This document compares web development and data science careers. It defines data science as using the data pipeline to ask questions, collect raw data, process it, explore it, and communicate results. Four common data scientist types are described: researchers with advanced degrees, AI specialists who code solutions from scratch, statisticians with math backgrounds, and super analysts who solve problems through code. Web development types include frontend developers using HTML/CSS/JavaScript, backend developers creating applications, and full stack developers who can do both. The document discusses indirect tech jobs and shows data science and web development have growing job markets. It provides suggestions for how to learn both fields through a free two-week trial program.
This presentation was provided by Frankie Wilson of the Bodleian Library, University of Oxford, during the NISO Webinar, Using Analytics to Extract Value from the Library's Data, Part Two, held on September 19, 2018.
In this webinar, I’ll show you how to choose the right metrics to measure your team’s effectiveness and encourage the right behaviors. Then, I’ll explain how metric-driven coaching can help teams understand their process and highlight areas for improvement.
Watch the webinar here: http://leankit.com/blog/2016/03/metric-driven-coaching/
This presentation was provided by Suzanna Conrad of the California State University - Sacramento during the NISO webinar, Using Analytics to Extract Value from the Library's Data, held on September 12.
This presentation was provided by Frankie Wilson of the Bodleian Library, University of Oxford, during the NISO Webinar, Using Analytics to Extract Value from the Library's Data, Part Two, held on September 19, 2018.
In this webinar, I’ll show you how to choose the right metrics to measure your team’s effectiveness and encourage the right behaviors. Then, I’ll explain how metric-driven coaching can help teams understand their process and highlight areas for improvement.
Watch the webinar here: http://leankit.com/blog/2016/03/metric-driven-coaching/
This presentation was provided by Suzanna Conrad of the California State University - Sacramento during the NISO webinar, Using Analytics to Extract Value from the Library's Data, held on September 12.
How to Get Started or Expand Your Learning Analytics ProgramWatershed
Watershed co-hosted a workshop with ATD Nashville and Rustici Software to help L&D pros get started with learning analytics. Facilitators Mike Rustici and John Mattox, Ph.D., explored the five steps of learning analytics and helped attendees choose the right approach for their learning analytics programs.
Presentation to FourthLion (my former employer) staff on some lessons learnt while doing analytics across three domains, and the motivation for automation. Data science will IMO (a) have significant growing pains (b) see evolution similar to those that we saw in software engineering.
Transforming learning requires the right tools, a willingness to experiment, and knowledgeable and supportive leadership. Are you ready to take the leap? C. Moersch, 2017
Analytical Skills Tools and Attitudes 2013 Survey lavastorm analyticsjjoseph100
Survey of 425 analytic professionals- those that are making big data and analytics work within organizations - to see if they have the skills needed to push analytics further and/or to identify the skills most needed and how people are developing them.
Kenosha Public Library participated this year in the free Library Impact Survey to gauge customer interaction with library technology and found the real wealth of information came through participant comments. Barbara Brattin, Director of Kenosha Public Library, will share what Kenosha learned directly and passively through participation.
New Project Workshop: A Place to Start Working on Your Good IdeasWiLS
Have a new idea you want to get off the ground? Have an old idea that didn’t go as far as you wanted? Finding it difficult to move forward? This discussion will help you start thinking about beginning (or restarting) your project by providing some background and examples of Lean Startup practices and meaningful community involvement. We’ll also discuss transition from making a project plan to finding funding for your project. Our presenters will “workshop” a sample project to illustrate how their methods can be put into practice.
HOW TO BECOME AN EFFECTIVE DATA SCIENTIST (WORKSHOP) - MIKE BUGEMBEBig Data Week
Mike is an award winning big data evangelist, blogger, author and executive advisor working with organisations on how to get value from the deluge of data that is being produced today.
Currently, Mike is the Chief Analytics Officer at JustGiving.com, the world’s leading social giving company, where in a short period he has successfully transformed the organisation, establishing data and analytics as the lifeblood of the company. His team of analysts, statisticians and data scientists are central to every operational and strategic decision for the organisation by uncovering new growth opportunities and building smart data centric products.
His work has been referenced in a couple of recent bestselling book big data books and he is also working with several leading universities on a big data education programme for executives.
LibQual Challenges & Lessons Learned at UW OshkoshWiLS
Maccabee Levine discusses how UW Oshkosh conducted its recent LibQUAL+ survey, from participant recruiting through results analysis, including some changes from previous years that helped or hurt the process.
