Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
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.
Data science is an interdisciplinary field that uses algorithms, procedures, and processes to examine large amounts of data in order to uncover hidden patterns, generate insights, and direct decision making.
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
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Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
A changing market landscape and open source innovations are having a dramatic impact on the consumability and ease of use of data science tools. Join this session to learn about the impact these trends and changes will have on the future of data science. If you are a data scientist, or if your organization relies on cutting edge analytics, you won't want to miss this!
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
Data Science Tutorial | What is Data Science? | Data Science For Beginners | ...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
In this PPT on Data Science Tutorial, you’ll get an in-depth understanding of Data Science and you’ll also learn how it is used in the real world to solve data-driven problems. It’ll cover the following topics in this session:
Need for Data Science
Walmart Use case
What is Data Science?
Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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.
Data science is an interdisciplinary field that uses algorithms, procedures, and processes to examine large amounts of data in order to uncover hidden patterns, generate insights, and direct decision making.
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
A changing market landscape and open source innovations are having a dramatic impact on the consumability and ease of use of data science tools. Join this session to learn about the impact these trends and changes will have on the future of data science. If you are a data scientist, or if your organization relies on cutting edge analytics, you won't want to miss this!
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
Data Science Tutorial | What is Data Science? | Data Science For Beginners | ...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
In this PPT on Data Science Tutorial, you’ll get an in-depth understanding of Data Science and you’ll also learn how it is used in the real world to solve data-driven problems. It’ll cover the following topics in this session:
Need for Data Science
Walmart Use case
What is Data Science?
Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
The talk is on How to become a data scientist. This was at 2ns Annual event of Pune Developer's Community. It focuses on Skill Set required to become data scientist. And also based on who you are what you can be.
Dirty data? Clean it up! - Datapalooza Denver 2016Dan Lynn
Dan Lynn (AgilData) & Patrick Russell (Craftsy) present on how to do data science in the real world. We discuss data cleansing, ETL, pipelines, hosting, and share several tools used in the industry.
Alexey Zinoviev presented this paper on Second Thumbtack Technology Expert Day.
This paper covers next topics: Data Mining, Machine Learning, Octave, R language
YouTube: http://youtu.be/kGIP6XeWiaA
If you’re learning data science, you’re probably on the lookout for cool data science projects. Look no further! We have a wide variety of guided projects that’ll get you working with real data in real-world scenarios while also helping you learn and apply new data science skills.
The projects in the list below are also designed to help you get a job! Each project was designed by a data scientist on our content team, and they’re representative examples of the real projects working data analysts and data scientists do every day. They’re designed to guide you through the process while also challenging your skills, and they’re open-ended so that you can put your own twist on each project and use it for your data science portfolio.
You can complete each project right in your browser, or you can download the data set to your computer and work locally! If you work on our site, you’ll also be able to download your code at any time so that you can continue locally, or upload your project to GitHub.
The sky is the limit here and what you decide to look into further is completely up to you and your imagination!
1. Learning by Doing
Learning by doing refers to a theory of education expounded by American philosopher John Dewey. It is a hands-on approach to learning, meaning students must interact with their environment in order to adapt and learn. This way of learning sharpen your current skills and knowledge and also helps in gaining new skills that could only be acquired by doing.
Car driving is a perfect example of this, you can read as much as you would like about the theory of driving and the rules, and this is very important, and the more you understand the theory the better you get in the practical part. But you will only be able to drive better by applying this knowledge on the real road. In addition to that, there are some skills and knowledge that will be only gained by actually driving.
Data science is the same as driving. It is very important to have solid theoretical knowledge and to regularly increase them to be able to get better while working on a project. However, you should always apply this theoretical knowledge to projects. By this, you will deepen your understanding of these concepts and Knowledge, have a better point of view of how they work in a real-life, and will also show others that you have strong theoretical knowledge and are able to put them into practice.
There are different types of guided projects. One of them is a guided project for
There are a lot of benefits for it:
It removes the barriers between you and doing projects
Saves you much time thinking about the project and preparing the data.
It allows you to apply the theoretical knowledge without getting distracted by obstacles.
Practical tips that can save your effort and time in the future.
#datasciencefree
#rohitdubey
#teachtechtoe
#linkedin.com/in/therohitdubey
Start your Data Science career journey with an extensive & practical Data Science course designed for young professionals and recent college graduates. We provide in-depth knowledge of Python’s data analytics tools and techniques in this Data Science certification program.
