5 Building a Data Science Profile

5.2 Events

5.2.1 CareerHub

CareerHub at UTS also has regular events run by UTS Careers and industry partners. This is a great way to meet other students and learn about career opportunities.

5.2.2 Eventbrite

Eventbrite has a wide range of workshops, courses and events. A great way to explore what is happening in the Data Science community near you.

5.2.3 General Assembly

General Assembly runs regular information sessions, panel discussions and courses across Data Science, Analytics, Software Engineering, User Experience Design, Digital Marketing and Web Development.

5.3 Online Networking

Attending these events, however, is not the only way to network. You can keep yourself updated with the latest happenings through social media.

The R community on Twitter can be found using #rstats - this will likely lead you to other niche communities (such as #TidyTuesday for visualisations).

On LinkedIn, you can connect with Data Science influencers, who can be found by following #datascience, #opendata, #deeplearning, #machinelearning, #ai, #artificialintelligence

5.3.1 Looking for some people to follow on LinkedIn?

5.4 Career opportunities

Data Scientists are in demand worldwide and Australia is no different. With big data exploding across industries, data scientists are now the most in-demand professionals in the world. The field of Data Science is growing exponentially thanks to the rise of 5G/IoT, big data, artificial intelligence, and machine learning.

According to study by Deloitte on The Future of work - ‘Occupational and education trends in data science in Australia’, in the age of the fourth industrial revolution, data science and analytics roles have emerged as pivotal roles in organisations, helping companies use their data to gain a competitive edge. The Australian data science workforce is forecast to see sound growth in the next five years. Aggregating the data science occupations identified above, Deloitte Access Economics projects the relevant workforce will grow from 301,000 persons in 2016-17 to 339,000 persons in 2021-22, an increase of around 38,000 workers at an annual average growth rate of 2.4%. The report also shows data scientists who have completed postgraduate study in IT will have an average income of $130,176 between 2021 and 2022, up from a $111,634 average across 2016 and 2017.

According to Dataquest, some key roles within the data science field may include: Operations Analyst, Business Intelligence Developer, Data Scientist, Machine Learning Expert, Data Architect, Data Engineer, Quantitative Analyst, Data Analytics Consultant, Digital Marketing Analytics Manager, Data Project/Program Manager & Transportation Logistics & Data Specialists.

The demand for Data Scientists is so great that there are career opportunities across almost every industry, including: Banking, Finance, Agriculture, Health Care, Medicine & Pharmaceutical, Energy, Manufacturing, Automotive and Transportation, Travel, Media, Mining, Education, Retail, Gaming and Insurance.

Data Science is an exciting career to move into, however there are also some potential challenges associated with the job. Firstly, many companies hire data scientists without a suitable infrastructure in place to start getting value out of AI. In addition, it’s likely that the data science job is only going to provide small incremental gains if the company’s core business is not machine learning. Secondly, this field is still far away from being mature. Data Scientists spent a lot of time convincing people, dodging attacks and rushing along executives to avoid stupid mistakes. Furthermore, leaders often don’t understand what “data scientist” means. This leads the data scientist should be the analytics expert as well as the go-to reporting guy and let’s not forget that you’ll be the database expert too! Beware of companies looking for the Data Science unicorn requiring every skill imaginable!

5.5 Electives

Apart from the obvious factors of assessment tasks, class schedules, and credit points, you will also have to understand how the electives you take up help you in progressing along your career path.

One of the biggest advantages of MDSI is that you can choose any elective being offered by any other faculty as long as the pre-requisites are met and the instructors don’t mind you doing the course. This can also be a disadvantage (as you will soon learn), because you are exposed to a seemingly unlimited number of options and you don’t know what to choose and what not to.

This section talks a little about the different electives you can choose and how each of them relate to career choices.

5.5.3 Domain specific electives

Offered by the Faculty of Health, this subject exposes students to data generated in the healthcare industry and explores principles of good data stewardship, including data processing, data governance, data standards, data privacy and data mining.

Offered by the Faculty of Health, this subject provides a supervised experience for graduate students to learn about making health service decisions using data, by manipulating data assembled to replicate real patient data to both generate and answer questions.

Offered by the Faculty of Design, Architecture and Building, this elective covers core visual interaction tools that are used to support property market research and property visual analytics, as well as an essential understanding of the different visualisation techniques that may be used when dealing with different forms of property data.

Offered by the Faculty of Business, this subject covers the design, management and measurement of services, their operations and supply chain networks using data analytics.

Offered by the Faculty of Business, this subject specifically deals with skills such as performing various multivariate data analyses to solve a variety of marketing-related problems that are critical to marketing theory.

Additionally, if there is a particular area of study that you are interested in, but is not offered by UTS, you can apply to study this specific subject in a different Australian university provided all the conditions are met. You can refer to this for more details.

5.5.4 Planning Electives

For a full list of electives available, refer to the UTS Handbook. To help you plan which electives are available each semester, you can view subject timetables here. The timetable planner also allows you to filter subjects by teaching period, faculty, activity type and even day/time.

5.6 Building your online presence

5.6.1 LinkedIn Profile

LinkedIn is a social network for professionals. Unlike a resume, which is confined by length, a LinkedIn profile allows you to describe your skills, projects, work experience, connect with professionals, participate in groups and apply for jobs. You can go through this guide to improve your LinkedIn profile.

5.6.2 Kaggle Portfolio

Kaggle is a community for data scientists. It’s a crowd-sourced platform to attract, nurture, train and challenge data scientists from all around the world to solve data science, machine learning and predictive analytics problems. It offers interesting and challenging projects where contributors can learn and practice and have insightful discussions with experts.

Build a portfolio to highlight a collection of completed projects that are examples of your data science work. Shape it according to the job profile you want as a data scientist.

If you want to be building systems/engineering/building pipelines (more on the machine learning engineering side) then focus on building end-to-end projects. Show that you can:

  • Come up with good/new ideas (not the place for Titanic!)
  • Finish projects that work (build a working prototype)
  • Clearly articulate the value of what you’ve built to a non-data-scientist

If you’re looking for more of an analysis/data storytelling/measurement/A/B testing job (more like analytics) then you need to show:

  • That you can find new information in data
  • That you can succinctly communicate that information to a variety of stakeholders

5.6.3 GitHub Portfolio

GitHub (Git) is primarily used as a version control system that allows developers to contribute, track and manage changes to a project. However, it can also be used to showcase your work. Maintain repositories for personal projects, kaggle competitions and research projects to demonstrate expertise in different categories (Data Analysis and Visualisation, NLP, Machine Learning, Deep Learning etc.)

5.6.4 Medium and Other Blogs

Communication and presentation are a big part of data science work. Writing about a project or a data science topic allows you to share your work with the community as well as learn from constructive feedback from the community. By exposing your work to the world, you’ll be able to form connections that can lead to job offers, collaborations, and new project ideas.

A workflow to use to get the best of all worlds:

  1. Maintain profile on LinkedIn.
  2. Participate in kaggle competitions, maintain and share your work on kaggle.
  3. Copy your kaggle work, create a GitHub repo and upload it. Make it publicly available to developers and recruiters.
  4. Blog about your work on Medium (other blogs), explain your work and share your work.
  5. Link your blogs and github portfolio to your LinkedIn profile.

Lastly, remember to engage with your MDSI peers, lecturers and participate in the Slack channels.