In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
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
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
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
This presentation explains what data engineering is and describes the data lifecycles phases briefly. I used this presentation during my work as an on-demand instructor at Nooreed.com
Data modelling is considered a staple in the world of data management. The skill of the data modeler and their knowledge of the business plays a large role in successful Enterprise Information Management across many organizations. Data modeling requires formal accountability, attention to metadata and getting the business heavily involved in data requirement development. These are all traits of solid Data Governance programs.
Join Bob Seiner and a special guest modeler extraordinaire in this month’s installment of Real-World Data Governance to discuss data modeling as a form of data governance. Learn how to use the skillfulness of the data modeler to advance data-as-an-asset and governance agendas while conveying the importance and value of both disciplines.
In this webinar Bob and a special guest will talk about:
•Data Modeling as Art or Science
•Role of Data Modeler in a Governance Program
•Data Modeler Skills as Governance Skills
•Modeling and Governance Best Practices
•Leveraging the Model as a Governance Artifact
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"
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
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
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
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
This presentation explains what data engineering is and describes the data lifecycles phases briefly. I used this presentation during my work as an on-demand instructor at Nooreed.com
Data modelling is considered a staple in the world of data management. The skill of the data modeler and their knowledge of the business plays a large role in successful Enterprise Information Management across many organizations. Data modeling requires formal accountability, attention to metadata and getting the business heavily involved in data requirement development. These are all traits of solid Data Governance programs.
Join Bob Seiner and a special guest modeler extraordinaire in this month’s installment of Real-World Data Governance to discuss data modeling as a form of data governance. Learn how to use the skillfulness of the data modeler to advance data-as-an-asset and governance agendas while conveying the importance and value of both disciplines.
In this webinar Bob and a special guest will talk about:
•Data Modeling as Art or Science
•Role of Data Modeler in a Governance Program
•Data Modeler Skills as Governance Skills
•Modeling and Governance Best Practices
•Leveraging the Model as a Governance Artifact
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"
Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
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.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Driving Data Intelligence in the Supply Chain Through the Data Catalog at TJXDATAVERSITY
Roles and responsibilities are a critical component of every Data Governance program. Building a set of roles that are practical and that will not interfere with people’s “day jobs” is an important consideration that will influence how well your program is adopted. This tutorial focuses on sharing a proven model guaranteed to represent your organization.
Join Bob Seiner for this lively webinar where he will dissect a complete Operating Model of Roles and Responsibilities that encompasses all levels of the organization. Seiner will detail the roles and describe the most effective way to associate people with the roles. You will walk out of this webinar with a model to apply to your organization.
In this session Bob will share:
- The five levels of Data Governance roles
- A proven Operating Model of Roles and Responsibilities
- How to customize the model to meet your requirements
- Setting appropriate role expectations
- How to operationalize the roles and demonstrate value
YouTube Link: https://youtu.be/aGu0fbkHhek
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Data Science Full Course" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science PPT will start with basics of Statistics and Probability and then moves to Machine Learning and Finally ends the journey with Deep Learning and AI. For Data-sets and Codes discussed in this PPT, drop a comment.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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 Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
Data Science is the Sexiest job in 21st century. Big Data Concept is going to rule the 21st century. Here is the presentation to give complete information and overview of data science big data.
Data Science Training | Data Science Tutorial for Beginners | Data Science wi...Edureka!
***** Data Science Training - https://www.edureka.co/data-science *****
This Edureka tutorial on "Data Science Training" will provide you with a detailed and comprehensive training on Data Science, the real-life use cases and the various paths one can take to become a data scientist. It will also help you understand the various phases of Data Science.
Data Science Blog Series: https://goo.gl/1CKTyN
http://www.edureka.co/data-science
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
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this session I will share insights and knowledge that I have gained from building up a Data Science department from scratch. The talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organization.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
JavaZone 2018 - A Practical(ish) Introduction to Data ScienceMark West
Code: https://github.com/markwest1972/titanic
Video: https://vimeo.com/289705893
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all of this?
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
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.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Driving Data Intelligence in the Supply Chain Through the Data Catalog at TJXDATAVERSITY
Roles and responsibilities are a critical component of every Data Governance program. Building a set of roles that are practical and that will not interfere with people’s “day jobs” is an important consideration that will influence how well your program is adopted. This tutorial focuses on sharing a proven model guaranteed to represent your organization.
Join Bob Seiner for this lively webinar where he will dissect a complete Operating Model of Roles and Responsibilities that encompasses all levels of the organization. Seiner will detail the roles and describe the most effective way to associate people with the roles. You will walk out of this webinar with a model to apply to your organization.
