLO4f1i4Yo
** Data Science Certification using R: https://www.edureka.co/data-science **
This session on Statistics And Probability will cover all the fundamentals of stats and probability along with a practical demonstration in the R language.
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
This presentation educates you about Classification and
Regression trees (CART), CART decision tree methodology, Classification Trees, Regression Trees, Differences in CART, When to use CART?, Advantages of CART, Limitations of CART and What is a CART in Machine Learning?.
For more topics stay tuned with Learnbay.
Machine Learning and Data Mining: 04 Association Rule MiningPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. This lecture introduces association rule mining and the Apriori algorithm
Presentation at Data ScienceTech Institute campuses, Paris and Nice, May 2016 , including Intro, Data Science History and Terms; 10 Real-World Data Science Lessons; Data Science Now: Polls & Trends; Data Science Roles; Data Science Job Trends; and Data Science Future
The Basics of Statistics for Data Science By StatisticiansStat Analytica
Want to learn data science, but don't know how to start learn data science from scratch? Here in this presentation you will going to learn the basics of statistics for data science. Start learn these basic statistics to get the good command over data science.
This presentation will present topics such as "What is Anomaly Detection? What are the different types of Data that may be used? What are the popular techniques may be used to identify anomalies. What are the best practices in anomaly detection? What is the Value of Anomaly Detection?
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Statistics And Probability Tutorial | Statistics And Probability for Data Sci...Edureka!
YouTube Link: https://youtu.be/XcLO4f1i4Yo
** Data Science Certification using R: https://www.edureka.co/data-science **
This session on Statistics And Probability will cover all the fundamentals of stats and probability along with a practical demonstration in the R language.
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
This presentation educates you about Classification and
Regression trees (CART), CART decision tree methodology, Classification Trees, Regression Trees, Differences in CART, When to use CART?, Advantages of CART, Limitations of CART and What is a CART in Machine Learning?.
For more topics stay tuned with Learnbay.
How to use Python to conduct regression analysis in management PhD research.pptxPhd Assistance
PhD Assistance provides guidance on using Python for regression analysis in management research, utilizing various libraries and modules specialized for this task.
Data Science & AI Road Map by Python & Computer science tutor in MalaysiaAhmed Elmalla
The slides were used in a trial session for a student aiming to learn python to do Data science projects .
The session video can be watched from the link below
https://youtu.be/CwCe1pKOVI8
I have over 20 years of experience in both teaching & in completing computer science projects with certificates from Stanford, Alberta, Pennsylvania, California Irvine universities.
I teach the following subjects:
1) IGCSE A-level 9618 / AS-Level
2) AP Computer Science exam A
3) Python (basics, automating staff, Data Analysis, AI & Flask)
4) Java (using Duke University syllabus)
5) Descriptive statistics using SQL
6) PHP, SQL, MYSQL & Codeigniter framework (using University of Michigan syllabus)
7) Android Apps development using Java
8) C / C++ (using University of Colorado syllabus)
Check Trial Classes:
1) A-Level Trial Class : https://youtu.be/v3k7A0nNb9Q
2) AS level trial Class : https://youtu.be/wj14KpfbaPo
3) 0478 IGCSE class : https://youtu.be/sG7PrqagAes
4) AI & Data Science class: https://youtu.be/CwCe1pKOVI8
https://elmalla.info/blog/68-tutor-profile-slide-share
You can get your trial Class now by booking : https://calendly.com/ahmed-elmalla/30min
And you can contact me on
https://wa.me/0060167074241
by Python & Computer science tutor in Malaysia
To succeed as a data scientist, you should follow a structured path known as the “Data Science Roadmap.” This path outlines foundational knowledge in math and programming. Data manipulation and visualization, exploratory data analysis. Machine learning, deep learning, and advanced topics such as natural language processing and time series analysis. Following this roadmap can help you acquire the skills and knowledge needed to excel in this rapidly growing field.
Becoming a successful data scientist requires a unique combination of technical skills, business acumen, and critical thinking ability. To achieve your career goals in this field, you need a structured plan or a data science roadmap that outlines the skills, tools, and knowledge required to succeed. In this blog, we’ll take a closer look at what a data science roadmap is, why it’s important, and how to create one that works for you.
At its core, It is a structured plan that outlines the skills, tools, and knowledge required to become a successful data scientist. It serves as a guidepost to help individuals navigate the complex landscape of data science and provides a clear path towards achieving their career objectives.
Data Analytics Course Curriculum_ What to Expect and How to Prepare in 2023.pdfNeha Singh
In 2023, aspiring data analysts can expect comprehensive data analytics course curriculums covering essential topics like statistical analysis, data visualization, machine learning, and big data processing. To prepare for the course, brushing up on basic mathematics, programming, and data handling skills would be beneficial.