Tips and Tricks to be an Effective Data ScientistLisa Cohen
Data Science is an evolving field, that requires a diverse skill set. From Analytical Techniques to Career Advice, this talk is full of practical tips that you can apply immediately to your job.
Level Education: A Data Analytics Bootcamp for YouLevel Education
What is Level? What do you learn during the Level Bootcamp? Why is Level for you? Learn about our curriculum, what our students are saying and when the program runs next.
HadoopSummit'2015:Self Evolving Models for Dynamic System AccuracyRekha Joshi
As a speaker at San Jose Hadoop Summit 2015, presented the principles for Self Evolving Models for Dynamic System Accuracy.I talked about self evolving model for dynamic system accuracy on big data ecosystem.The theme of the topic is streaming and machine learning.
[moved from my peas2bees unofficial slideshare account; with side effects of loss of stats]
Whether you are a beginner, a transient, or a data scientist, this plan addresses each individual's needs. You can learn data science in a year if you follow this process.
How to Get Started or Expand Your Learning Analytics ProgramWatershed
Watershed co-hosted a workshop with ATD Nashville and Rustici Software to help L&D pros get started with learning analytics. Facilitators Mike Rustici and John Mattox, Ph.D., explored the five steps of learning analytics and helped attendees choose the right approach for their learning analytics programs.
Presentation to FourthLion (my former employer) staff on some lessons learnt while doing analytics across three domains, and the motivation for automation. Data science will IMO (a) have significant growing pains (b) see evolution similar to those that we saw in software engineering.
Transforming learning requires the right tools, a willingness to experiment, and knowledgeable and supportive leadership. Are you ready to take the leap? C. Moersch, 2017
Analytical Skills Tools and Attitudes 2013 Survey lavastorm analyticsjjoseph100
Survey of 425 analytic professionals- those that are making big data and analytics work within organizations - to see if they have the skills needed to push analytics further and/or to identify the skills most needed and how people are developing them.
Kenosha Public Library participated this year in the free Library Impact Survey to gauge customer interaction with library technology and found the real wealth of information came through participant comments. Barbara Brattin, Director of Kenosha Public Library, will share what Kenosha learned directly and passively through participation.
New Project Workshop: A Place to Start Working on Your Good IdeasWiLS
Have a new idea you want to get off the ground? Have an old idea that didn’t go as far as you wanted? Finding it difficult to move forward? This discussion will help you start thinking about beginning (or restarting) your project by providing some background and examples of Lean Startup practices and meaningful community involvement. We’ll also discuss transition from making a project plan to finding funding for your project. Our presenters will “workshop” a sample project to illustrate how their methods can be put into practice.
HOW TO BECOME AN EFFECTIVE DATA SCIENTIST (WORKSHOP) - MIKE BUGEMBEBig Data Week
Mike is an award winning big data evangelist, blogger, author and executive advisor working with organisations on how to get value from the deluge of data that is being produced today.
Currently, Mike is the Chief Analytics Officer at JustGiving.com, the world’s leading social giving company, where in a short period he has successfully transformed the organisation, establishing data and analytics as the lifeblood of the company. His team of analysts, statisticians and data scientists are central to every operational and strategic decision for the organisation by uncovering new growth opportunities and building smart data centric products.
His work has been referenced in a couple of recent bestselling book big data books and he is also working with several leading universities on a big data education programme for executives.
LibQual Challenges & Lessons Learned at UW OshkoshWiLS
Maccabee Levine discusses how UW Oshkosh conducted its recent LibQUAL+ survey, from participant recruiting through results analysis, including some changes from previous years that helped or hurt the process.
Tips and Tricks to be an Effective Data ScientistLisa Cohen
Data Science is an evolving field, that requires a diverse skill set. From Analytical Techniques to Career Advice, this talk is full of practical tips that you can apply immediately to your job.
Level Education: A Data Analytics Bootcamp for YouLevel Education
What is Level? What do you learn during the Level Bootcamp? Why is Level for you? Learn about our curriculum, what our students are saying and when the program runs next.
HadoopSummit'2015:Self Evolving Models for Dynamic System AccuracyRekha Joshi
As a speaker at San Jose Hadoop Summit 2015, presented the principles for Self Evolving Models for Dynamic System Accuracy.I talked about self evolving model for dynamic system accuracy on big data ecosystem.The theme of the topic is streaming and machine learning.