In this session Bob will share:
- The five levels of Data Governance roles
- A proven Operating Model of Roles and Responsibilities
- How to customize the model to meet your requirements
- Setting appropriate role expectations
- How to operationalize the roles and demonstrate value
YouTube Link: https://youtu.be/aGu0fbkHhek
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Data Science Full Course" provides an end to end, detailed and comprehensive knowledge on Data Science. This Data Science PPT will start with basics of Statistics and Probability and then moves to Machine Learning and Finally ends the journey with Deep Learning and AI. For Data-sets and Codes discussed in this PPT, drop a comment.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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 Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
Data Science is the Sexiest job in 21st century. Big Data Concept is going to rule the 21st century. Here is the presentation to give complete information and overview of data science big data.
Data Science Training | Data Science Tutorial for Beginners | Data Science wi...Edureka!
***** Data Science Training - https://www.edureka.co/data-science *****
This Edureka tutorial on "Data Science Training" will provide you with a detailed and comprehensive training on Data Science, the real-life use cases and the various paths one can take to become a data scientist. It will also help you understand the various phases of Data Science.
Data Science Blog Series: https://goo.gl/1CKTyN
http://www.edureka.co/data-science
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
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this session I will share insights and knowledge that I have gained from building up a Data Science department from scratch. The talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organization.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
JavaZone 2018 - A Practical(ish) Introduction to Data ScienceMark West
Code: https://github.com/markwest1972/titanic
Video: https://vimeo.com/289705893
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all of this?
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
NDC Oslo : A Practical Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this?
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
(1) I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
(2) Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
(3) The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Smarter businesses apply AI to learn and continuously evolve the way they work. To extract full value from AI, companies need data strategy that gives them access to all their data – no matter where it lives – in an environment that easily scales and applies the latest discovery technology including advanced analytics, visualization and AI. Learn how IBM Watson and Data provides all the tools companies need to embed AI, machine learning and deep learning in their business, while enabling professionals to gain the most from their data to drive smarter business and lead industry-changing transformations.
DATA SCIENCE IS CATALYZING BUSINESS AND INNOVATION Elvis Muyanja
Today, data science is enabling companies, governments, research centres and other organisations to turn their volumes of big data into valuable and actionable insights. It is important to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. According to the McKinsey Global Institute, the U.S. alone could face a shortage of about 190,000 data scientists and 1.5 million managers and analysts who can understand and make decisions using big data by 2018. In coming years, data scientists will be vital to all sectors —from law and medicine to media and nonprofits. Has the African continent planned to train the next generation of data scientists required on the continent?
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.
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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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Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
A 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.
Eduxfactor is an online data science training institution based in Hyderabad. A 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.
Data science online training in hyderabadVamsiNihal
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
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A 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.
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Learn to collect, clean, and analyze big data with R. Understand how to employ appropriate modeling and methods of analytics to extract meaningful data for decision making.
Implement clustering methodology, an unsupervised learning method, and a deep neural network (a supervised learning method).
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A 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
EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
Data science online training in hyderabadVamsiNihal
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
data science online training in hyderabadVamsiNihal
A 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. Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
Best data science training in HyderabadKumarNaik21
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Similar to A Practical-ish Introduction to Data Science (20)
Explaining the new Java release and licensing modelsMark West
A couple of years back, Oracle announced a new 6 month release cadence for Java and a paid subscription model for the Oracle JDK. These changes are now in force and there is a lot of confusion about what they actually mean.
Is Java still free to use? And if not, what alternatives are there to the Oracle JDK? In this talk I give a definitive answer to the above questions.
NOTE: INTERNAL TALK GIVEN AT BOUVET.
A common challenge of the IoT is adding AI capabilities to constrained devices. In this session, Mark will compare 2 solutions that he's tried out for his Raspberry Pi Zero Smart Camera:
(1) AWS Machine Learning as a Service
(2) Movidius Neural Compute Stick.
Make Data Smart Again 2018 - Building a Smart Security Camera with Raspberry ...Mark West
In this session I’ll share the story of how I transformed a simple Raspberry Pi Zero webcam into a Smart Security Camera (with motion detection, threat detection and alert notifications) by combining open source software with cloud based AI. Attendees can expect a demonstration of how I used a range of AWS API’s (including Rekognition, Lambda and Step Functions) to help my Smart Security Camera distinguish between an unwanted guest and the neighbours’ cat.
DevExperience 2018 : Building a Smart Security Camera with Raspberry Pi Zero,...Mark West
In this session, I’ll share how I transformed a lowly Raspberry Pi Zero W webcam into a smart security camera (with motion detection, threat analysis and alert notifications) by combining open source software with cloud based image analysis.
