Boston | April 13th – April 17th, 2020

Data Science Kick-start

Kick-start Your Data Science Career

Learn Essential Skills to Kick-start Your Career

Data Science skills are in ever-increasing demand. Whether you are a data science beginner, a software engineer looking to augment skills, or even a business professional looking beyond the hype, our Kick-start track is for you.

Accelerated Learning Over 4 Packed Days

The Data Science Kick-start track offers many introductory level talks, tutorials, and workshops to get you started. Our expert speakers and presenters offer world-class instruction, and give you invaluable insights to help you learn what matters most.  We cover the most important tools, topics, and techniques in use to ensure you hit the ground running.

Some of Data Science Kickstart Speakers


Click Here For Full Lineup
2020 Speakers

Sample Talk, Workshop, and Training Sessions

Data Science Kick-start Sessions
Wednesday, April 15th
Tuesday, April 14th
Monday, April 13th
Friday, April 17th
Wednesday, April 15th
Tuesday, April 14th
Monday, April 13th
Friday, April 17th
09:00 - 18:00
Programming with Data: Python and Pandas

Full-Day Training | Kick-starter | Open-source | Intermediate

 

Whether in R, MATLAB, Stata, or Python, modern data analysis, for many researchers, requires some kind of programming. The preponderance of tools and specialized languages for data analysis suggests that general purpose programming languages like C and Java do not readily address the needs of data scientists; something more is needed.

In this training, you will learn how to accelerate your data analyses using the Python language and Pandas, a library specifically designed for interactive data analysis. Pandas is a massive library, so we will focus on its core functionality, specifically, loading, filtering, grouping, and transforming data. Having completed this workshop, you will understand the fundamentals of Pandas, be aware of common pitfalls, and be ready to perform your own analyses…more details

 

Programming with Data: Python and Pandas image
Daniel Gerlanc
President | Enplus Advisors Inc.
09:00 - 13:00
Recommendation Systems in Python

Training | Open-source | Kick-starter | Beginner

 

Recommendation systems are fundamental to essentially every business on the planet. From providing the shows and movies you should watch, the books and articles you should read, the products you should purchase, or the people you should date making good recommendations can make or break your business. This workshop will give a general introduction to recommendation systems, as well as introduce practical tools for you to apply recommendation systems to your own use cases. Though the primary focus of this workshop will be to understand the different methods you might use to make recommendations, we will also touch briefly on common difficulties with building effective recommendation systems, how to deploy your recommendations, and how to evaluate whether your recommendations are effective…more details

Recommendation Systems in Python image
Joshua Bernhard
Data Scientist | NerdWallet
09:00 - 13:00
Machine Learning in R Part III: Forecasting Time Series Data

Training | Machine Learning | R-programming | All Levels

 

Temporal data requires special care to model as it violates several principles of standard machine learning models. R has long had top-of-the-line forecasting tools, though recently new ones have been developed which greatly ease working with time series data. We use the tsibble package for manipulating time series data, feasts for visualization , and fable for building forecasting models such as ETS and ARIMA…more details

Machine Learning in R Part III: Forecasting Time Series Data image
Jared Lander
Chief Data Scientist, Author of R for Everyone, Professor | Lander Analytics, Columbia Business School
09:00 - 12:30
SQL for Data Science

Bootcamp | Kickstarter | Open-source | Beginner

 

By completing this workshop, you will develop an understanding of relational models of data, how SQL is used to retrieve that data, and how to join tables, aggregate information, and answer data science questions. You will also become familiar with many of the common types of SQL databases, how to access information in a database from the command line, and how to integrate database access from within Python.

Lesson 1: Relational Databases and Foundational SQL
Familiarize yourself with relational databases and the SQL syntax necessary to retrieve information from tables in a database. At the end of this lesson, you will be able to comfortably explore a database and retrieve filter, and sort information from a table…more details

SQL for Data Science image
Mona Khalil
Data Scientist & Consultant | Educator | GreenHouse Software | EMERITUS
09:00 - 13:00
Introduction to Deep Learning & Neural Networks I: Concepts

Training | Deep Learning | Kick-starter | Beginner

 

This session will focus on the foundational concepts of neural networks and deep learning. Concrete examples will be used to illustrate abstract concepts and methods. Participating in this workshop you will gain an intuition for the principles underlying modern neural networks including multilayer perceptrons, autoencoders, convolutional neural networks, and recurrent neural networks including long short-term memory. No prior experience with neural networks or machine learning required…more details

Introduction to Deep Learning & Neural Networks I: Concepts image
Brandon Rohrer, PhD
Principal Data Scientist | iRobot
09:00 - 13:00
Data Analysis, Dashboards and Visualization with Tableau – How to Create Powerful Visualizations Like a Zen Master

Training | Data Visualization | Kick-starter | All Levels

 

In this complete hands-on training session, you will learn to turn your data into interactive dashboards, how to create stories with data and share these dashboards with your audience. We will begin with a quick refresher of basics about design and information literacy and discussions about practices for creating charts and storytelling utilizing best visual practices. Whether your goal is to explain an insight or let your audience explore data insights, using Tableau’s simple drag-and-drop user interface makes the task easy and enjoyable.
In this session, we’ll cover intermediate and advanced tableau functionality:
1. Database connectivity
2. Blending and joins using multiple data sources, web connectors and plugins,
3. Perform data analyses and create graphs from a real-world dataset, using Tableau Public (free to use)
4. Deeper Analysis – Trends, Clustering, Distributions, and Forecasting
5. Table Calculations, Sets, Filters, Level of Detail expressions, Parameters
6. Spatial analytics – Using Maps and Geocoding
7. Right and Wrong way to build Dashboards and Best Practices…more details 

 

Data Analysis, Dashboards and Visualization with Tableau – How to Create Powerful Visualizations Like a Zen Master image
Nirav Shah
Founder | OnPoint Insights
09:00 - 13:00
Introduction to Machine Learning with scikit-learn

Training | Kickstarter | Machine Learning | Beginner

 

Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detect the Higgs boson, machine learning excels at extracting knowledge from large amounts of data. This talk will give a general introduction to machine learning, as well as introduce practical tools for you to apply machine learning in your research. We will focus on one particularly important subfield of machine learning, supervised learning. The goal of supervised learning is to “learn” a function that maps inputs x to an output y, by using a collection of training data consisting of input-output pairs. We will walk through formulating a problem as a supervised machine learning problem, creating the necessary training data and applying and evaluating a machine learning algorithm. This workshop should give you all the necessary background to start using machine learning yourself…more details

Introduction to Machine Learning with scikit-learn image
Andreas Mueller, PhD
Author, Research Scientist, Core Contributor of scikit-learn | Columbia Data Science Institute
09:00 - 10:30
Quick Package Development in R and Python (from “Python or R” to “Python and R”)

Workshop | ML for Programmers | Kick-starter | Beginner-Intermediate

 

When building a data science team it is important to document and record work flows in both R and python. A data science department can use installable git controlled packages to form a foundation of code and best practices. In addition, libraries dramatically cut down the time and effort required for a team to bring work to production. Additionally, package development is the first step for engineers and scientists aiming to contribute their unique ideas back to the community.

This workshop aims to teach the basics of package development in both R and python in 90 minutes. We will touch upon why a data science team should strive to be fully fluent in both languages. We will show simple R package development and simple Python package development. Finally, we will demonstrate how one can use an open source package to test the interface similarity between R and Python packages designed to support identical workflowsmore details

 

Quick Package Development in R and Python (from “Python or R” to “Python and R”) image
Theodore Bakanas
Senior Data Scientist | Uptake
Quick Package Development in R and Python (from “Python or R” to “Python and R”) image
Zhi Lu, PhD
Data Scientist | Uptake
12:00 - 12:45
Managing Data Projects Like a Software Engineer

Talk | ML for Programmers | Kick-starter | Beginner

 

In this talk we’ll go over how to write code that is reproducible and easy for other people to work with.

We’ll start by talking about virtual environments. Virtual environments allow you to define the dependencies for your projects (such as NumPy or Matplotlib) and to keep these dependencies separated between projects. We’ll also outline some choices you have about how to manage your virtual environments.