Data Con LA 2022 - Intro to Data ScienceData Con LA
Zia Khan, Computer Systems Analyst and Data Scientist at LearningFuze
Data Science tutorial is designed for people who are new to Data Science. This is a beginner level session so no prior coding or technical knowledge is required. Just bring your laptop with WiFi capability. The session starts with a review of what is data science, the amount of data we generate and how companies are using that data to get insight. We will pick a business use case, define the data science process, followed by hands-on lab using python and Jupyter notebook. During the hands-on portion we will work with pandas, numpy, matplotlib and sklearn modules and use a machine learning algorithm to approach the business use case.
Which institute is best for data science?DIGITALSAI1
EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
Join us for the Best Selenium certification course at Edux factor and enrich your carrier.
Dream for wonderful carrier we make to achieve your dreams come true Hurry up & enroll now.
<a href="https://eduxfactor.com/selenium-online-training">Best Selenium certification course</a>
Data Science Online Training In HA comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.hyderabad Data Science Online Training
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Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Show drafts
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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/
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).
2. ● Eduction
○ 2012 Pass out, M.Sc. Information system - Bits, Pilani Rajasthan.
○ Trained in RHEL 6, AIX, Business Communications
○ Certified Data Modelling Engineer.
● Software Engineer
○ 4.5 Years in Data Engineering & Data Analytic.
○ 1 Year in Data Sciences and Data Modelling.
○ Python, Oracle DB, Oracle Apex.
● Personal Life
○ Teaching(blog), Music, Anime, lazy.
○ Health Conscious, Gym/Yoga/lots of Sleep.
○ Technology & Personal communication skills.
● Motivation:
○ Bridge the gap between Technology and People. Lead a R&D Team.
About Me
3. 0:05 Nobody's born smart
1:08 Because the most beautiful, complex concepts in the whole universe are built on basic ideas
1:13 that anyone can learn, anywhere can understand. Whoever you are, whereever you are
1:18 You only have to know one thing: You can learn anything
4.
5. 2011 Watson - Jeopardy
Data Science
1952 - Tic Tac Toe ⇒ Human vs Computer
1997 - Deep Blue - Chess ⇒ Exploring Solution Space
2011 - Watson - Jeopardy ⇒ Constructive Reasoning
2017 - AlphaGo - Go ⇒ Developing Intuition
In AlphaGo, no. of possibilities > total no. atoms in this universe.
6. Plan
Introduction
● Definitions [ Data Science ]
● What, Why and How
● Examples
Data Science - In Action
● Stages [DG, DC, DM, ME]
● Regression & Clustering Models
● Basics [ LR, GD ]
Real Life Application
● Examples
Data Science Tools
● Examples
Suggestions
● Tips
8. What is Data Science?
da•ta
Factual information, especially information organized for
analysis or used to reason or make decisions.
Computer Science Numerical or other information
represented in a form suitable for processing by computer.
Values derived from scientific experiments.
sci·ence (sī′əns)
The observation, identification, description,
experimental investigation, and theoretical explanation
of phenomena. Ex. New advances in science and
technology.
Such activities restricted to a class of natural
phenomena. Ex. The science of astronomy.
A systematic method or body of knowledge in a given
area. Ex. The science of marketing.
Archaic Knowledge, especially that gained through
experience.
12. Information Explosion & Doubling Processing Power
Metcalfe's law states that the value of a telecommunications network is
proportional to the square of the number of connected users of the system (n2).
Moore's law is the observation that the number of transistors in a dense integrated
circuit doubles approximately every two years.
(Population - Thanks to Advanced Medical Sciences & Improving Health Care.)
Sources: Wikipedia
14. How to Data Science? - AI, ML
Rosey, Spacely, Jetson MIT Cheetah Robot
15. How to do Data Science
You can use lots of sophisticated analytical & Business Intelligent tools and come to
a simple understandable explanations.
(or)
You can also use, simple tools like calculators or excel sheet to generate simple
and simple results.
16. Plan
Introduction
● Definitions [ Data Science ]
● What, Why and How
● Examples
Data Science - In Action
● Stages [DG, DC, DM, ME]
● Regression & Clustering Models
● Basics [ LR, GD ]
Real Life Application
● Examples
Data Science Tools
● Examples
Suggestions
● Tips
21. Plan
Introduction
● Definitions [ Data Science ]
● What, Why and How
● Examples
Data Science - In Action
● Stages [DG, DC, DM, ME]
● Regression & Clustering Models
● Basics [ LR, GD ]
Real Life Application
● Examples
Data Science Tools
● Examples
Suggestions
● Tips
22. Data Science - Real Life App
Few applications that inspired me
23. Passive Designs + AI
Maurice Cont
Director of Applied Research & Innovation
Autodesk, San Francisco Bay Area.