[moved from my peas2bees unofficial slideshare account; with side effects of loss of stats]
Whether you are a beginner, a transient, or a data scientist, this plan addresses each individual's needs. You can learn data science in a year if you follow this process.
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.
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Answer to the most commonly used terminology Data Science with their areas of crucial roles in solving issues with case studies.
Likewise, let me know if anything is required. Ping me at google #bobrupakroy
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.
Making a Successful Career in Data ScienceSamidha Takle
Start a Successful Career in Data Science with the Leading IT Training Institute in Pune & Mumbai. Contact Now.
To know more details you can visit here:
https://texceed.in/making-a-successful-career-in-data-science/
Data Science. Business Analytics is the statistical study of business data to gain insights. Data science is the study of data using statistics, algorithms and technology. Uses mostly structured data. Uses both structured and unstructured data.
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.
Business leaders everywhere are looking to data to inform their decision making. Accompanying this demand are misunderstandings of what it takes to transform data into something that can inform a decision. What is the data infrastructure required? In this talk, I'll dispel some of these misunderstandings and discuss what it takes to build good data infrastructure. I'll discuss the components of a good data infrastructure. The best practices and available tools for gathering data, processing it, storing it, analyzing it and communicating the results. The goal is for these components to create a data infrastructure which can evolve from simple reporting to sophisticated insights for decision making.
Presented at OpenWest 2018
If you’re learning data science, you’re probably on the lookout for cool data science projects. Look no further! We have a wide variety of guided projects that’ll get you working with real data in real-world scenarios while also helping you learn and apply new data science skills.
The projects in the list below are also designed to help you get a job! Each project was designed by a data scientist on our content team, and they’re representative examples of the real projects working data analysts and data scientists do every day. They’re designed to guide you through the process while also challenging your skills, and they’re open-ended so that you can put your own twist on each project and use it for your data science portfolio.
You can complete each project right in your browser, or you can download the data set to your computer and work locally! If you work on our site, you’ll also be able to download your code at any time so that you can continue locally, or upload your project to GitHub.
The sky is the limit here and what you decide to look into further is completely up to you and your imagination!
1. Learning by Doing
Learning by doing refers to a theory of education expounded by American philosopher John Dewey. It is a hands-on approach to learning, meaning students must interact with their environment in order to adapt and learn. This way of learning sharpen your current skills and knowledge and also helps in gaining new skills that could only be acquired by doing.
Car driving is a perfect example of this, you can read as much as you would like about the theory of driving and the rules, and this is very important, and the more you understand the theory the better you get in the practical part. But you will only be able to drive better by applying this knowledge on the real road. In addition to that, there are some skills and knowledge that will be only gained by actually driving.
Data science is the same as driving. It is very important to have solid theoretical knowledge and to regularly increase them to be able to get better while working on a project. However, you should always apply this theoretical knowledge to projects. By this, you will deepen your understanding of these concepts and Knowledge, have a better point of view of how they work in a real-life, and will also show others that you have strong theoretical knowledge and are able to put them into practice.
There are different types of guided projects. One of them is a guided project for
There are a lot of benefits for it:
It removes the barriers between you and doing projects
Saves you much time thinking about the project and preparing the data.
It allows you to apply the theoretical knowledge without getting distracted by obstacles.
Practical tips that can save your effort and time in the future.
#datasciencefree
#rohitdubey
#teachtechtoe
#linkedin.com/in/therohitdubey
Intro to Artificial Intelligence w/ Target's Director of PMProduct School
Given that Machine Learning (ML) is on every product enthusiast’s mind, this talk gave a broad view of the investment landscape for future innovation. Director of Product Management at Target, Aarthi Srinivasan, talked about macro AI themes & trends, how you can build your AI team and how to create a ML backed product vision.
Additionally, this talk armed the attendees with enough information to create your Point of View (POV) on how to incorporate AI into your business.
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
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
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.