Attendees can expect a short explanation of how to set up their own motion activated webcam and a demonstration of how they can use Java and a range of AWS Services (including Rekognition, Lambda Functions and Step Functions) to help their camera distinguish between an unwanted guest and the neighbour’s cat.
GeeCON Prague : Building a Smart Security Camera with Raspberry Pi Zero, Java...Mark West
In this session, I’ll share how I transformed a lowly Raspberry Pi Zero W webcam into a smart security camera (with motion detection, threat analysis and alert notifications) by combining open source software with cloud based image analysis.
Attendees can expect a short explanation of how to set up their own motion activated webcam and a demonstration of how they can use Java and a range of AWS Services (including Rekognition, Lambda Functions and Step Functions) to help their camera distinguish between an unwanted guest and the neighbour’s cat.
Finally we’ll compare Node.js and Java versions of this solution and compare them in terms of execution speed, operating cost and ease of development.
JavaZone 2017 : Building a smart security camera with raspberry pi zero, java...Mark West
In this session, I’ll share how I transformed a lowly Raspberry Pi Zero W webcam into a smart security camera (with motion detection, threat analysis and alert notifications) by combining open source software with cloud based image analysis.
Attendees can expect a short explanation of how to set up their own motion activated webcam, followed by a demonstration of how they can use Java and a range of AWS Services (including Rekognition, Lambda Functions and Step Functions) to help their camera distinguish between an unwanted guest and the neighbour’s cat.
Finally we'll compare Node.js and Java versions of this solution and compare them in terms of execution speed, operating cost and ease of development.
Video here: https://vimeo.com/233849443
GeeCon 2017 : Building a Smart Security Camera with Raspberry Pi Zero, Node.j...Mark West
A key advantage of the IoT is that it enables you to expand the potential of constrained devices by giving them access to the computational power of The Cloud. In this session I’ll show you how I transformed a lowly Raspberry Pi Zero webcam into a smart security camera (with motion detection, threat detection and alert notifications) by combining open source software with cloud based image analysis. Attendees can expect an introduction on how to set up their own Raspberry Pi Zero webcam, a comparison of some of the image analysis API’s currently available, and finally a demonstration of how I used Node.js and a range of cloud based API’s (including AWS Rekognition, AWS Lambda and AWS Step Functions) to help my smart security camera distinguish between an unwanted guest and the neighbours cat.
Riga Dev Days: Building a Smart Security Camera with Raspberry Pi Zero, Node....Mark West
A key advantage of the IoT is that it enables you to expand the potential of constrained devices by giving them access to the computational power of The Cloud.
In this session I’ll show you how I transformed a lowly Raspberry Pi Zero webcam into a smart security camera (with motion detection, threat detection and alert notifications) by combining open source software with cloud based image analysis.
Attendees can expect a short introduction on how to set up their own Raspberry Pi Zero webcam, a comparison of some of the image analysis API’s currently available, and finally a demonstration of how I used Node.js and a range of cloud based API’s (including Amazon's Rekognition, Lambda and Step Functions) to help my smart security camera distinguish between an unwanted guest and the neighbours cat.
IoT Tech Day Smart Camera slides. Utrecht, April 2017.Mark West
A key advantage of the IoT is that it enables you to expand the potential of constrained devices by giving them access to the computational power of The Cloud.
In this session I’ll show you how I transformed a lowly Raspberry Pi Zero webcam into a smart security camera (with motion detection, threat detection and alert notifications) by combining open source software with cloud based image analysis. Attendees can expect a short explanation of how to set up their own Raspberry Pi Zero webcam, an introduction to Image Analysis as a Service, and finally a demonstration of how I used Node.js and a range of cloud based API’s (including Amazon’s Rekognition, Lambda and Step Function services) to help my smart security camera distinguish between an unwanted guest and the neighbours cat.
http://iottechday.nl/sessions/building-a-smart-security-camera-with-raspberry-pi-zero-node-js-and-the-cloud/
NTNU Tech Talks : Smartening up a Pi Zero Security Camera with Amazon Web Ser...Mark West
A key advantage of the IoT is that it enables you to expand the potential of constrained devices by giving them access to the computational power of The Cloud.
In this session I’ll show you how I transformed a lowly Raspberry Pi Zero webcam into a smart security camera (with motion detection, threat detection and alert notifications) by combining open source software with cloud based image analysis.
Attendees can expect a short introduction on how to set up their own Raspberry Pi Zero webcam, a comparison of some of the image analysis API’s currently available, and finally a demonstration of how I used Node.js and a range of cloud based API’s (including Amazon's Rekognition, Lambda and Step Functions) to help my smart security camera distinguish between an unwanted guest and the neighbour’s cat.
