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Join more than 25 incredible speakers who will demystify data science, and discuss the training, the tools, and the career path to the "best job in the United States."**SOURCE: Glassdoor
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You have been cruelly misled into thinking that the way to get a data science job is to stack up pre-requisites and hope somebody picks you. You have wasted your very limited time and energy trying to keep up with the ever growing heap of data science "must-knows" like Machine Learning Algorithms, Distributed Computing, SQL & NoSQL Databases, Statistical Modeling, Deep Learning, Natural Language Processing, Data Visualization, Hadoop, Kafka, Spark, Big Data, and more. Your patience, confidence, and sleep are plummeting from the fear that you'll chose the wrong things to learn and you won't get a data science job.
How would you feel waking up tomorrow knowing how to get the exact type of data science job that fulfills all of your goals? You would be overflowing with extra energy and time because of your ability to make all of the right choices of what to learn, what to study, and what to ignore. Your increased confidence in getting the right type of job (for you) with the right group (for you) at the right company (for you) doing the right things (for you) would show up in all other aspects of your life. How excited and relieved would you be having a crystal-clear guide for how to be the chooser rather than the person waiting at the sidelines hoping, praying, and crossing-their-fingers in the hopes that they are chosen? You'd be a high-profile data scientist with a lucrative salary in no time!
Great news - you can stop waiting and hoping a data science manager will pick you. You can become a Data Scientist without being overwhelmed, perplexed, or unmotivated. This talk will make you a chooser by showing you three key ways you can turn the "Getting A Data Science Job" process on its head so that you have a 100% highly customized plan for you. You'll learn a) how to take control of the process from the beginning, b) how to build your portfolio so that you beat out all the other candidates who are hoping to be chosen, and c) how to revamp your resume so that your resume always goes to the top of the "must-hire" pile.
I will highlight the 5 most important things in data science, providing a short illustrative (hopefully enlightening and informative) example from my own experience for each of these: The Data, The Science, Data Storytelling, Data Ethics, and Data Literacy. Since the primary focus of data science is discovery (new insights, better decisions, and value-added innovations), I will include an overview of the different flavors of machine learning for discovery in big data, plus a summary of the different levels of analytics maturity and what they mean for real world data science applications. I will finish with a review of the top characteristics of leading candidates for data scientist positions within my organization.
Ten years ago, while I was studying math in my undergraduate, Business Week published an article declaring that "There has never been a better time to be a mathematician." As the field of data science has developed since, so has the hype around mathematics. While mathematical modeling is an important component of data science, my math education did little to prepare me for the types of challenges I face in my day-to-day as a data scientist. Many of my most valuable skills and tools are not taught in any traditional educational settings. In this talk, I will discuss this disconnect between my mathematics education and my data science career, the importance of self-instruction for data scientists, and advice on how students can prepare for a career in data science.
Data science can be an overwhelming field. Many people will tell you that you can't become a data scientist until you master the following: statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and more. That's simply not true.
In this talk, you'll learn how you can get started with your data science career today by learning how to master a small set of tools in a single programming language.
The worst part of data science is the hiring process. It’s broken on both ends. People trying to get into the field don’t really understand the field well enough to position themselves for success. Companies hiring their 1st data science team have much the same problem. This is the real data science skills gap.
I want to speak to both audiences: aspiring/novice data scientists and those tasked with building their 1st team. Business needs meet skill sets in a rapid-fire fashion. I want both sides to leave with a framework, high quality questions to ask, and an understanding of what they need to educate themselves about to be successful.
Data science has been around for decades, and it’s not just big data. I hear a lot of people clumping these two together like they go hand-in-hand, which I agree with to an extent. However, big data needs data science but data science doesn’t necessarily need big data. Most of the data a typical company handles on a daily basis or house internally is not big data. Even Facebook and Google break up or segment their data into workable pieces. Data science is big, small, structured, unstructured, messy, clean, etc… It’s more than just analytics. As a data scientist, you’ll become a liaison between the IT department and the C suite. You have to talk both languages and you have to understand the hierarchy of data, you can’t be just an architect or data expert.
What really matters in data science is the team effort and your role as a liaison. Your company has large amounts of data and you want to make sure your queries are correct. Whatever tool you use, make sure you have your data cleansed. You want to know that it’s normalized and indexed so that things run smoother. You want to be able to give insight, which requires knowledge of your audience. If your audience is the C suite of a multi-million dollar company, you’re going to need everything you have to back up your conclusions. Be able to prove it and be prepared for questions.