5. What is Data Science: The Data Pipeline
1. Form a Question
2. Collect the Raw Data
3. Process the Data
4. Explore the Data
5. Communicate Results
6. What is Data Science: 4 Types of Data Scientists
1. The Researcher
2. The AI or Automation Guru
3. The Statistician
4. The Super Analyst
7. What is Data Science: The Researcher
The first data scientists
Often have PhD’s or other advanced degrees
Research background (often in academia)
Research at big companies
Google, Microsoft, etc all have research
departments
Become Teachers themselves
Government agencies
8. What is Data Science: The Researcher
Work on cutting edge research
The first facial recognition software
Self driving cars
Start by getting a PhD or graduate degree
Then learn to code
9. What is Data Science: The AI Guru
They love to code
Mostly from scratch
Work at big companies and startups
There are over 3,000 Artificial Intelligence
startups on AngelList
(if you don't know what AngelList is...)
https://angel.co
10. What is Data Science: The AI Guru
Chatbots and automated assistants
(Siri, Alexa, Einstein, Will.i.Am, etc)
Automated Support
Really just automating anything
Replacing humans, adding efficiency
Write code for everything
Try to automate your own life or routines
Python/JavaScript
11. What is Data Science: The Statistician
Formal mathematics or statistics training
Lots of Masters in Statistics (shocking)
Not necessarily experienced programmers
May use tools like STATA or R
Statistical consultants
Political polling as an example
Experimentation experts
A/B testing
Optimizely
12. What is Data Science: The Statistician
Answer statistical questions with data
Are these things different?
Which is best?
Usually don’t build consumer products
Study the math
Elements of Statistical Learning is a great place
to start
But the AI gurus may be automating a lot of
these jobs away...
13. What is Data Science: The Super Analyst
This is the fastest growing section of data
science
Love numbers and products, and want to put
the two together
Expert problem solvers through code
Can communicate those solutions
Different from a Data Analyst (machine
learning)
The foot-soldiers of the data science revolution...
14. What is Data Science: The Super Analyst
Every company that generates data
Every app and website records enormous
amounts of data
What can be learned from that data
So basically, Data Scientists are needed
everywhere.
15. What is Data Science: The Super Analyst
They don’t necessarily build consumer
products, but find consumer insights
Who’s going to be our next customer?
What’s our growth going to be next month?
Will this person click on our ad?
Learn to code
Immerse yourself in data
Learn some basic Machine Learning
16. What is Data Science: Growth in the Job Market
https://www.glassdoor.com/Salaries/houston-data-scientist-salary-SRCH_IL.0,7_IM394_KO8,22.htm
17. What is Data Science: Growth in the Job Market
https://trends.google.com/trends/explore?date=today%205-y&q=data%20science
19. What is Web Development: Types of Developers
Frontend Engineer: using technologies such as
HTML, CSS, and JS to create interactive
experiences through websites
Backend Engineer: creating applications that
deliver data to websites, mobile apps, or
databases.
20. What is Web Development: Types of Developers
Full Stack Engineer: a rare breed of developers
that can do both frontend and backend
engineering (2-5 years)
QA Engineer: creating and running tests for
code that is written by other developers to
help catch bugs before the code is made live
21. What is Web Development: Indirect Tech Jobs
Product Manager (Coding + Product)
someone who understands the business
goals of a customer and can create product
requirements to give to developers.
Lay-nical / Tech-man
Growth Hacker (Coding + Marketing)
someone who can use data and analytics
to come up with experiments on how to
increase traffic to a website or social media
profile
22. What is Web Development: Indirect Tech Jobs
Sales Engineer (Coding + Sales)
understanding coding can be extremely
helpful in a sales role. Come out of the
basement nerd.
Data Scientist (Coding + Data)
someone who can use statistics and
programming to find valuable insights from
extremely large datasets
27. Interests and Personality: Data Scientists
Have a graduate degree or a strong,
quantitative academic background
Statistician or a Researcher
Are an experienced engineer
You could be an AI guru
Love data, and analysis, and want to find the
‘signal in the noise’
You could be a Super Analyst
29. Interests and Personality: Web Developers
If you enjoy designing and creating user
experiences that others can enjoy
frontend engineer or full stack engineer
If you love organizing data in tables and
sending data to the right place
backend engineer or full stack engineer
If you like catching bugs or creating tests to
make sure features pass or fail
QA Engineer
32. Jordan Zurowski
Education Advisor
Thinkful Two Week TrialThinkful Two Week Trial
Start With: Python/SQL or HTML,CSS
and JavaScript
Personal Program Manager
Unlimited Group Mentor Sessions
Student Slack Community
Option to continue with full bootcamp
Financing & scholarships available
http://bit.ly/thinkful-ds-trial