JavaZone 2016 : MQTT and CoAP for the Java DeveloperMark West
After HTTP, MQTT and CoAP are perhaps the most commonly used communication protocols for connecting devices to the Internet of Things. But what are MQTT and CoAP, and what benefits do they provide over plain old HTTP?
In this session we’ll start by looking at the limitations to using HTTP in the IoT world. We will then introduce MQTT and CoAP, and explain why these can be compelling replacements for HTTP. By examining the strengths and weaknesses for HTTP, MQTT and CoAP we’ll identify IoT use cases for all three.
JavaZone 2015 : NodeBots - JavaScript Powered Robots with Johnny-FiveMark West
Video of me giving this talk : https://2015.javazone.no/details.html?talk=65b51b83b9d6816ebde93e8bef1fae273ee411bc957fa291150d58f3077d58fe
Theres no denying that JavaScript is everywhere! But did you know that you could also use it to interact with a range of Sensors (i.e. distance, pressure and heat) and control Actuators (i.e. servos and LED's)? Attend this lightning talk to find out more about the NodeBots revolution!
In 10 fast paced minutes you'll learn about how you can get started with JavaScript powered Robots using an Arduino UNO and the Johnny-Five framework. If you are interested in picking up some JavaScript, Node.js, Arduino, Robotics or Electronics skills then this is the lightning talk for you!
There'll also be drones!
IoT Tech Day Coding Mojo slides. Utrecht, April 2016Mark West
http://www.iottechday.nl/sessions/how-i-rediscovered-my-coding-mojo-by-building-an-iotrobotics-prototype/
Includes links to video demos.
"Come hear the story of how I learned new technologies and rediscovered my coding mojo by building an IoT/robotics prototype: a JavaScript powered, voice-controlled robot! Along the way you can expect to learn about HTML5 speech recognition, controlling hardware with Node.js and Johnny-Five, using WebSockets and MQTT for communication between components, and finally how you can combine the Raspberry Pi and Arduino platforms to gain ultimate power over your own projects!"
JavaOne 2015 : How I Rediscovered My Coding Mojo by Building an IoT/Robotics ...Mark West
Is your project dragging you down? Are you stuck with the same old technologies? Are you bored with coding? If you answer “yes” to any of these questions, you may have lost your coding mojo—just like this session’s speaker did a few years back. Come hear how he learned new technologies and rediscovered his coding mojo by building an IoT/robotics prototype: a voice-controlled robot. Along the way, you’ll hear about HTML5 speech recognition, controlling hardware with Node.js and Johnny-Five, using WebSocket and MQTT for communication between components, and finally how you can easily combine the Raspberry Pi and Arduino platforms to gain ultimate power over your own projects.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
3. Who Am I?
• Previously Java Developer and Architect.
@markawest
4. Who Am I?
• Previously Java Developer and Architect.
• Currently building and managing a team of
Data Scientists at Bouvet Oslo.
@markawest
5. Who Am I?
• Previously Java Developer and Architect.
• Currently building and managing a team of
Data Scientists at Bouvet Oslo.
• Leader javaBin (Norwegian Java User Group).
@markawest
10. What is Data Science?
What is Data
Science?
Machine
Learning
Algorithms
Practical
Example
@markawest
11. @markawest
“Data Science… is an interdisciplinary
field of scientific methods, processes,
and systems to extract knowledge or
insight from data…”
Wikipedia
12. @markawest
“Data Science… is an interdisciplinary
field of scientific methods, processes,
and systems to extract knowledge or
insight from data…”
Wikipedia
17. @markawest
“Data Science… is an interdisciplinary
field of scientific methods, processes,
and systems to extract knowledge or
insight from data…”
Wikipedia
18. @markawest
1. Question 2. Data
3. Exploratory
Data Analysis
4. Formal
Modelling
5. Interperetation 6. Communication 7. Result
Data Science Process : Hypothesis Driven
19. @markawest
1. Question 2. Data
3. Exploratory
Data Analysis
4. Formal
Modelling
5. Interperetation 6. Communication 7. Result
Data Science Process : Hypothesis Driven
20. @markawest
1. Question 2. Data
3. Exploratory
Data Analysis
4. Formal
Modelling
5. Interperetation 6. Communication 7. Result
Data Science Process : Hypothesis Driven
21. @markawest
1. Question 2. Data
3. Exploratory
Data Analysis
4. Formal
Modelling
5. Interperetation 6. Communication 7. Result
Data Science Process : Hypothesis Driven
22. @markawest
1. Question 2. Data
3. Exploratory
Data Analysis
4. Formal
Modelling
5. Interpretation 6. Communication 7. Result
Data Science Process : Hypothesis Driven
23. @markawest
1. Question 2. Data
3. Exploratory
Data Analysis
4. Formal
Modelling
5. Interpretation 6. Communication 7. Result
Data Science Process : Hypothesis Driven
24. @markawest
1. Question 2. Data
3. Exploratory
Data Analysis
4. Formal
Modelling
5. Interpretation 6. Communication 7. Result
Data Science Process : Hypothesis Driven
25. @markawest
Roles Required in a Data Science Project
• Prove / disprove
hypotheses.
