Starts Monday! Beginner Python & Math for Data Science Course. Enroll Now

Accredited Professional Development Course

Introduction to Data Science

Offered Live Online & In Person

This course serves as an introduction to the data science principles required to tackle real-world, data-rich problems in business and academia, including:

  • Data acquisition, cleaning, and aggregation
  • Exploratory data analysis and visualization
  • Feature engineering
  • Model creation and validation
  • Basic statistical and mathematical foundations for data science

Course designed by Sergey Fogelson, VP of Analytics and Measurement Sciences, Viacom

Who the course is designed for:

You have a strong desire to learn data science through top-quality instruction, a basic understanding of data analysis techniques and an interest in improving their ability to tackle data-rich problems in a systematic, principled way. This course provides structure and accountability to ensure you stay on track, finish strong, and achieve your desired outcomes.

Outcomes

  • An understanding of problems solvable with data science and an ability to attack them from a statistical perspective.
  • An understanding of when to use supervised and unsupervised statistical learning methods on labeled and unlabeled data-rich problems.
  • The ability to create data analytical pipelines and applications in Python.
  • Familiarity with the Python data science ecosystem and the various tools needed to continue developing as a data scientist.
Have questions? Get answers to frequently asked questions. FAQs

What you'll receive upon completion:

  • Certificate of completion
  • Certificate link and instructions on how to add to your LinkedIn profile
  • 3.3 Continuing Education Units

Dates & Instructors

New York City
$2,100

Introduction to Data Science

June 4 to July 11

Mondays and Wednesdays

6:30 - 9:30pm

Sergey fogelson
Sergey Fogelson
Instructor

Sergey Fogelson is the vice president of analytics and measurement sciences at Viacom. He began his career as an academic at Dartmouth College in Hanover, New Hampshire, where he researched the neural bases of visual category learning and obtained his Ph.D. in Cognitive Neuroscience. After leaving academia, Sergey got into the rapidly growing startup scene in the NYC metro area, where he has worked as a data scientist in alternative energy analytics, digital advertising, cybersecurity, finance, and media. He is heavily involved in the NYC-area teaching community and has taught courses at various bootcamps, and has been a volunteer teacher in computer science through TEALSK12. When Sergey is not working or teaching, he is probably hiking. (He thru-hiked the Appalachian trail before graduate school).

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Prerequisites

Students should have some familiarity with basic statistical and linear algebraic concepts such as mean, median, mode, standard deviation, correlation, and the difference between a vector and a matrix. Additionally, Python is a requirement for the course. In Python, it will be helpful to know basic data structures such as lists, tuples, and dictionaries, and what distinguishes them (that is, when they should be used). Python v2 is currently used in the course.

To ensure everyone begins the course on the same page, students are encouraged to complete the following pre-work (approximately 8 hours) before the first day of instruction:

  1. Exercises 1-7, 13, 18-21, 27-35, 38,39 of Learn Python The Hard Way.
  2. Videos 1-6 of Linear Algebra review from Andrew Ng’s Machine Learning course (labeled as: III. Linear Algebra Review (Week 1, Optional).
  3. The exercises in Chapters 2 and 3 of OpenIntro Statistics.
Considering our immersive data science bootcamp?

Professional development alumni can apply the amount of tuition paid for one part-time course towards enrollment in an upcoming bootcamp upon admittance.