What sort of personality makes for an effective data scientist? Definitely curiosity, I remember in college, my professors shut the door if they saw me coming because telling me that a2 + b2 = C2 was never enough. I wanted to know why. So the biggest question in data science is “why?” Why is this happening? If you notice that there’s a pattern, ask “why?” Is there something wrong with the data or is this an actual pattern going on? Can we conclude anything from this pattern? A natural curiosity will definitely give you a good foundation.
Data science is a way to solve problems using data and analytics. To ensure data science success, you need to provide data professionals with an environment that is open, engaging, and fosters collaboration. To explore how you or your data science team can optimize the value of your data, this talk is for you.
In this talk, I will present the latest research on people, processes and technologies behind the insights. The talk will help aspiring data professionals better understand how they can contribute to data science projects. I will cover topics related to data science skills, organizational factors and the use of popular technologies and tools that drive insights.
Discover how to take your career to the next level, while also paving a path to personal fulfillment, by earning influence in your industry through active community engagement and advocating on behalf of the organizations you love and work with the most. Technology companies, large and small, are investing in customer communities in an effort to learn more from their users, ensure customer success and stay competitive. This is great news for data scientists because investment in communities enables you to effortlessly connect with peers that have common interests, share best practices, and build your personal brand or expertise. Get insider tips and tricks for using corporate community and advocacy programs to excel your career in data science from an Analytics Community Manager who cares deeply about enriching vibrant user communities and empowering users.
This presentation is not about data science, or machine learning, or neural networks, or anything you'd expect from a talk like this. Rather, this talk is about how to learn data science. How to grasp its basics, claw your way to solid understanding, then reach for mastery. This is a talk about notebooks, markers, flashcards, and personal projects. If you want to learn how to learn data science, this is the talk for you.
Data scientists use statistics to reach meaningful conclusions about data. Unfortunately, statistical tools are often misapplied, resulting in errors that cost both time and money. By being aware of the most common mistakes involving statistics, we can become better and more productive data scientists. To illustrate the kinds of problems that often arise, I present examples of egregious misuses of statistics in business, technology, science, and the media and analyzes them through a review of basic statistical concepts. I explain how to weed out the most common errors and reduce the chance of being fooled by statistics.
No tool is best for all problems. The very thing that makes an algorithm great for solving one problem can make it terrible for another. To know which tool to use when, it helps to understand a little bit about how they work.
There are several complementary strategies for learning how tools work, including tutorials, coursework and coding them up yourself. By far the best that I have found to familiarize myself with a tool is to build something with it. I haven’t found any shortcut to this.
The promise of artificial intelligence is that it will give use tools that are good at solving lots of different problems. When designing general purpose tools, it’s important to remember that being excellent at everything is impossible. Every agent has blind spots and weaknesses. Defining our set of target tasks can keep us focused on building tools that are excellent at the right things.
Data Scientist is consistently rated among the top career choices. However, being a data scientist can mean many different things. There are numerous skills involved, and few people can master them all. This reason is why data science is rapidly adopting the team approach. In the new teams, each member focuses on very specialized skills. What are these specialties?
This talk will cover the common specialties in data science. It will provide some examples and a first step for getting started in each specialty. There is no long checklist, no sequence of courses to take, and nothing overwhelming. All you need to do is choose a specialty and go! And lucky for you, there is no wrong choice.
Are you a beginner, or maybe transitioning into a data science career? Could you use some help figuring out what topics to learn to become a data scientist, and how to frame your learning journey? Then this talk is for you! Renée M. P. Teate from the Becoming a Data Scientist Podcast shares advice from her podcast guests, and from her own journey to become a Data Scientist at HelioCampus, a higher ed analytics startup. Learn how to evaluate advice from others, construct your own learning path, and find learning resources to get yourself started on the path to becoming a data scientist!
There are 44 types of data scientists. I'll tell you 88 lines worth of things I've learned about them. Hopefully they'll rhyme (the lines, not the data scientists).
Many people claim that deep learning needs to be a highly exclusive field, saying that you must spend years studying advanced math before you even begin to attempt it. Jeremy Howard and I believed that this was just not true, so we set out to see if we could teach deep learning to coders (with no math prerequisites) in 7 part-time weeks, using a code-centric, application-focused approach.
We've now successfully taught thousands of students, and our students have gone on to become Google Brain residents, created new deep learning based products, launched companies, won hackathons, and had their work featured on HBO's Silicon Valley. I’ll share what we learned about how to learn deep learning effectively, so that you can set out on your own learning journey.
