Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
Data Scientist has been regarded as the sexiest job of the twenty first century. As data in every industry keeps growing the need to organize, explore, analyze, predict and summarize is insatiable. Data Science is creating new paradigms in data driven business decisions. As the field is emerging out of its infancy a wide range of skill sets are becoming an integral part of being a Data Scientist. In this talk I will discuss the different driven roles and the expertise required to be successful in them. I will highlight some of the unique challenges and rewards of working in a young and dynamic field.
This is a presentation that I presented in the talk of "Woman in Data science" in Turin, 2018. This is a guide to help beginners to start their journey in Data science, it provided suggestion where to start, what to study, what are the best online & off-line resource & materials and how to put all the theory in practice. Enjoy your journey!
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
The world’s most valuable resource is no longer oil, but data. Data does not have any meaning unless we study it and make inference out of it or draw insights from it.
#data ,#data analytics ,#ai ,#algorithms ,#bigdata ,#ml ,#machine learning ,#artificial intelligence
Effectiveness of Data Analytics and Big Data in United States Presidential Elections, Polls, Voting and Campaigns. U.S. presidential elections are the most talked about topic now a days. Who will win race? Donald Trump or Hillary Clinton ? This presentation gives an insight on how people can utilize the data analytics approaches to achieve specific goals and get insight to the target users.
Employees, Business Partners and Bad Guys: What Web Data Reveals About Person...Connotate
This presentation will discuss how to collect Web data with precision, transform it and then apply next-generation text analytics to reveal insights about the past activities of persons of interest and/or predict future outcomes. Featured guest speaker Claire Schmidt will discuss results of a project which proved the potential of using automated Web data collection and advanced analytics to identify potential child victims of exploitation.
What does it_takes_to_be_a_good_data_scientist_2019_aim_simplilearnPraj H
Over the years, the term ‘data scientist’ has evolved greatly. From describing a person who handles data, to a professional who leverages machine learning — this definition has seen a great deal of change. Now, circa 2019, there are numerous blogs, Reddit pages and Quora threads dedicated to the discussion about “how to become a good data scientist”.
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 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
Panel Discussion – Grooming Data Scientists for Today and for TomorrowHPCC Systems
In this session, we will explore the talent gap for data scientists including the potential causes and what academia and the private sector are doing to develop the necessary talent. Will the skills which are in such explosive demand today still be in demand in the future? This panel of professors and practitioners will engage in a conversation about the talent issues facing companies across the country and around the world and what they are doing about it.
Advances in technology for capturing information have led to the promise of “Big Data” to dramatically alter the business environment. However, technology is only an enabler of aggregation and analysis. Many firms struggle to convert information to business knowledge and insights. Learn how organizations are using data to improve skill development at all levels and developing models for organizational structures to link these skills to executive decision-making.
Speakers: Dan McGurrin, Ph.D., NC State and Pamela Webber, Cisco
Machine learning is growing very rapidly day by day. We are using machine learning in our daily life even without knowing it such as Google Maps, Google assistant, Alexa, etc.
How semantic search changes recruitment - Glen CatheyTextkernel
Presentation by Glen Cathey, SVP Talent Strategy and Innovation at Kforce, at Textkernel's conference Intelligent Machines and the Future of Recruitment on 2 June at the Beurs van Berlage in Amsterdam. At the end of this slide deck, you can also find the YouTube recording.
Without semantic search, recruiters searching for potential candidates only see a fraction of available and relevant results and unknowingly exclude qualified candidates unless they understand and employ advanced methods of manual information retrieval. In this keynote, Glen Cathey will explain how semantic search has specifically impacted recruitment today and how further advancements will impact recruitment in the future.
Data Scientist has been regarded as the sexiest job of the twenty first century. As data in every industry keeps growing the need to organize, explore, analyze, predict and summarize is insatiable. Data Science is creating new paradigms in data driven business decisions. As the field is emerging out of its infancy a wide range of skill sets are becoming an integral part of being a Data Scientist. In this talk I will discuss the different driven roles and the expertise required to be successful in them. I will highlight some of the unique challenges and rewards of working in a young and dynamic field.