• Information and
Data Gathering.
• Data Wrangling.
• Algorithm and ML
models.
• Communication.
Data
Scientist
• Build Data Driven
Platforms.
• Operationalize
Algorithms and
Machine Learning
models.
• Data Integration.
Data
Engineer
• Storytelling.
• Build Dashboards
and other Data
visualizations.
• Provide insight
through visual
means.
Visualization
Expert
• Project
Management.
• Manage
stakeholder
expectations.
• Maintain a Vision.
• Facilitate.
Process
Owner
26. @markawest
Roles Required in a Data Science Project
• Prove / disprove
hypotheses.
• Information and
Data gathering.
• Data wrangling.
• Algorithm and ML
models.
• Communication.
Data
Scientist
• Build Data Driven
Platforms.
• Operationalize
Algorithms and
Machine Learning
models.
• Data Integration.
Data
Engineer
• Storytelling.
• Build Dashboards
and other Data
visualizations.
• Provide insight
through visual
means.
Visualization
Expert
• Project
Management.
• Manage
stakeholder
expectations.
• Maintain a Vision.
• Facilitate.
Process
Owner
27. @markawest
Roles Required in a Data Science Project
• Prove / disprove
hypotheses.
• Information and
Data gathering.
• Data wrangling.
• Algorithm and ML
models.
• Communication.
Data
Scientist
• Build Data Driven
Platforms.
• Operationalize
Algorithms and
Machine Learning
models.
• Data Integration.
• Monitoring.
Data
Engineer
• Storytelling.
• Build Dashboards
and other Data
visualizations.
• Provide insight
through visual
means.
Visualization
Expert
• Project
Management.
• Manage
stakeholder
expectations.
• Maintain a Vision.
• Facilitate.
Process
Owner
28. @markawest
Roles Required in a Data Science Project
• Prove / disprove
hypotheses.
• Information and
Data gathering.
• Data wrangling.
• Algorithm and ML
models.
• Communication.
Data
Scientist
• Build Data Driven
Platforms.
• Operationalize
Algorithms and
Machine Learning
models.
• Data Integration.
• Monitoring.
Data
Engineer
• Storytelling.
• Build Dashboards
and other Data
visualizations.
• Provide insight
through visual
means.
Data
Visualization
• Project
Management.
• Manage
stakeholder
expectations.
• Maintain a Vision.
• Facilitate.
Process
Owner
29. @markawest
Roles Required in a Data Science Project
• Prove / disprove
hypotheses.
• Information and
Data gathering.
• Data wrangling.
• Algorithm and ML
models.
• Communication.
Data
Scientist
• Build Data Driven
Platforms.
• Operationalize
Algorithms and
Machine Learning
models.
• Data Integration.
• Monitoring.
Data
Engineer
• Storytelling.
• Build Dashboards
and other Data
visualizations.
• Provide insight
through visual
means.
Data
Visualization
• Project
Management.
• Manage
stakeholder
expectations.
• Maintain a Vision.
• Facilitate.
• Evangelize.
Process
Owner
30. @markawest
“Data Science… is an interdisciplinary
field of scientific methods, processes,
and systems to extract knowledge or
insight from data…”
Wikipedia
31. Isn’t Data Science just
a rebranding of
Business Intelligence?
@markawest
32. @markawest
Data Science as an Evolution of BI
Business Intelligence Data Science Adds..
Data
Sources
Structured Data, most often
from Relational Database
Management Systems (RDBMS).
Unstructured Data (log files, audio,
images, emails, tweets, raw text,
documents).
Available
Tools
Data Visualization, Statistics. Machine Learning.
Goals Provide support to strategic
decision making, based on
historical data.
Provide business value through
advanced functionality.
Source: https://www.linkedin.com/pulse/data-science-business-intelligence-whats-difference-david-rostcheck
34. @markawest
Machine Learning: A Tool for Data Science
Artificial
Intelligence
Artificial Intelligence
Enabling computers to mimic human
intelligence and behavior.
35. @markawest
Machine Learning: A Tool for Data Science
Artificial
Intelligence
Machine
Learning
Artificial Intelligence
Enabling computers to mimic human
intelligence and behavior.
