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.
What you'll receive upon completion:
- Certificate of completion
Dates & Instructors
Check back soon for our next scheduled course.
Prerequisites
Students should have some experience with Python and 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. 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). Students should skip the pre-work if they can accomplish all of the following:
- Write a program in Python that finds the most frequently occurring word in a given sentence.
- Explain the difference between correlation and covariance, and why the difference between the two terms matters.
- Multiply two small matrices together (e.g. 3X2 and 2X4 matrices).
Otherwise, students should complete the following pre-work (approximately 8 hours) before the first day of class:
- Exercises 1-7, 13, 18-21, 27-35, 38,39 of Learn Python The Hard Way. (If the link is outdated, you can access the main website here.)
- Videos 1-6 of Linear Algebra review from Andrew Ng’s Machine Learning course (labeled as: III. Linear Algebra Review (Week 1, Optional).
- The exercises in Chapters 2 and 3 of OpenIntro Statistics. (This book is free, but there is a suggested donation. Feel free to donate an amount or set it to zero.)
Students must have a Github account to get access to the content. Sign-up for an account on their site is free, fast and easy.
Course Structure & Syllabus
Week 1
CS/Statistics/Linear Algebra Short Course
Week 2
Exploratory Data Analysis and Visualization
Week 3
Data Modeling: Supervised/Unsupervised Learning and Model Evaluation
Week 4
Data Modeling: Feature Selection, Engineering, and Data Pipelines
Week 5
Data Modeling: Advanced Supervised/Unsupervised Learning
Week 6
Data Modeling: Advanced Model Evaluation and Data Pipelines | Presentations
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.
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.
Nathan Grossman, an instructor of the Live Online Introduction to Data Science course, will cover a few sample topics in the on-demand class:
- A brief overview of K-means clustering
- Marketing application for K-means clustering
- An example in Python
FAQs
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For this course, do I need to know Python. If so, how much Python do I need to know to take this course? What version of python is used?
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 v3 is currently used in the course.
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How much do part-time courses at Metis cost?
Live online bootcamp prep courses cost $750.
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Do I receive pass/fail grades on completion of a part-time course?
No, you receive a certificate of completion stating that you completed up to 36 hours of the course. Hours vary by course.
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How much homework is required outside of class time?
While there is no official homework, you can expect to spend a minimum of 3 hours per week reviewing material or working on projects. The non-class time spent will depend on your background and the course itself. Each instructor will address this on the first day of class, and there will be lab/office hours outside of class during which students and the instructor can collaborate.
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Will we work on any projects? What are some examples of projects students have completed in the past?
Students work on a final project in this course. Here is an example project, which analyzes the likelihood of pets getting adopted in shelters, and here’s another example about predicting star ratings on Yelp.
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Do I get career support if I take a part-time course?
No. We do not offer career support for students of these courses like we do for our bootcamp students, but you will gain access to our alumni community network of 1000+ data scientists. Networking events and job opportunities are posted on a regular basis in this active digital community.
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Who are the instructors for the part-time courses? Are they bootcamp instructors? What are their backgrounds?
Our part-time course instructors come to teach at Metis from industry and are not bootcamp instructors. Please visit the respective course pages for specific information on each instructor’s background and current jobs.
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How often do Metis part-time courses meet?
Our part-time courses typically run two nights per week over the course of 6 weeks, totaling 36 hours of instruction, but this can vary. Please see the full schedule here for the most up-to-date information. We consistently add new courses, so be sure to check back routinely.
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Does Metis offer any part-time courses online?
Yes, we currently offer a rotating selection of our part-time bootcamp prep courses in a Live Online format, meaning once registered, you can login from anywhere to learn.
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What are the benefits of taking a course in a Live Online format?
The beauty of the Live Online format is that you’re taught by our industry-leading instructors live, but you can attend class sessions from literally anywhere you have an internet connection. Unlike some other online course options out there, which might consist of pre-recorded lectures, our courses allow for interaction with the instructor, teaching assistants, and other students – and because these are on a set schedule, you’ll be held accountable to actually attend, do the work, and learn the material (which is what you’re really here for anyway!).
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I took a Metis part-time course and now want to apply for the bootcamp. Does that give me a competitive edge?
It does not, simply because we evaluate each and every bootcamp applicant the same way in order to ensure fairness.
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How will I receive access to the curriculum?
The curriculum will be provided via Github; therefore, you must register a Github account. Sign-up for an account on their site is free, fast and easy. Github is a web-based hosting service for version control using Git.