During undergraduate studies, Steve was a proactive student who engaged and won multiple programming competitions including a National Olympiad. After graduating with a Bachelor of Software Engineering, Steve started working as a junior software engineer and within a couple of years, he got promoted to the Lead Developer role. He has spent more than 7 years designing and developing software before being introduced to data science, then studied Master of Data Science at the University of Technology Sydney which helped him to get involved in academia to deepen his knowledge about algorithms and techniques. During postgraduate studies he participated and won several competitions including International GovHack and NASA Hackathons.
Meanwhile, his focus was on research and tutoring at the university for about 2 years that exposed him to the cutting-edge AI technologies and people who drive them. Having the substantial professional experience and graduating with Academic Excellence and the Dean’s List Awards secured him a senior role in a Fintech start-up, he is now the Lead Data Scientist at Nod. which is an AI platform for delivering real-time financial advices.
How did you get into Data Analytics? What interested you in learning Data Analytics?
Steve Nouri: I have been always involved with transforming and querying the structured data and intrigued by the power of it. My journey formally started a couple of years ago when I was part of a team building an Electronic Health Records and as the development team leader I had to come up with a flexible way to generate dynamic reports which became my introductory point to the world of data science.
What was the first data set you remember working with? What did you do with it?
Steve Nouri: When I was studying Master of Data Science, there was an in-class Kaggle competition using the bank clients’ data set predicting whether they will accept the loan or not. I remember using whatever tricks and techniques to climb on the leader board including denoising, feature engineering and visual analysis. Finally, by making the ensemble model combining several classifiers with the legendary xgboost algorithm my submission ended up on the top of the board.
Was there a specific “aha” moment when you realized the power of data?
Steve Nouri: I had the privilege to be part of a high-profile team of scientists and professors who were trying to find the genes related to paediatric cancer. I was responsible for dimensionality reduction of gene expression dataset using an innovative deep learning algorithm which was highly suffering from curse of dimensionality. The moment I put on the 3D glasses with a joystick in hand to explore the clustered visualization of patients’ data at UTS 360-degree data arena was one the epic moments for me and made a very powerful image in my mind.
What is your typical day-in-a-life in your current job. Where do you spend most of your time?
Steve Nouri: It starts with a morning stand up briefing about today’s activities. The biggest chunks of my days are spent on research, design and implementation of innovative AI algorithms but the rest of the day varies based on the situation, generally, contains mentoring the AI team, some meetings with other teams, potential investors, clients and executives.
How do you stay updated on the latest trends in Data Analytics? Which are the Data Analytics resources (i.e. blogs/websites/apps) you visit regularly?
Steve Nouri: The world of AI and Data Science are evolving very rapidly and it’s challenging to catch up with all the updates. Yet, I constantly check out and follow game changers and pioneers, academic publications, professional communities, seminars and conferences in my areas of interests. Specially, International Conference on Machine Learning (ICML) and Conference on Neural Information Processing Systems (NIPS) which are my favourite go to conferences.
Share the names of 3 people that you follow in the field of Data Science.
- Andrew Ng
- Yoshua Bengio
- Ian Goodfellow
Team, Skills and Tools
Which are your favourite Data Analytics Tools that you use to perform in your job, and what are the other tools used widely in your team?
Steve Nouri: We mostly use python (lots of different libraries including NLTK, Spacy, TensorFlow, Keras ) as the main data science programming language, Tableau for visualization, Knime for rapid exploratory analysis and also Excel.
What are the different roles and skills within your data team?
Steve Nouri: Machine Learning Engineer, Data Analyst and Data Scientist.
Help describe some examples of the kind of problems your team is solving in this year?
Steve Nouri: As I said before, Nod. is an NLP, Artificial Intelligence platform that generates financial advices in real-time using corpus of historical advices so optimizing the accuracy of generated financial advice and the performance of NLP parser are two of the most important tasks that always have the priority.
Advice to Aspiring Data Scientists
How do you measure the performance of your team?
According to you, what are the top skills, both technical and soft-skills that are needed for Data Analysts and Data Scientists?
Apart from having the routine soft skills every IT employee should have, curiosity, patience and perseverance are the key soft skills, others are:
- Critical thinking
- Data architecture
- Risk analysis, process improvement, systems engineering
- Problem-solving and good business intuition.
Some important technical skills for a good data scientist:
- STRONG — Machine Learning and AI.
- STRONG — Math/Statistics.
- STRONG — Computer Science/Ability to sling code.
How much focus should aspiring data practitioners do in working with messy, noisy data? What are the other areas that they must build their expertise in?
Steve Nouri: It’s literally the kind of data set they will end up using at industry, so better be prepared for real life challenges and instead of getting stuck with the iris dataset they need to find some raw datasets and prepare them themselves.
Apart from Data Preparation, Data visualization and storytelling are also helpful for junior data scientists to secure their first job.
What is your advice for newbies, Data Science students or practitioners who are looking at building a career in Data Analytics industry?
Steve Nouri: I believe math and stats are the foundations for their future success and it’s crucial to devote enough time and effort mastering fundamentals. Don’t forget using MOOCs and data science blogs that are providing easy to understand and in many cases free learning materials
- Programming and software skills: R, Python, SAS, Hive, SQL, Pig, Spark, Hadoop
- Visualization Tools: Tableau, Power BI
- Statistical foundation and applied knowledge: Calculus, Applied Stats, Leaner Algebra
- Machine Learning: Decision Tree, Random Forest, Neural network, Regression
What are the changing trends that you foresee in the field of Data Science and what do you recommend the current crop of data analysts do to keep pace?
Steve Nouri: AI is understanding and solving more complex cases very fast and the trend is towards interacting closer with human and substituting and enhancing most of the current human centric activities.
Anyone who wants to stay current in this field needs to allocate big chunk of their life following and performing the research.
Would you like to share few words about the work we are doing at Digital Vidya in developing Data Analytics Talent for the industry?
Digital Vidya is a very valuable resource for anyone who is eager to learn and upskill in Data Science world. I started pretty much around the time D.J. Patil published the article “Data Scientist: The Sexiest Job of the 21st Century” at Harvard business review and resources were very limited but not easily accessible and nicely organized. Digital Vidya is also doing a great work collecting and sharing insights from experienced professionals.
To know more about Steve Nouri, you can check out his LinkedIn.
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