Next we’ll talk about version control and why you should be using it even if you’re the only contributor to a project. Version control helps create a log of what work was done and why, and will give you the ability to go back when you inevitably make a change to your project that you can’t figure out how to undo.

Then we’ll discuss project structure by reviewing DrivenData’s Cookiecutter Data Science template. The template encourages a number of best practices, and makes it so that anyone familiar with the template will be able to look at your code for the first time will be reasonably well oriented.

Finally, we’ll briefly cover why you should establish coding styles and always use a linter...more details

Managing Data Projects Like a Software Engineer image
Michael Jalkio
Data Engineer | Amazon
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See all our talks and hands-on workshop and training sessions
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Sessions on Data Science Kick-start Track

  • Workshop: Deciphering the Black Box: Latest Tools and Techniques for Interpretability

  • Talk: Adversarial Attacks on Deep Neural Networks

  • Training: Integrating Pandas with Scikit-Learn, an Exciting New Workflow

  • Workshop: Machine Learning for Digital Identity

  • Talk: Adding Context and Cognition to Modern NLP Techniques

  • Training: Good, Fast, Cheap: How to do Data Science with Missing Data

  • Workshop: Open Data Hub workshop on OpenShift

  • Talk: Practical AI Solutions within Healthcare and Biotechnology

  • Training:  Apache Spark for Fast Data Science (and Fast Python Integration!) at Scale

  • Workshop: Reproducible Data Science Using Orbyter

  • Talk: Combining Millions of Products into One Marketplace Using Computer Vision and Natural Language Processing

  • See the whole schedule!

What You’ll Learn

Data Science has many focus areas.  The goal of this track is to accelerate your knowledge of data science by offering a series of introductory level training, talks, and workshops on the most important tools and topics to help you quickly gain essential data science skills

  • Deep Learning

  • Machine Learning

  • Artificial Intelligence

  • Text Analytics

  • Data Wrangling

  • Predictive Analytics

  • Machine Vision

  • Voice AI

  • Pyton for Data Science

  • R & Julia

  • Apache Spark, MLlib

  • Scikit-learn, Azure ML, Amazon ML, H20.ai

  • Tensorflow, Caffe, CNTK, Torch

  • Data Visualization

  • Neo4J, D3.js, R-Shiny

Why Attend?

Accelerate and broaden your knowledge of key areas in data science, including deep learning, machine learning, and predictive analytics

With numerous introductory level workshops, you get hands-on experience to quickly build up your skills

Post-conference, get access to recorded talks online and learn from over 100+ high quality recording sessions that let you review content at your own pace 

Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science

Learn directly from world-class instructors who are the authors of and contributors to many of the tools and languages used in data science today

Meet companies ranging from hot startups to Fortune 500 looking to hire professionals with data science skills at all levels

Network at our numerous lunches and events to meet with data scientists, enthusiasts, and business professionals

Get access to other focus area content including, ML / DL, Data Visualization, Quant finance, and Open Data Science

Who Should Attend

The Data Science Kick-start track is ideal for anyone looking to learn the languages, tools, and topics of data science. Not only will you train in key areas of data science like deep learning and machine learning;  you will also learn the tools and languages to implement modules such as TensorFlow, scikit-learn, Python, and R

  • Beginners interested in getting started in data science

  • Individuals seeking to better understand focus areas of data science such as deep learning, machine learning, text analytics etc.

  • Software engineers and software architects looking to employ machine learning and data science in their programming

  • Data wranglers and database specialists looking to leverage their existing data assets with data science tools and models

  • Business professionals interested in data science and looking to gain a deeper understanding

  • Experienced data scientists looking to enhance their data science skills

  • Anyone interested in learning data science languages such as Python, R, and Julia

  • Technologists looking to use the latest data science tools such as Apache Spark and TensorFlow to implement machine learning and deep learning

  • Students and academics looking for more practical applied training in data science tools and techniques

  • Industry experts looking to assess the impact of data science on their industry

Sign Up for ODSC East 2020 | April 13th – April 17th

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