Machine Learning
Algorithms allowing computers to learn, make
predictions and describe data without being
explicitly programmed.
36. @markawest
Machine Learning: A Tool for Data Science
Artificial
Intelligence
Machine
Learning
Deep
Learning
Machine Learning
Algorithms allowing computers to learn, make
predictions and describe data without being
explicitly programmed.
Artificial Intelligence
Enabling computers to mimic human
intelligence and behavior.
Deep Learning
Black box learning with multi-layered Neural
Networks.
37. What is Data Science: Key Takeaways
• Data Scientists require Math and Statistics skills in addition to
traditional Software Development.
• Data Science is Hypothesis Driven.
• Data Science projects require a range of competencies/roles.
• Data Science can be seen as an evolution of Business Intelligence,
providing additional capabilities through the application of cutting
edge technologies and unstructured data.
@markawest
39. @markawest
“Machine Learning:
Field of study that gives
computers the ability to
learn without being
explicitly programmed.”
Arthur L. Samuel
IBM Journal of Research and Development, 1959
Computer
Data
Rules
Output
Computer
Data
Output
Rules
Traditional Programming
Machine Learning
40. Generalized
Captures the correlations in
your training data. May have
an error margin.
The Art of The Generalized Model
@markawest
Underfitted Overfitted
Model memorizes the
training data rather than
finding underlying patterns.
Model overlooks underlying
patterns in your training
data.
41. Supervised Learning
Machine Learning Types
@markawest
Unsupervised Learning
Model trained on historical
data. Resulting model can be
used to make predictions on
new data.
Use Case: Predicting a value
based on patterns discovered
in previous data.
Algorithm finds trends and
patterns in data, without
prior training on historical
data.
Use Case: Describing your
data based on statistical
analysis.
Reinforcement Learning
Model uses a feedback loop
to iteratively improve it’s
performance.
Use Case: Learning how to
best solve a problem based
on trial and error.
48. Fitting a trend line: Ordinary Least Squares
@markawest
a
b
c
d
e
f
a2 + b2 + c2 + d2 + e2 + f2 = sum of squared error
Outlier?
49. Linear Regression Notes
Benefits
• Simple to
understand.
• Transparent.
Limitations
• Outliers skew
trend line.
• Doesn’t work
with non-
linear
relationships.
Some
Alternatives
• Non-linear
Least Squares.
• Tree
algorithms.
@markawest
50. Example Machine Learning Algorithms
@markawest
Supervised Learning Unsupervised Learning
Linear
Regression
ClassificationRegression
K-Means
Clustering
Decision Trees
51. Decision Tree: Calculating the Best Split
@markawest
Name Placements Complaints Lived in Norway Payrise
Don Yes Yes Yes Yes
Lewis Yes Yes No Yes
Mike Yes No Yes Yes
Danny Yes Yes No Yes
Dan No No Yes No
Elliot Yes No No Yes
Luke Yes No No Yes
Tom Yes Yes No Yes
Nathan No Yes Yes No
Owen Yes No No Yes
Goal: Build a
Decision Tree for
deciding who gets a
payrise this year,
based on historical
payrise data.