This is a presentation that I presented in the talk of "Woman in Data science" in Turin, 2018. This is a guide to help beginners to start their journey in Data science, it provided suggestion where to start, what to study, what are the best online & off-line resource & materials and how to put all the theory in practice. Enjoy your journey!
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
The world’s most valuable resource is no longer oil, but data. Data does not have any meaning unless we study it and make inference out of it or draw insights from it.
#data ,#data analytics ,#ai ,#algorithms ,#bigdata ,#ml ,#machine learning ,#artificial intelligence
Effectiveness of Data Analytics and Big Data in United States Presidential Elections, Polls, Voting and Campaigns. U.S. presidential elections are the most talked about topic now a days. Who will win race? Donald Trump or Hillary Clinton ? This presentation gives an insight on how people can utilize the data analytics approaches to achieve specific goals and get insight to the target users.
Employees, Business Partners and Bad Guys: What Web Data Reveals About Person...Connotate
This presentation will discuss how to collect Web data with precision, transform it and then apply next-generation text analytics to reveal insights about the past activities of persons of interest and/or predict future outcomes. Featured guest speaker Claire Schmidt will discuss results of a project which proved the potential of using automated Web data collection and advanced analytics to identify potential child victims of exploitation.
What does it_takes_to_be_a_good_data_scientist_2019_aim_simplilearnPraj H
Over the years, the term ‘data scientist’ has evolved greatly. From describing a person who handles data, to a professional who leverages machine learning — this definition has seen a great deal of change. Now, circa 2019, there are numerous blogs, Reddit pages and Quora threads dedicated to the discussion about “how to become a good data scientist”.
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 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
Panel Discussion – Grooming Data Scientists for Today and for TomorrowHPCC Systems
In this session, we will explore the talent gap for data scientists including the potential causes and what academia and the private sector are doing to develop the necessary talent. Will the skills which are in such explosive demand today still be in demand in the future? This panel of professors and practitioners will engage in a conversation about the talent issues facing companies across the country and around the world and what they are doing about it.
Advances in technology for capturing information have led to the promise of “Big Data” to dramatically alter the business environment. However, technology is only an enabler of aggregation and analysis. Many firms struggle to convert information to business knowledge and insights. Learn how organizations are using data to improve skill development at all levels and developing models for organizational structures to link these skills to executive decision-making.
Speakers: Dan McGurrin, Ph.D., NC State and Pamela Webber, Cisco
Machine learning is growing very rapidly day by day. We are using machine learning in our daily life even without knowing it such as Google Maps, Google assistant, Alexa, etc.
How semantic search changes recruitment - Glen CatheyTextkernel
Presentation by Glen Cathey, SVP Talent Strategy and Innovation at Kforce, at Textkernel's conference Intelligent Machines and the Future of Recruitment on 2 June at the Beurs van Berlage in Amsterdam. At the end of this slide deck, you can also find the YouTube recording.
Without semantic search, recruiters searching for potential candidates only see a fraction of available and relevant results and unknowingly exclude qualified candidates unless they understand and employ advanced methods of manual information retrieval. In this keynote, Glen Cathey will explain how semantic search has specifically impacted recruitment today and how further advancements will impact recruitment in the future.
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.
Data Science is in high demand, the melting pot
of complex skills requires a qualified data scientist have made them the unicorns in today's data-driven landscape.
Understanding Data Science: Unveiling the Basics
What is Data Science?
Data science is an interdisciplinary field that combines techniques from statistics, mathematics, computer science, and domain knowledge to extract insights and knowledge from data. It involves collecting, processing, analyzing, and interpreting large and complex datasets to solve real-world problems.
Importance of Data Science
In today's data-driven world, organizations are inundated with data from various sources. Data science allows them to convert this raw data into actionable insights, enabling informed decision-making, improved efficiency, and innovation.
Intersection of Data Science, Statistics, and Computer Science
Data science borrows heavily from statistics and computer science. Statistical methods help in understanding data patterns, while computer science provides the tools to process and analyze large datasets efficiently.
Key Components of Data Science
Data Collection and Storage
The first step in data science is gathering relevant data from various sources. This data is then stored in databases or data warehouses for further processing.
Data Cleaning and Preprocessing
Raw data is often messy and inconsistent. Data cleaning involves removing errors, duplicates, and irrelevant information. Preprocessing includes transforming data into a usable format.