Course Structure & Syllabus

Week 1
CS/Statistics/Linear Algebra Short Course
We start with the basics. For CS, we briefly cover basic data structures/types, program control flow, and syntax in Python. For statistics, we go over basic probability and probability distributions, along with general properties of some common distributions. For linear algebra, we cover matrices, vectors, and some of their properties and how to use them in Python.
Week 2
Exploratory Data Analysis and Visualization
We spend a considerable amount of time using the Pandas Python package to attack a dataset we’ve never seen before, uncovering some useful information from it. At this point, students decide on a course project that would benefit from the data-scientific approach. The project must involve public (freely-accessible and usable) data and must answer an interesting question, or collection of questions, about that data. (Several resources of free data will be provided.)
Week 3
Data Modeling: Supervised/Unsupervised Learning and Model Evaluation
We learn about the two basic kinds of statistical models, which have classically been used for prediction (supervised learning): Linear Regression and Logistic Regression. We also look at clustering using K-Means, one of the ways you can glean information from unlabeled data.
Week 4
Data Modeling: Feature Selection, Engineering, and Data Pipelines
We switch gears from talking about algorithms to talk about features. What are they? How do we engineer them? And what can be done (Principal Component Analysis/Independent Component Analysis, regularization) to create and use them given the data at hand? We also cover how to construct complete data pipelines, going from data ingestion and preprocessing to model construction and evaluation.
Week 5
Data Modeling: Advanced Supervised/Unsupervised Learning
We delve into more advanced supervised learning approaches and get a feel for linear support vector machines, decision trees, and random forest models for regression and classification. We also explore DBSCAN, an additional unsupervised learning approach.
Week 6
Data Modeling: Advanced Model Evaluation and Data Pipelines | Presentations
We explore more sophisticated model evaluation approaches (cross-validation and bootstrapping) with the goal of understanding how we can make our models as generalizable as possible. Students complete data science projects and share learnings and discoveries.

Live Online Interactive Learning

Learn from world-class data science practitioners.

Our Live Online instructors bring deep industry experience from a broad range of industries and companies including Viacom, Spotify, and Capital One Labs. You’ll have an Instructor and Assistant Instructor to support you throughout your learning process.

Interact with instructors and classmates in real-time.

This course is truly live, which means you can interact with the instructors and your fellow students in real-time. Stay engaged by asking questions and participating in polls and conversations, and join your course Slack channel for additional support, communication, and collaboration.

Learn online without sacrificing the value of live instruction.

The world is your classroom. Log in from wherever you are and gain access to live, interactive data science instruction that will push your career further in the right direction. In case you have to miss a class, you can access all recordings 24/7 to stay caught up and refer back.

Earn CEUs for accredited courses.

Not only will you walk away with new data science skills and knowledge, you’ll also earn up to 3.3 Continuing Education Units (CEUs). Our courses are accredited by ACCET, who requires we maintain high standards in areas such as quality of instruction and positive student feedback.

Register for an on-demand sample class

Our 1-hour on-demand sample class is a great way to preview what the Live Online experience is like.

Trent Hauck, an instructor of the Live Online Introduction to Data Science course, will cover a few sample topics in the on-demand class:

  1. Using the jupyter notebook
  2. Python primitive types
  3. Python lists
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The class was an excellent introduction to this topic, and I now feel so much more prepared to continue learning about data science on my own.
Laura Pederson
The Intro to Data Science class provides a great overview on Data Science topics. The instructor clearly explains the main points and always takes extra mileage to help us to understand the issue whether in the class or after hours via group chat. I am really impressed on the lectures and have learned a lot from this intensive class.
Linda Fu
As a BI professional with 20+ years experience, I found the Metis Intro to Data Science course to be exactly the shot in the arm my career needed to upgrade my skills.
Ronald Haynes
I felt supported in my journey into Data Science throughout the class. I started the class with only a bare minimum knowledge of Python and ended knowing how to create several machine learning models in the span of six weeks. I'd say that's a win. My only regret was that it ended so quickly.
Drace Zhan
The Metis Intro To Data Science course was the most interactive online course I’ve ever taken. The instructor and TA made the class feel like we were all in the same room, and we built up a really supportive community over the six weeks. This made learning a huge amount of material much more manageable.
Allison Hegel
PhD Student
Incredible how much information [the instructor] could get through in a 3-hour period. He also did a great job providing relevant examples, and talking to more than just the syllabus to enhance understanding.
Adam Watson

FAQs

Have more questions? No problem. Schedule a chat with admissions