Features Labels
52. Decision Tree: Calculating the Best Split
@markawest
Name Placements Complaints Lived in Norway Payrise
Don Yes Yes Yes Yes
Lewis Yes Yes No Yes
Mike Yes No Yes Yes
Danny Yes Yes No Yes
Dan No No Yes No
Elliot Yes No No Yes
Luke Yes No No Yes
Tom Yes Yes No Yes
Nathan No Yes Yes No
Owen Yes No No Yes
Lived in
Norway
Yes No
53. Decision Tree: Calculating the Best Split
@markawest
Name Placements Complaints Lived in Norway Payrise
Don Yes Yes Yes Yes
Lewis Yes Yes No Yes
Mike Yes No Yes Yes
Danny Yes Yes No Yes
Dan No No Yes No
Elliot Yes No No Yes
Luke Yes No No Yes
Tom Yes Yes No Yes
Nathan No Yes Yes No
Owen Yes No No Yes
Complaints
Yes No
54. Decision Tree: Calculating the Best Split
@markawest
Name Placements Complaints Lived in Norway Payrise
Don Yes Yes Yes Yes
Lewis Yes Yes No Yes
Mike Yes No Yes Yes
Danny Yes Yes No Yes
Dan No No Yes No
Elliot Yes No No Yes
Luke Yes No No Yes
Tom Yes Yes No Yes
Nathan No Yes Yes No
Owen Yes No No Yes
Placements
Yes No
55. Decision Tree: Calculating the Best Split
@markawest
Placements
Yes No
Complaints
Yes No
Lived in
Norway
Yes No
Recruiters Placements Complaints Lived in Norway Payrise
8 8 4 2 Yes
2 0 1 2 No
56. Building a Decision Tree: A Bad Split?
@markawest
Placements
Yes No
Complaints
Yes No
Lived in
Norway
Yes No
Recruiters Placements Complaints Lived in Norway Payrise
8 7 8 2 Yes
2 1 0 2 No
57. Decision Tree: Recursive Partitioning
@markawest
Outlook Temp Humidity Wind Play
Sunny Hot High Weak No
Sunny Hot High Strong No
Overcast Hot High Weak Yes
… … … … …
… … … … …
Overcast Mild High Strong Yes
Overcast Hot Normal Weak Yes
Rain Mild High Strong No
No Yes No Yes
Yes
Outlook
Humidity Wind
Features Labels
Overcast
Sunny Rain
High WeakNormal Strong
58. Building a Decision Tree: Where to Stop?
@markawest
#1 : All Data at
current leaf
belongs to the
same class.
No Yes No Yes
YesHumidity Wind
Overcast
Sunny Rain
High Normal Strong
Outlook
59. Building a Decision Tree: Where to Stop?
@markawest
No Yes No Yes
YesHumidity Wind
Overcast
Sunny Rain
High Normal Strong
Outlook
#2 : Maximum tree
depth reached.
60. Decision Tree Notes
Benefits
• White Box.
• Flexible (use for
both regression
and classification).
• Robust to outliers.
• Handle non-linear
boundaries.
Limitations
• Susceptible to
overfitting.
• Changes to where
the Data is sliced
can produce
different results.
Some Alternatives
• Support Vector
Machine.
• Logistic
Regression.
• Random Forests.
@markawest
61. Example Machine Learning Algorithms
@markawest
Supervised Learning Unsupervised Learning
Linear
Regression
ClassificationRegression
K-Means
Clustering
Decision Trees
62. K-Means Clustering
@markawest
• K = The amount of clusters the
algorithm will try to find.
• K = Should be large enough to
extract meaningful patterns but
small enough that clusters remain
clearly distinct.
• So how do we calculate K?
63. Sum of Squared Errors
@markawest
a b
c
de
f
a2 + b2 + c2 + d2 + e2 + f2 = sum of squared error
a
b
c
d
e
f
67. K-Means: Calculating the K value
@markawest
• Scree Plots allow us to find
optimal number of clusters.
• Shows the Sum of Squared
Errors for different
numbers of clusters.
• The optimal K value is at
the “Elbow” of the plot.
74. K-Means Demo
After 6 iterations: Clusters and centroids stablise, algorithm stops
@markawest
75. K-Means Clustering Notes
Benefits
• Fast and highly
effective at
uncovering basic
data patterns.
• Works best for
spherical, non-
overlapping
clusters.
Limitations
• Each data point
can only be
assigned to one
cluster.
• Clusters are
assumed to be
spherical.
Some Alternatives
• Gaussian mixtures.
• Fuzzy K-Means.
@markawest
76. Machine Learning Algorithms: Key Takeaways
@markawest
• The three main types of Machine Learning are Supervised,
Unsupervised and Reinforcement Learning.
• Machine Learning is more than Neural Networks and Deep Learning.
• A successful Machine Learning Model needs to find the balance
between Overfitting and Underfitting.
• Machine Learning Algorithms are merely tools. Good results come
from understanding how they work and tuning them correctly.
78. Use Case: Titanic Passenger Survival
@markawest
Goal: Build a
classification model
for predicting
Titanic survivability.
79. Hypothesis
That it is possible
to predict Titanic
survivability based
on Age, Gender
and Ticket Class.
@markawest
80. @markawest
Variable Description
PassengerId Unique Identifier
Survival Survived = 1, Died = 0
Pclass Ticket class (1, 2 or 3)
Sex Gender (‘male’ or ’female’)
Age Age in years
Sibsp Number siblings / spouses aboard the Titanic
Parch Number parents / children aboard the Titanic
Ticket Ticket number
Fare Passenger fare
Cabin Cabin number
Embarked Port of Embarkation
Name Passenger name, including honorific.
Titanic
Dataset
83. Practical Example: Key Takeaways
@markawest
• Scikit-learn and Jupyter Notebooks provide a free and flexible basis for starting
with Data Science. Use the Anaconda distribution to save time on installation!
• Feature Engineering is a vital skill for Data Scientists.
• Domain Knowledge is key to succeed!
• Split your data into Test and Training sets.
• Tweaking Hyperparameters may give better results (but you should be able to
explain how your tweak improved model performance).