Exploratory Data Analysis (EDA)
EDA involves visualizing and summarizing data to uncover patterns, trends, and anomalies. It helps in forming hypotheses and guiding further analysis.
Machine Learning and Predictive Modeling
Machine learning algorithms are used to build predictive models from data. These models can make predictions and decisions based on new, unseen data.
Data Visualization
Visual representations of data, such as graphs and charts, help in understanding complex information quickly. Data visualization aids in conveying insights effectively.
The Data Science Process
Problem Definition
The data science process begins with understanding the problem you want to solve and defining clear objectives.
Data Collection and Understanding
Collect relevant data and understand its context. This step is crucial as the quality of the analysis depends on the quality of the data.
Data Preparation
Clean, preprocess, and transform the data into a suitable format for analysis. This step ensures that the data is accurate and ready for modeling.
Model Building
Select appropriate algorithms and build predictive models using machine learning techniques. This step involves training and fine-tuning the models.
Model Evaluation and Deployment
Evaluate the model's performance using metrics and test datasets. If the model performs well, deploy it for making predictions on new data.
Technologies Driving Data Science
Programming Languages
Languages like Python and R are widely used in data science due to their extensive libraries and versatility.
Machine Learning Libraries
Libraries like Scikit-Learn and TensorFlow prov
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
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/
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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.
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
3. About you
You already have a career in data
I'm interested in switching into a data career
I just want to see what all the fuss is about
4. About Thinkful
Thinkful helps people become developers or data
scientists through 1-on-1 mentorship and project-based
learning
These workshops are built using this approach.
5. Today's Goals
What is Data Science?
How and why has the field emerged?
What do they do?
Next steps
6.
7.
8.
9. Example: 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
10. Enter: Data Scientist
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”
12. Other Examples
Uber — Where drivers should hang out
Tala — Microfinance loan approval
13. Why now?
Big Data: datasets whose size is
beyond the ability of typical database
software tools to capture, store,
manage, and analyze
14. Brief history of "big data"
Trend "started" in 2005
Web 2.0 - Majority of content is created
by users
Mobile accelerates this — data/person
skyrockets
15. Big Data
90% of the data in the world
today has been created in the
last two years alone
- IBM, May 2013
19. Data Science is just the beginning
“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
20. The Process - LinkedIn Example
Frame the question
Collect the raw data
Process the data
Explore the data
Communicate results
21. Case: Frame the Question
What questions do we want to answer?
22. Case: 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?
23. Case: Collect the Data
What data do we need to answer these
questions?
24. Case: Collect the Data
Connection data (who is who connected to?)
Demographic data (what is the profile of the
connection)
Engagement data (how do they use the site)
25. Case: Process the Data
How is the data “dirty” and how can we clean
it?
26. Case: Process the Data
User input
Redundancies
Feature changes
Data model changes
27. Case: Explore the Data
What are the meaningful patterns in the
data?
28. Case: Explore the Data
Triangle closing
Time overlaps
Geographic overlaps
36. #3: Machine Learning Algorithms
Machine learning algorithms provide
computers with the ability to learn
without being explicitly programmed —
“programming by example”
42. But if you're interested...
Knowledge of statistics, algorithms, &
software
Comfort with languages & tools (Python,
SQL, Tableau)
Inquisitiveness and intellectual curiosity
Strong communication skills
It’s all Teachable!
45. 92%of grads placed in full-time tech jobs
job guarantee
Link for the third party audit jobs report:
https://www.thinkful.com/bootcamp-jobs-stats
Thinkful's track record of getting students jobs
46. Our students receive unprecedented support
1-on-1 Learning Mentor
1-on-1 Career MentorProgram Manager
San Diego Community
You
47. 1-on-1 mentorship enables flexible learning
Learn anywhere,
anytime, and at your
own schedule
You don't have to quit
your job to start career
transition
48. Try us out!
Learn Python, Python’s
data science toolkit,
Statistics intro.
Initial 3-week prep course
includes nine mentor
sessions for $250.
Option to continue onto
Data Science bootcamp
Talk to me (or email
jordan@thinkful.com) if
you’re interested