TED Talk: The incredible inventions of intuitive AI
24. Generative Designs > Passive Designs
AI Designed Lightweight Cabin Partition
Airbus - A320
AI Designed Lightweight Drone Chassis
27. Music XRay
● Jimmy Lloyd Songwriter Showcase
● Popular songs share Melody & Rhythm
● Genere - 70
● Cluster 60
● Singer & Song Writer NY
● http://www.heidimerrill.com/epk/index.html
28. Pred Pole
● 2011 Santa Cruz Pred Pole
● Crime, Location & Date-Time
● https://www.predpol.com/
Results:
● 50% Crime Rate control
● 20% reduction in Crime Rate
30. Plan
Introduction
● Definitions [ Data Science ]
● What, Why and How
● Examples
Data Science - In Action
● Stages [DG, DC, DM, ME]
● Regression & Clustering Models
● Basics [ LR, GD ]
Real Life Application
● Examples
Data Science Tools
● Examples
Suggestions
● Tips
31. Data Science - Tools
Too many to name, but none of them are close perfection.
32. Data Science Tools
● Languages: Scala, R, Python, Java, C#
● Lib: Scikit, DeepNet, Tensor flow, Theano, H20
● Frameworks: Apache Spark
These are some used by used us (Imaginea Labs - Data Sciences - 4th Floor, Hyd).
34. Suggestions?
● Data Preparation
○ “Give me six hours to chop down a tree and I will spend the first four sharpening the axe”.
Abraham Lincoln
○ Python, Scala, Excel, Databases(regex).
● Data Analytics
○ “Seeing is believing”
○ Python(Matplotlib, Seaborn), D3.Js, Excel.
● Data Models
○ “There are no perfect solutions, but some work better”
○ Learn 2-3 types of Clustering, Regression Models(LR,RF,SVM,KNN,XGB)
● Evaluation
○ “A product not tested is broken by default”
○ Accuracy, RMSE, Precision-Recall, F1 Score
1952 - Tic Tac Toe # Picture Above. First Human vs Computer race started.
1997 - Deep Blue - Chess ==> Exploring Solution Space
2011 - Watson - Jeopardy ==> Constructive Reasoning
2017 - Alpha Go - Go - [Possibilities > total no. atoms in this universe] ==> Developing Intuition
1952 - Tic Tac Toe
1997 - Deep Blue - Chess ==> Exploring Solution Space
2011 - Watson - Jeopardy ==> Constructive Reasoning
2017 - Alpha Go - Go - [Possibilities > total no. atoms in this universe] ==> Developing Intuition
AQ - System Admins/Developers/ QA/ HR/
AQ - How many of you heard of Data Science? Can you explain me, what is data science to you?
Learn to draw - Newton’s observation of Apple falling from a Tree. Trojan Horse. Galileo - Watching ships moving, Kepler’s Law - Planetary System. Edision - bulb.
> Newton’s Laws of Motions
> Laws of Diminishing Returns
> Kepler’s Laws of Planetary Motions
> U-235 Chain Reaction
> Arts - Music, Painting, Linguistics,..
Usual Method: Data ⇒ Analysis ⇒ Rules/ Principles.
Data ⇒ Principles/Laws/Observation ⇒ Evaluation Experiments ⇒ Real Life Applications.
# Landing on Moon # Talking to a person at the other End of the world # Flying to other end of worlds
Basic Fundamental
Artificial Intelligence. Actual Goal of - simulate a human being.
1 understand 2 (action) interact 3 expressive # they know table manners
Like a child, first achievement is talking first step.
1 understand situations 2 acting(judge height/speed/time)
Director of Applied Research & Innovation, Autodesk
3D Printed AI Design - Cabin Partition for Airbus - A320
Cars - Manufactured to Farmed
Buildings - Constructions to Growns
Cities - Isolated to Connected
Traditions Race Car Chassis - Gave Nervous System - 4 Billions Data Points
4 Billions Data Points
AI - Predicting if a Song will be HIT
Songs - Optimal Mathematical Patterns
25 Million Views
#### Minority Report is a 2002 American Sci-Fi
#### Director:Steven Spielberg
#### Starring:Tom Cruise, Colin Farrell, Samantha Morton, Max von Sydow
Project Interlace - Singapore
DayLights Problems + Energy Consumption + Water Bodies(micro Climates)
Experience is the best mentor.
Open Sourced Tools used us, if you are planning to use these - you can take some help.