Predictive Modeling and Supervised Learning are staple techniques in the Data Science arsenal of algorithms. The origins of some of those solutions trace back more than 50 years, but with the recent wide adoption of data technologies they are receiving a new level of attention. This talk takes on some of the more commonly asked question around predictive modeling and machine learning: What are the key success factors of making prediction work? Which algorithm is best? When does it even make sense to try predictive modeling? When is a predictive model good enough? And when do predictive models fail?
Data Science interviews are a beast. Companies want to test your coding skills, assess your stats knowledge, understand your problem solving skills, gauge your ability to communicate real business insights, and evaluate your overall “fit” for the company culture. Overwhelmed yet? I’ll break the process down piece by piece, walking you through what to expect and how to best prepare so you can overcome those pre-interview jitters. I’ll also share some insider tips and tricks to help you stand out from the crowd.
Many aspiring data scientists I talk to are confused about the specific day-to-day role of a data scientist. A lot of this is because different companies use the same term to refer to vastly different roles. I will talk about the different kinds of data scientists at various kinds of companies, the kind of day-to-day responsibilities you might expect from each of the roles, and how you can start figuring out which one is more interesting to you. I will have a special focus on Product Analytics (my main role at Quora) which focuses on using statistics, programming, and metrics to help product development make better decisions with data.
Are you a real Data Scientist? No? Cool, neither am I — well, at least not according to some of the Venn diagrams I've seen out there. I don't even play one on TV.
If you're reading this, though, someone out there thought it'd be a good idea to have me talk about something data science-y. Barring a case of mistaken identity, this is probably because I've been learning, doing, sharing, and "discussing" data science on Twitter for the past couple of years.
This talk will cover some of the ways in which I try to navigate the overwhelming amount of data-science material out there. I'll go through mistakes I've made, lessons I've learned, and how connecting with data science communities (online and off) makes all of the above less intimidating, and much more fun than it has any right to be.
We'll figure out how you, too, can go from being "not a real Data Scientist," to "not a real Data Scientist whom people ask to talk about data science."
A/B Testing has become the gold standard for measuring the effectiveness of changes to a company's website or app, ranging from a change in the color of a button to a whole new advertising model. It's used extensively at large technology companies such as Amazon, Facebook, and Google, as well as at small startups and non-tech companies.
For companies with a lot of traffic and a data engineering pipeline already set-up, A/B Testing can appear to be exceedingly simple. But whether it's trying to sequence experiments, work with non-technical partners, or deciding what to do when there is a bug, there are a number of issues that can complicate an analysis. We'll cover some of these business and statistical challenges, using some recent experiments at Etsy as examples.
Artificial Intelligence is not just able to mimic us, but now it can create art and music. How does AI ‘imagine’? I’ll talk about the model that makes this possible - Generative Adversarial Networks, and how that leads to some surprising findings about machines and humanity.
Our actions and choices as humans are largely influenced by the culture around us—things like race, language, accessibility, and sense of equality. As we offload decisions to algorithms and machine learning, those choices become less and less visible, leaving us to wonder—can algorithms really understand sensitivities like humans do?
In this talk, Camille walks through some examples of services we all use and how they have adapted machine learning to become more inclusive. She explores what we can do to create culturally sensitive computer intelligence and why that is important for the future of both AI and human beings.
What does linear regression have to do with building a reinforcement learning agent that defeated the world's best Go players in a competition? How do concepts from Bayesian statistics inform strategies for exploration and exploitation? What is the 20,000-foot view of how a robot mouse might learn to navigate a maze and find the reward of cheese it seeks? In this session, we'll explore how supervised learning concepts form the foundation of deep reinforcement learning, an active research area at the cutting edge of artificial intelligence. Topics covered: linear regression, objective functions, gradient descent, Markov decision processes, Q-learning, policy learning, and deep reinforcement learning.
For individuals seeking to transition their career into data science, a bootcamp can be a smart decision, providing a structured learning environment at a fraction of the cost of obtaining an advanced degree. The first Metis bootcamp ran in Fall, 2014. Since that time, Metis has graduated hundreds of students, who are now in data-science related jobs across a wide range of industries and companies, including Facebook, Apple, IBM, Booz Allen, Tesla, and Capital One Labs.
Megan Ayraud, Head of Careers for Metis, will moderate this five-person Metis alumni panel to see, their answers to why they chose to attend a bootcamp, what background training they had, how well prepared were they for their data science jobs, what was the post-bootcamp interview process like, and many other questions.
The panelists include: