Here's a starting template for anyone presenting data science topic to elementary school students. Exhibits how fun the field is and how the job market for these skills is excellent. Includes hyperlinks to various examples of interesting interactive visualizations.
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
Data science is the new thing! How to be a data scientist? See here.
This was originally was written by the team behind DataCamp, - the online interactive learning platform for data science!
Una breve introduzione alla data science e al machine learning con un'enfasi sugli scenari applicativi, da quelli tradizionali a quelli più innovativi. La overview copre la definizione di base di data science, una overview del machine learning e esempi su scenari tradizionali, Recommender systems e Social Network Analysis, IoT e Deep Learning
Presentation at Data ScienceTech Institute campuses, Paris and Nice, May 2016 , including Intro, Data Science History and Terms; 10 Real-World Data Science Lessons; Data Science Now: Polls & Trends; Data Science Roles; Data Science Job Trends; and Data Science Future
Data Science is a form of science that focuses on dealing with huge chunks of data by using modern data analysis tools and techniques to discover hidden patterns, meaningful insights, and make critical business decisions.
A Data Science professional has to utilize complicated machine learning algorithms to develop predictive models. There could be multiple sources present in different formats used in data analysis.
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
Slide 2: Etymology: The etymology of the term ‘Big Data’ can be traced back to the mid-1990s, when it was first used by John Mashey to refer to handling and analysis of massive datasets. However, by 2013, ‘Big Data’ was already being declared obsolescent as a meaningful term by some, as it was too wide ranging and vague in definition (e.g. de Goes, 2013).
Side 6: Vagaries: Kitchin argues that it is velocity and these additional key characteristics that set Big Data apart and make them a “disruptive innovation – one that radically changes the nature of data and what can be done with them” (Kitchin, 2014). However, there is no one characteristic profile that all Big Data fit and they can take multiple forms.
Slide 8: Ethics: Several ethical questions have been raised about the scope of data being generated and retained; such as those concerning privacy, informed consent, and protection from harm.
These questions raise wider issues about what kinds of data should be combined and analysed, and the purposes to which the resulting information should be put.
Slide 9: Inequalities: Challenges of inequality have also been posed:
Whose data traces will be analysed? It is likely that only those who are better off will be represented (as they are more likely to use social media, etc.)
Access and use of open data is unlikely to be equally available to everyone due to existing structural inequalities (Eynon, 2013)
Slide 11: What do Big Data actually tell us? Eynon (2013) argues that Big Data is concerned with capturing and examining patterns, and tells us more about what people actually do than about what they say they do. However, this is not sufficient for all kinds of social science research. We need to understand the meanings of behaviours which cannot be inferred simply from tracking specific patterns.
In order that Big Data are used appropriately, we need to ensure understanding of what kinds of research can or cannot be carried out using them. Big Data should not be seen as [a] “technical fix” for research, but should be used to empower, support and facilitate practice and critical research.
Κnowledge Architecture: Combining Strategy, Data Science and Information Arch...Connected Data World
"The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker", said Peter F. Drucker in 1999, and time has proven him right.
Even NASA is no exception, as it faces a number of challenges. NASA has hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, IT logs, and all kinds of other data stored nation-wide.
The data is growing in terms of variety, velocity, volume, value and veracity. NASA needs to provide accessibility to engineering data sources, whose visibility is currently limited. To convert data to knowledge a convergence of Knowledge Management, Information Architecture and Data Science is necessary.
This is what David Meza, Acting Branch Chief - People Analytics, Sr. Data Scientist at NASA, calls "Knowledge Architecture": the people, processes, and technology of designing, implementing, and applying the intellectual infrastructure of organizations.
HOW TO BECOME AN EFFECTIVE DATA SCIENTIST (WORKSHOP) - MARC WARNERBig Data Week
Marc is the CEO and co-founder of ASI, a leading Data Science consultancy and training company in London. He is an Associate of the Physics Department at Harvard University, having recently been a Research Fellow based there. He has a PhD in Quantum Computing from UCL, where he is a Visiting Researcher at the London Centre for Nanotechnology. He has consulted for a range of organisations and companies, including The Houses of Parliament, The NHS, The BBC and many start-ups. His work has been published in the highest profile scientific journals, including Nature, and in the New York Times, Wired and many others.
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
Here's a starting template for anyone presenting data science topic to elementary school students. Exhibits how fun the field is and how the job market for these skills is excellent. Includes hyperlinks to various examples of interesting interactive visualizations.
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
Data science is the new thing! How to be a data scientist? See here.
This was originally was written by the team behind DataCamp, - the online interactive learning platform for data science!
Una breve introduzione alla data science e al machine learning con un'enfasi sugli scenari applicativi, da quelli tradizionali a quelli più innovativi. La overview copre la definizione di base di data science, una overview del machine learning e esempi su scenari tradizionali, Recommender systems e Social Network Analysis, IoT e Deep Learning
Presentation at Data ScienceTech Institute campuses, Paris and Nice, May 2016 , including Intro, Data Science History and Terms; 10 Real-World Data Science Lessons; Data Science Now: Polls & Trends; Data Science Roles; Data Science Job Trends; and Data Science Future
Data Science is a form of science that focuses on dealing with huge chunks of data by using modern data analysis tools and techniques to discover hidden patterns, meaningful insights, and make critical business decisions.
A Data Science professional has to utilize complicated machine learning algorithms to develop predictive models. There could be multiple sources present in different formats used in data analysis.
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
Slide 2: Etymology: The etymology of the term ‘Big Data’ can be traced back to the mid-1990s, when it was first used by John Mashey to refer to handling and analysis of massive datasets. However, by 2013, ‘Big Data’ was already being declared obsolescent as a meaningful term by some, as it was too wide ranging and vague in definition (e.g. de Goes, 2013).
Side 6: Vagaries: Kitchin argues that it is velocity and these additional key characteristics that set Big Data apart and make them a “disruptive innovation – one that radically changes the nature of data and what can be done with them” (Kitchin, 2014). However, there is no one characteristic profile that all Big Data fit and they can take multiple forms.
Slide 8: Ethics: Several ethical questions have been raised about the scope of data being generated and retained; such as those concerning privacy, informed consent, and protection from harm.
These questions raise wider issues about what kinds of data should be combined and analysed, and the purposes to which the resulting information should be put.
Slide 9: Inequalities: Challenges of inequality have also been posed:
Whose data traces will be analysed? It is likely that only those who are better off will be represented (as they are more likely to use social media, etc.)
Access and use of open data is unlikely to be equally available to everyone due to existing structural inequalities (Eynon, 2013)
Slide 11: What do Big Data actually tell us? Eynon (2013) argues that Big Data is concerned with capturing and examining patterns, and tells us more about what people actually do than about what they say they do. However, this is not sufficient for all kinds of social science research. We need to understand the meanings of behaviours which cannot be inferred simply from tracking specific patterns.
In order that Big Data are used appropriately, we need to ensure understanding of what kinds of research can or cannot be carried out using them. Big Data should not be seen as [a] “technical fix” for research, but should be used to empower, support and facilitate practice and critical research.
Κnowledge Architecture: Combining Strategy, Data Science and Information Arch...Connected Data World
"The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker", said Peter F. Drucker in 1999, and time has proven him right.
Even NASA is no exception, as it faces a number of challenges. NASA has hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, IT logs, and all kinds of other data stored nation-wide.
The data is growing in terms of variety, velocity, volume, value and veracity. NASA needs to provide accessibility to engineering data sources, whose visibility is currently limited. To convert data to knowledge a convergence of Knowledge Management, Information Architecture and Data Science is necessary.
This is what David Meza, Acting Branch Chief - People Analytics, Sr. Data Scientist at NASA, calls "Knowledge Architecture": the people, processes, and technology of designing, implementing, and applying the intellectual infrastructure of organizations.
HOW TO BECOME AN EFFECTIVE DATA SCIENTIST (WORKSHOP) - MARC WARNERBig Data Week
Marc is the CEO and co-founder of ASI, a leading Data Science consultancy and training company in London. He is an Associate of the Physics Department at Harvard University, having recently been a Research Fellow based there. He has a PhD in Quantum Computing from UCL, where he is a Visiting Researcher at the London Centre for Nanotechnology. He has consulted for a range of organisations and companies, including The Houses of Parliament, The NHS, The BBC and many start-ups. His work has been published in the highest profile scientific journals, including Nature, and in the New York Times, Wired and many others.
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
How to crack Big Data and Data Science rolesUpXAcademy
How to crack Big Data and Data Science roles is the flagship event of UpX Academy. This slide was used for the event on 10th Sept that was attended by hundreds of participants globally.
This presentation is prepared by one of our renowned tutor "Suraj"
If you are interested to learn more about Big Data, Hadoop, data Science then join our free Introduction class on 14 Jan at 11 AM GMT. To register your interest email us at info@uplatz.com
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
Privacy, Ethics, and Future Uses of the Social WebMatthew Russell
A presentation to the Owen Graduate School of Management (Vanderbilt University) about social media and some of the technology behind the future uses of social media that are likely to shape the future of the Web as we know it.
These are slides from Ellen Wagner\'s featured theme presentation Making Learning Analytics Matter in the Educational Enterprise from Blackboard World 2012, New Orleasn, LA, July 12, 2012
Similar to Getting started in data science (4:3) (20)
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
2. About me
• Jasjit Singh
• Worked in finance & tech
• Co-Founder Hotspot
• Thinkful General Manager
3. About us
Thinkful prepares students for web development &
data science jobs with 1-on-1 mentorship programs
4. About you
•I already have a career in data
•I’m serious about switching into a career in data
•I’m curious about switching into a career in data
•Ugh I just want to see what all the fuss is about
•Data is my favorite character in Star Trek
5. Today’s goals
•What is a data scientist and what do they do?
•How and why has the field emerged?
•How can one become a data scientist?
6. Agenda for tonight
• What is the market landscape for dev jobs?
• What programming language should I learn?
• What are the best ways to learn to code?
• What are the first jobs / trajectories?
• How do I break into the field?
7. Why do we care?
“The United States alone faces a shortage of 140,000 to
190,000 people with deep analytical skills as well as 1.5
million managers and analysts to analyze big data and
make decisions based on their findings.”
- McKinsey
12. Case study: LinkedIn (2006)
“[LinkedIn] was like arriving at a conference reception
and realizing you don’t know anyone. So you just stand in
the corner sipping your drink—and you probably leave
early.”
-LinkedIn Manager, June 2006
13. The new guy
Jonathan Goldman
•Joined LinkedIn in 2006, only
8M users (450M in 2016)
•Started experiments to
predict people’s networks
•Engineers were dismissive:
“you can already import your
address book”
15. Other examples
•Uber — Where drivers should hang out
•Netflix — $1M movie recommendations contest
•Ebola — Mobile mapping in Senegal to fight disease
16. “Big Data” changed the game
Big Data: datasets whose size is beyond the ability of
typical database software tools to capture, store,
manage, and analyze
17. Brief history of “Big Data”
•Trend “started” in 2005 (Hadoop!)
•Web 2.0 - Majority of content is created by users
•Mobile accelerates this — data/person skyrockets
25. Frame the question
•What connections (type and number) lead to higher user
engagement?
•Which connections do people want to make but are
currently limited from making?
•How might we predict these types of connections with
limited data from the user?
27. Collect the data
•Connection data (who is who connected to?)
•Demographic data (what is profile of connection)
•Retention data (how do people stay or leave)
•Engagement data (how do they use the site)
33. Communicating the findings
•Tell story at the right technical level for each audience
•Make sure to focus on Whats In It For You (WIIFY!)
•Be objective, don’t lie with statistics
•Be visual! Show, don’t just tell
39. Tool #3: Machine learning algorithms
Machine learning algorithms provide computers with
the ability to learn without being explicitly
programmed — “programming by example”
44. This is what you’ll need
•Knowledge of statistics, algorithms, & software
•Comfort with languages & tools (Python, SQL, Tableau)
•Inquisitiveness and intellectual curiosity
•Strong communication skills
45. Data science bootcamp
Syllabus: Python Toolkit, Statistics & Probability,
Experimentation, Machine Learning, Communicating
Data, Algorithms and Big Data
46. More about Thinkful
• Anyone who’s committed can learn to code
• 1-on-1 mentorship is the best way to learn
• Flexibility matters — learn anywhere, anytime
• We only make money when you get a job
47. Our Program
You’ll learn concepts, practice with drills, and build capstone projects
for your own portfolio — all guided by a personal mentor
50. Special Prep Course Offer
• Three-week program, includes six mentor sessions for $250
• Overview of Python, Python’s data science toolkit, stats
• Option to continue into full data science bootcamp
• Talk to me (or email me) if you’re interested