84. Tips for Getting Started with Data Science
@markawest
• Become a Data Engineer!
• Learn Python or R (SQL is also useful)!
• Learn some statistical methods!
• Take an online Data Science course (i.e. Udemy DS Nano Degree)!
• Understand the Data Science process!
• Join a Meetup!
• Practice with Kaggle!
But first, who the devil am I? As you can see from my twitter handle my name is Mark West, and I’m an English living here in Oslo, Norway.
Speaking for me is a hobby that I do to learn and share my own knowledge and experiences. In the past couple of years I have spoken at a range of conference across Europe and the US. The good news is that this is the first time I have spoken at NDC. This is also the first time I have given this specific talk so I am excited to hear your feedback.
So lets get started!
Speaking for me is a hobby that I do to learn and share my own knowledge and experiences. In the past couple of years I have spoken at a range of conference across Europe and the US. The good news is that this is the first time I have spoken at NDC. This is also the first time I have given this specific talk so I am excited to hear your feedback.
So lets get started!
Speaking for me is a hobby that I do to learn and share my own knowledge and experiences. In the past couple of years I have spoken at a range of conference across Europe and the US. The good news is that this is the first time I have spoken at NDC. This is also the first time I have given this specific talk so I am excited to hear your feedback.
So lets get started!
Here is the Agenda for my talk. As you can see it is split into four sections.
I’ll then do on to define what Data Science is, what parts are most relevant for us, and out Data Science is linked with Machine Learning and Aritifical Intelligence. I’ll also talk about the drivers behind Data Science projects that the roles that these projects require.
Machine Learning is the most popular application of Data Science at the moment, and I’ll therefore use some time to define the categories and types of Machine Learning algorithms, and give you some examples of the most commonly used algorithms.
Finally I will show you a practical example of applied Data Science, and show you how Data Science is more than just Machine Learning.
Right, so whats the motivation. Why am I here today?
Tip : Possibly replace this with Bouvet’s own methodology if it is ready and good enough.
Ok, so lets move on to the second part of my talk – Machine Learning algorithms.
Machine Learning is all about giving computers a framework to create their own logic or rules, without these being programmed by a human. Look at it as an inversion of control when compared to traditional programming.
An underfitted model is likely to neglect significant trends, which would cause it to yield less accurate predictions for both current and future data.
An overfitted model would yield highly accurate predictions for the current data, but would be less generalizable to future data.
But when parameters are tuned just right, such as shown in Figure 2b, the algorithm strikes a balance between identifying major trends and discounting minor variations, rendering the resulting model well-suited for making predictions.
Note – more complex models are prone to overfitting.
Note that reinforcement learning continuously improves itself, which supervised and unsupervised models will have to be built again to reflect new data. So if your use case requires you to
Other forms of Regression Model that are popular include Non-Regression, which is used for modelling non-linear trend lines, and Logistic Regression, which is a form of Regression where the trend line is used to separate data points into classes.
Multicollinearity
You go to see a rock and roll band with two great guitar players. You're eager to see which one plays best. But on stage, they're both playing furious leads at the same time! When they're both playing loud and fast, how can you tell which guitarist has the biggest effect on the sound? Even though they aren't playing the same notes, what they're doing is so similar it's difficult to tell one from the other.
But first, who the devil am I? As you can see from my twitter handle my name is Mark West, and I’m an English living here in Oslo, Norway.
But first, who the devil am I? As you can see from my twitter handle my name is Mark West, and I’m an English living here in Oslo, Norway.
But first, who the devil am I? As you can see from my twitter handle my name is Mark West, and I’m an English living here in Oslo, Norway.
But first, who the devil am I? As you can see from my twitter handle my name is Mark West, and I’m an English living here in Oslo, Norway.
But first, who the devil am I? As you can see from my twitter handle my name is Mark West, and I’m an English living here in Oslo, Norway.
But first, who the devil am I? As you can see from my twitter handle my name is Mark West, and I’m an English living here in Oslo, Norway.
As decision trees are grown by splitting data points into homogeneous groups, a slight change in the data could trigger changes to the split, and result in a different tree.
Why Random Forests
As decision trees also aim for the best way to split data points each time, they are vulnerable to overfitting (see Chapter 1.3). Inaccuracy. Using the best binary question to split the data at the start might not lead to the most accurate predictions.
Sometimes, less effective splits used initially may lead to better predictions subsequently.
More Data beats complex algorithms : It’s all about the DATA!!!!
Garbage in, Garbage out!!
Right, so whats the motivation. Why am I here today?
survival – Did the passenger survive?
pclass – Which
sex
age
sibsp
parch
ticket
Fare
cabin
embarked