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
1. SEMINAR
D E P A R T M E N T O F C O M P U T E R S C I E N C E
Data Science
& Analysis
TOPIC
Prashant Yadav
M.Tech (CS)
Roll No. 223410
PRESENTED BY
BABASAHEB BHIMRAO AMBEDKAR UNIVERSITY
LUCKNOW UTTAR PRADESH
2. CONTENTS
Introduction
Open Source Tools
Methodology
Python For Data Science
Data Analysis
Applications
Challenges
3. INTRODUCTION
• Data science is an interdisciplinary field that involves the use of statistical and
computational methods to extract insights and knowledge from data. It combines
techniques from mathematics, statistics, computer science, and domain-specific
knowledge to analyze and interpret complex data sets.
• Data science involves various stages, including data collection, data cleaning, data
analysis, and data visualization.
• The goal of data science is to uncover patterns, trends, and insights that can be used to
inform decision-making and solve real-world problems. It has applications in a wide range
of fields, including business, healthcare, finance, and social sciences.
4. • NEED OF DATA SCIENCE
PARAMETER Description
Data-driven decision making
Data science enables organizations to make informed
decisions based on data insights, rather than relying on
intuition or guesswork.
Predictive analytics
Data science allows organizations to use historical data
to make predictions about future events or trends, such
as customer behavior or market trends.
Improved efficiency and productivity
By automating repetitive tasks and streamlining
processes, data science can help organizations improve
efficiency and productivity.
Personalization
Data science enables organizations to personalize their
products or services to individual customers, based on
their preferences and behavior.
Fraud detection
Data science can be used to detect fraudulent activity,
such as credit card fraud or insurance fraud, by
analyzing patterns and anomalies in data.
Risk management
Data science can help organizations identify and
mitigate risks, such as financial risks or cybersecurity
risks, by analyzing data and identifying potential
threats.
Improved customer experience
By analyzing customer data, data science can help
organizations improve the customer experience by
identifying pain points and areas for improvement.
Competitive advantage
Data science can provide organizations with a
competitive advantage by enabling them to make data-
5. • Real World Example Of Data Science
1. Credit Risk Assessment:
• A bank uses data science to analyze customer data and credit history to assess
the risk of default on loans.
• Machine learning algorithms are used to identify patterns in customer behavior
and credit history that are associated with higher risk.
• Based on these insights, the bank can make informed decisions about loan
approvals and interest rates.
2. Predictive Maintenance:
• A manufacturing company uses data science to predict when equipment is likely to
fail.
• Sensor data is collected from the equipment and analyzed using machine learning
algorithms to identify patterns that are associated with equipment failure.
• Based on these insights, the company can schedule maintenance before equipment
failure occurs, reducing downtime and maintenance costs.
6. OPEN SOURCE TOOLS
Tool Description Suitable for
Python
A popular programming language for data
science, with a wide range of libraries and
frameworks for data analysis, machine
learning, and visualization.
Programmers
R
A programming language and environment for
statistical computing and graphics, with a wide
range of packages for data analysis and
visualization.
Programmers
Jupyter
Notebook
An open-source web application that allows
users to create and share documents that
contain live code, equations, visualizations,
and narrative text.
Both
7. Apache Spark
An open-source distributed computing system
for big data processing, with support for data
analysis, machine learning, and graph
processing.
Programmers
Apache
Hadoop
An open-source distributed computing system
for storing and processing large data sets, with
support for data analysis and machine
learning.
Programmers
Tableau
A data visualization tool that allows users to
create interactive dashboards and reports.
Non-
programmers
KNIME
An open-source data analytics platform that
allows users to create workflows for data
analysis, machine learning, and visualization.
Both
8. RapidMiner
An open-source data science platform
that allows users to create workflows for
data analysis, machine learning, and
visualization.
Both
Orange
An open-source data visualization and
analysis tool that allows users to create
workflows for data analysis and machine
learning.
Both
Weka
An open-source machine learning tool
that allows users to create and apply
machine learning models to data sets.
Both
10. METHODOLOGY
• The Business Understanding stage is crucial because it helps to clarify the goal of the customer. In this
stage, we have to ask a lot of questions to the customer about every single aspect of the problem.
• The next step is the Analytic Approach, where, once the business problem has been clearly stated, the
data scientist can define the analytic approach to solve the problem.
• Data Requirements is the stage where
we identify the necessary data content,
formats, and sources for initial data
collection, and we use this data inside the
algorithm of the approach we chose.
• In the Data Collection Stage, data
scientists identify the available data
resources relevant to the problem
domain. To retrieve data, we can do web
scraping on a related website, or we can
use repository with premade datasets
ready to use.
( Decision Tree)
11. METHODOLOGY
• In the Data Understanding stage, data scientists try to understand more about the data collected before.
We have to check the type of each data and to learn more about the attributes and their names.
• In the Data Preparation stage, data scientists prepare data for modeling, which is one of the most crucial
steps because the model has to be clean and without errors.
• In the Modeling stage, the data scientist has the chance to understand if his work is ready to go or if it
needs review. Modeling focuses on developing models that are either descriptive or predictive, and these
models are based on the analytic approach that was taken statistically or through machine learning.
