November 15th - 18th, 2021

Data Science Kick-start

Kick-start Your Data Science Career
Register now | 75% ends soon

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.

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, topic, and techniques in use to ensure you hit the ground running.

Some Previous Speakers

Software EngineerStride Consulting

Boris Paskhaver

Boris Paskhaver is a full-stack web developer based in New York City with experience building apps in React / Redux and Ruby on Rails. His favorite part of programming is the never-ending sense that there’s always something new to master — a secret language feature, a popular design pattern, an emerging library or — most importantly — a different way of looking at a problem.

Getting Started with Pandas for Data Analysis(Half-Day Training)

Data Science InstructorGeneral Assembly

Bethany Poulin

Bethany Poulin is a data scientist and educator with expertise in statistical analysis, data visualization, and complex algorithmic problem-solving. She has worked as a professional data scientist and educator for the last 4 years and loves sharing what she has learned with her students. Her unusual background in Fine Arts and experience teaching high school give her a unique perspective on both problem-solving and the learning-teaching process. Prior to teaching this part-time course, she was a lead instructor in our Data Science Immersive program on the Boston campus. She teaches simply because she loves students and enjoys being a part of their success. She holds a BFA in Professional Photography from Rochester Institute of Technology, did post bachelor’s studies and the University of Montana in Environmental Biology, where she was recognized nationally as a Morris K Udall Scholar, is one semester away from an MS in Data Science from the City University of New York. She is has presented at PyOhio 2018 and 2019 and gave a presentation at ODSC East in 2019. In her spare time, Bethany is an avid fly fisherman, potter, and maker.

Introduction to Shiny Application Development(Half-Day Training)

Data Scientist II AI and MLAmazon

Wale Akinfaderin, PhD

Adewale (Wale) Akinfaderin is a Data Scientist at Amazon Web Services. His expertise is in machine learning, deep learning, statistical experimentation and general information theory. He has broad experience implementing and extending ML techniques to solve practical and business problems. In his spare time, he conducts research on Machine Learning for the Developing World.

Mathematics for Data Science and Machine Learning(Half-Day Training)

Principal, Data EngineeringTredence Inc.

Changa Reddy

Changa has 20 years of Consulting experience implementing data and analytics solutions for cross sector clients with focus on reducing costs, improving operational efficiencies and adoption across different business functions. He has seen the transition from database solutions, packaged analytics to self-service advanced analytics over the last 2 decades.

ML Easel – Tredence’s Data Science and ML Engineering Workbench(Talk)

Staff Associate - Machine LearningColumbia University in the City of New York

Thomas Fan

Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open-source machine learning library is written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open-source software is one of the best ways to maximize one’s impact.

Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)

Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values(Half-Day Training)

Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection(Half-Day Training)

Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression(Half-Day Training)

Data Visualization DeveloperObservable

Ian Johnson

Ian Johnson is a User Experience Engineer at Google. He also organizes of Bay Area d3, starts with SVG and then dives deep into d3 including DOM manipulation, categorical and quantitative scales, axis, brushes, color schemes, events and histograms. Ian likes to make sense of data by exploring it visually with D3.js!

Painting with Data: Introduction to d3.js(Half-Day Training)


Sessions on Data Science Kick-start Track

  • Training: Mathematics for Data Science and Machine Learning

  • Training: Getting Started with Pandas for Data Analysis

  • Training: Introduction to Machine Learning

  • Training: Hands-On Introduction to LSTMs in Keras/TensorFlow

  • Training: Introduction to Deep Learning for Engineers

  • Training: Introduction to Scikit-learn: Machine learning in Python

  • Bootcamp: Python Fundamentals

  • Bootcamp: Python for Data Acquisition

  • Talk: ML Easel – Tredence’s Data Science and ML Engineering Workbench

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 sessions, 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

  • Python 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 and contributors to many of the tools and languages used in data science today

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

Network at our numerous lunches and events to meet 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. You will train in key areas of data science like deep learning and machine learning, and 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 WEST | November 15th - 18th, 2021

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