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Social distancing may be keeping us in our homes, but communities like TechLadies will always find ways to thrive. This May, amid the extended Circuit Breaker, Giselle, Vanessa, and Ning from our Events Team delivered our first completely virtual event – a livestream brunch discussion with Jana Marlé-Zizková, co-founder of Meiro and SheLovesData.
Thanks to organizations increasingly embracing the role of data in their decision-making process, working as a data scientist has quickly become The Sexiest Job of the 21st Century. In Singapore, where demand for tech jobs grew by 20% since 2018, data science is one of the 10 most sought after skill sets. However, is it the only data role worth pursuing? Together with Jana and Giselle we dove deep into the topic, looking at industry trends, structure of data teams, and different job roles in the area of data to answer this question.
Data Project Roles
Data consultancies and internal data teams can deliver immense value to organizations. With the global shift from offline to online (amplified by the crisis in the past few months), companies got access to incredible amounts of information about their customers’ behaviour, preferences, and needs. According to Jana, by treating data as an asset and creating data availability for business users – by implementing technologies that help translate raw data into readable information – data teams help businesses make strategic decisions that will have a positive effect on the bottom line.
Using her expertise as a data consultant, Jana explained how different roles work together to implement data-oriented solutions in organizations. There are many roles on a data project, taking care of all aspects of the implementation process:
CTO (Chief Technology Officer) – verifies the architecture behind the solution
CDO (Chief Data Officer) – responsible for data strategy as a part of an overall business strategy
Business experts – define business needs of the organization
Business consultant – bridge between the business and the IT team
IT, security, and data compliance – decide how to securely access the data
Data Security officers – defining rules and data governance procedures which may be country- and company-specific
Data engineers – technical team taking care of the architecture, finding secure and efficient ways to get and clean the data
Data analysts – working on the final form of the data that the client will see
UX and UI teams – last mile delivery and the look and feel of the product
Client services director – project manager, managing all communication with the client and the team
Data Scientist, Data Analyst, Data Engineer – what is the difference?
Project team breakdown made the distinction between different roles much clearer, but the three roles – data scientist, data analyst, and data engineer – are so frequently confused we discussed them in a bit more detail.
- Data engineer – often forgotten title. Critical role, basically equivalent to a database administrator. Understands the principles of ETL: how to extract, transform, and load the data. Data engineers build and maintain the logic of the pipeline so the data is ready for future analysis. They also control how we store and move the data. Skills: SQL, Python, R, Apache Hadoop, databases, pipelines, and structuring data for further use in the organization.
- Data Analyst – needs to understand ETL, know where the data is coming from, if it’s clean, structured or unstructured. Performs simple transformations. Skills: SQL, Python, R or any other language used for simple data transformation, data visualisation tools such as Google Data Studio, Tableau, Power BI, DOMO.
- Data Scientist – works on mining and defining data, creates algorithms and models, “teaches” machines how to find valuable information, allowing humans to find hidden patterns in data. Skills: Statistics, mathematics, programming; understanding business problems; machine learning, deep learning, text analysis.
How to get started?
As it’s always the case with starting in a new industry, you need to begin with some research. “There are many free resources out there,” Giselle advised data newbies. “Get into fundamentals, try a Statistics 101 refresher, try different languages, find one you are most comfortable with – this way you will quickly know if this is your cup of tea and which areas you’d like to study in more depth.” If you’re not sure which languages to choose, SQL and Python are always a great starting point. While SQL is the key tool when going into the field of data, Python is a versatile programming language with a great community, many free resources, and groups like PyLadies to reach out to for support.
Practical experience, says Jana, means more than certificates and diplomas. Being comfortable with SQL is a must and a good starting point for going into a data job. “It doesn’t matter if you’re self-taught,” assures Jana. “If you have the passion and curiosity – join study groups, get an internship, work somewhere for 3-6 months to gain experience. This will be your stepping stone to a junior analyst job.” According to Jana, certain skills and attitude are more important than formal education. Critical thinking, following correct logic, as well as understanding the circumstances of the client and the context of their industry can get you a long way. Building a GitHub portfolio can also help you land your first job.
Jana gave a few examples from her own team at Meiro. Her colleagues come from all walks of life and educational backgrounds: mathematics and statistics, software testing, project management. What they all have in common is passion for data and life-long learning. They relied on many different resources to build their skill sets: Udemy and Udacity online classes, data science courses provided by the Singaporean Government, learning through work experience and small projects.
What matters most is that you never stop learning. Data is an industry that never stops and always keeps you searching for new information – you will need to have patience and perseverance if you are considering this field. To stay relevant, you will have to be open to new, emerging fields such as data science applications in retail, education, human capital analytics, safety and security, marketing, and business administration. For those considering a career switch, your understanding of a specific domain area and the characteristics and challenges of an industry might be your greatest advantage and give you a leg up when searching for a data job. Build your domain expertise from your past experience and add your data skillset on top of that.
Even if you don’t consider yourself a techie but a business user, building basic data literacy can be a valuable addition to your skillset. If current trends are any indication, organizations will shift from lengthy written reports to data visualisation tools. Learning the basics of SQL or software such as Google Data Studio or Tableau will allow you to communicate better with analysts and data specialists in your organization, understand your KPIs better, and make more informed business decisions. It will also open the door to some of the emerging roles such as marketing technologist that bridge the gap between tech and business.
TechLadies Brunch will be back soon – follow us on Facebook to keep an eye on future event announcements. To watch the recording of our full interview with Jana, visit our Facebook group (available to member’s only). If you want to share your feedback about the event with us, complete this short survey: https://tinyurl.com/TLlivestreamJana.