Looking to accelerate your career in data science? Gain in-demand knowledge immediately transferrable to your work with live online data science programs, powered by Metis.

Introduction to Data Science

Designed for those with a basic understanding of data analysis techniques, looking for hands-on coding experience to take them one step closer to becoming a data scientist. This course provides a well-rounded introduction to data science core concepts and technologies, including basic machine learning principles and hands-on coding experience. Plus you’ll put it all into practice with a mini data science project of your own.

 


“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 class was fantastic. Great instructional material, good course topic coverage and the instructor was great… All in all a very good experience…
I’d highly recommend Metis to anyone seeking to learn.”

MARK HAYES

“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

“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 HAYENS

“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

Learn from world-class data science practitioners

Live online instructors bring deep industry experience and will be available to support you throughout your learning process.

Interact with instructors and classmates in real-time

Ask questions, participate in discussions and join your course Slack channel for maximum engagement, collaboration and support.

The benefit of online learning with live instruction

Log in from wherever you are to access live online classes. If you miss a class or need to refer back, recordings are available 24/7.

Available in late-2020

Special Introductory Price: $990

The live online Introduction to Data Science will be available for enrolment in late-2020. Please click here to register your interest and we’ll let you know when classes are open for enrolment.

Overview

COURSE TOPICS:

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

COURSE 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 labelled and unlabelled 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.

Course Structure and 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 Visualisation

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 Modelling: 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 unlabelled data.

WEEK 4

Data Modelling: 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? What can be done (principal component analysis/independent component analysis, regularisation) to create and use them given the data at hand? We also cover how to construct complete data pipelines, going from data ingestion and pre-processing to model construction and evaluation.

WEEK 5

Data Modelling: 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 Modelling: 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 generalisable as possible. Students complete data science projects and share learnings and discoveries.

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 v3 is currently used in the course.

To ensure everyone begins the course on the same page, students will be encouraged to complete approximately 8 hours of pre-work before the first day of instruction.

Students will need a Github account to get access to the content and a Slack account to collaborate with their instructor and peers. Sign-up is free, fast and easy.

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

Download a brochure

Please complete the form to download a brochure. We’ll also send you a 1-hour sample class so you can see what the live online experience is like for our data science courses.

Gordon Dri, Data Scientist at Oracle, and instructor of the live online Beginner Python & Math for Data Science course, will cover the following sample topics in the sample class:

 

How to set up Jupyter Notebook and begin to write basic Python code

 

A brief overview of a fundamental math topic from the course

Frequently asked questions

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.

No, you’ll receive a certificate of completion stating you’ve completed the course. 

While there’s 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. There will be lab/office hours outside of class during where students and the instructor can collaborate.

Course instructors are from the industry and have real-world experience as practitioners of data science. Please visit the respective course pages for specific information on each instructor’s background and current job.

The course runs two nights per week over 6 weeks, totalling 36 hours of instruction.

All classes are delivered live online via Zoom. Course content is accessed via GitHub, and Slack is used to provide support, as well as facilitate collaboration with the instructor and your peers. Details on how to access these platforms will be provided in your welcome email upon enrolment.

The live online format allows you to attend class sessions from anywhere with a stable internet connection. Unlike other online options, where sessions may be pre-recorded, the live online format allows for interaction with the instructor, teaching assistant and other participants.

The curriculum will be provided via Github, so 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.

To complete the Introduction to Data Science course, you’ll require a computer or laptop and a stable internet connection to attend online sessions via Zoom. Python is a course requirement. 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.

Course content is accessed via GitHub and Slack is used to provide support, as well as facilitate collaboration with your instructor and classmates. Sign-up to these platforms is free and details on how to access them will be provided in your welcome email on enrolment.

Python is a widely used and rapidly growing open-source programming language, commonly used by data scientists, data analysts and software engineers. Unlike Excel, Python is scalable, and is better able to meet business needs by readily handling massive data sets and accommodating the demands of real-time analysis and collaboration. In addition to being immensely popular, Python is a straightforward and user-friendly programming language, making it very easy to learn.

Research shows there’s growing demand for qualified individuals for positions in the data science field. This is largely driven by the exponential growth of available data and the narrow set of specific skills required to extract value from that data. Recent estimates suggest 2.5 quintillion bytes of data are created each year, with 90% of all data in existence being created in just the last two years1.  As a result, the number of data-related job postings has surged and median salaries have risen, leading to ‘data scientist’ becoming the best job in a 2016 Glassdoor survey based on the number of job openings, salary and job satisfaction1.

External data continues to validate the demand for the types of programs Metis offers. For example:

  • According to a recent global survey, 76% of businesses plan on increasing investment in analytics capabilities over the next two years1.
  • The most common salary for data scientists in Australia is between $120,000- $140,0002.
  • The projected job growth for data scientists in Australia between 2019 and 2024 is 12.9%2.

 

1 Deloitte Access Economics, 2018, The future of work: Occupational and education trends in data science in Australia.
2
Seek, 2020, How to become a Data Scientist- Salary, Qualifications & Reviews.

Yes, you can access recordings of live sessions at any time if you’re unable to attend classes. To get the most out of your learning experience it’s strongly advised you attend all live online classes. This ensures you can interact and participate in discussions and ask questions.

Introduction to Data Science is designed for those with a basic understanding of data analysis techniques. It provides a well-rounded introduction to the core concepts of basic machine learning and hands-on coding experience.

To complete the course, you should have some familiarity with basic statistical and linear algebra concepts such as mean, median, mode, standard deviation, correlation, and the difference between a vector and a matrix. Python is also a requirement for the course, so it will be helpful to know basic data structures such as lists, tuples and dictionaries, and what distinguishes them.

If you don’t meet the course prerequisites, it’s recommended you complete Beginner Python and Math for Data Science before progressing to Introduction to Data Science.