12. METHODOLOGY
• In the Model Evaluation stage, data scientists can evaluate the model in two ways: Hold-Out
and Cross-Validation. In the Hold-Out method, the dataset is divided into three subsets:
a training set as we said in the modeling stage; a validation set that is a subset used to
assess the performance of the model built in the training phase; a test set is a subset to
evaluate the likely future performance of a model.
• The Deployment stage depends on the purpose of the model, and it may be rolled out to a
limited group of users or in a test environment.
• The Feedback stage is usually made the most from the customer.
13. METHODOLOGY
Common Algorithms :
1 .Linear Regression
A statistical method used to model the relationship between a dependent variable and one or
more independent variables.
We can use simple linear regression when you want to know:
1. How strong the relationship is between two variables (e.g., the relationship between rainfall
and soil erosion).
2. The value of the dependent variable at a certain value of the independent variable (e.g., the
amount of soil erosion at a certain level of rainfall).
14. METHODOLOGY
Simple linear regression formula :
y is the predicted value of the dependent
variable (y) for any given value of the
independent variable (x).
B0 is the intercept, the predicted value
of y when the x is 0.
B1 is the regression coefficient – how much
we expect y to change as x increases.
x is the independent variable ( the variable
we expect is influencing y).
e is the error of the estimate, or how much
variation there is in our estimate of the
regression coefficient.
15. METHODOLOGY
2. Decision Tree :
• A decision tree is a machine learning
algorithm that uses a tree-like model of
decisions and their possible consequences
to predict outcomes. It is a supervised
learning algorithm that can be used for both
classification and regression tasks.
• The decision tree algorithm works by
recursively splitting the data into subsets
based on the values of the input features.
The goal is to create a tree that predicts the
target variable with high accuracy.
17. PYTHON FOR DATA SCIENCE
Python is a popular programming language for data science due to its simplicity, versatility, and
extensive libraries and frameworks for data analysis, machine learning, and visualization.
Here are some of the key libraries and frameworks in Python for data science:
• NumPy: A library for numerical computing in Python, with support for arrays, matrices, and
mathematical functions.
• Pandas: A library for data manipulation and analysis in Python, with support for data structures
such as data frames and series.
• Matplotlib: A library for data visualization in Python, with support for creating a wide range of
charts and graphs.
• Scikit-learn: A library for machine learning in Python, with support for a wide range of algorithms
for classification, regression, clustering, and more.
• TensorFlow: A library for machine learning and deep learning in Python, with support for building
and training neural networks.
18.
19. DATA ANALYSIS
• Data analysis using Python involves using the Python programming language and its
associated libraries and frameworks to manipulate, analyze, and visualize data. Python
is a popular language for data analysis due to its simplicity, versatility, and extensive
libraries and frameworks for data analysis, machine learning, and visualization.
• By using Python for data analysis, we can gain insights into complex data sets and
make informed decisions based on data insights. Python's popularity in data analysis is
also due to its ease of use and readability, making it accessible to both experienced
programmers and beginners.
• The process of data analysis using Python typically involves several steps, including
data cleaning, data manipulation, data analysis, and data visualization. Python libraries
such as Pandas, NumPy, and Matplotlib are commonly used for these tasks.
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23. APPLICATIONS
• Business: Data science and analysis are widely used in business to analyze
customer data, sales data, and market trends to inform decision-making. This
includes customer segmentation, product recommendations, and pricing
optimization.
• Healthcare: Data science and analysis are used in healthcare to analyze patient
data, identify disease patterns, and improve patient outcomes. This includes
disease diagnosis, drug discovery, and personalized medicine.
• Finance: Data science and analysis are used in finance to analyze financial data,
identify market trends, and inform investment decisions. This includes risk
assessment, fraud detection, and portfolio optimization.
• Social media: Data science and analysis are used in social media to analyze user
behavior, identify trends, and improve user engagement. This includes sentiment
analysis, user profiling, and content recommendation And Many more as such
applications of data Science and analysis exist.
24. CHALLENGES
• Data Quality: One of the biggest challenges in data science is ensuring that the data being used is accurate,
complete, and reliable. Poor data quality can lead to inaccurate results and flawed insights.
• Data Volume: With the increasing amount of data being generated, managing and processing large volumes
of data can be a significant challenge. This requires specialized tools and techniques for data storage,
processing, and analysis.
• Data Variety: Data comes in many different forms, including structured, semi-structured, and unstructured
data. Working with unstructured data, such as text, images, and video, can be particularly challenging and
requires specialized techniques for natural language processing, computer vision, and other areas of artificial
intelligence.
• Data Privacy and Security: As data becomes more valuable, ensuring its privacy and security becomes
increasingly important. Data scientists need to be aware of privacy regulations and take steps to protect
sensitive data from unauthorized access.
• Model Interpretability: Machine learning models can be complex and difficult to interpret, making it
challenging to understand how they arrived at their conclusions. This can be particularly problematic in
applications where decisions have significant consequences, such as healthcare or finance.
• Business Understanding: Data scientists need to have a deep understanding of the business context in
which they are working in order to develop insights that are relevant and actionable.
