Digital Technology Instructor Interview: Rashed Iqbal
I work as Program Manager, Data Solutions in Teledyne Technologies. I did my doctorate in 1994 in Predictive Systems. At that time terms Data Science, Predictive Analytics, and Deep Learning were non-existent or were not popular. The landscape has changed and today skills in these domains are most sought in the job market. In 2014, I proposed Data Science courses at UCLA Extension. I’ve since created and taught a number of courses including Machine Learning, Data Visualization, and Predictive Analytics. I also helped set up Data Science track for Masters in Economics Department at UCLA where I teach as Adjunct Professor. Furthermore, I teach at UC Irvine Extension where I will teach Deep Learning in Winter 2018, a key area of my interest.
Although I am passionate about promoting and teaching Data Science, I am more of a practitioner. I develop solutions that involve Machine Learning and NLP. I’ve started and completed multiple ventures in the domain. My core interest these days lies in Deep Learning and Natural Language Processing (NLP). I program in Python often with NumPy and TensorFlow. I also use PySpark. I am not a programmer but have extensively programmed in C/C++ and R in the past.
While talking about myself, I must mention Agile Methods that is very synergistic with Data Science. I’ve been practicing, teaching, and consulting in Agile Methods for over 10 years.
Is there anything you are currently working towards that you would like to share with us?
I recently started working in an immensely exciting area called Narrative Economics. Introduced formally by the renowned economist Professor Shiller, Narrative Economics studies the impact of the popular narratives and stories on economic fluctuations in the context of human interests and emotions. This domain has a very close relationship with Data Science and NLP.
I am working with a few friends to use NLP to extract narratives in human communication. We are finding Narrative Extraction to be a lot more challenging and interesting compared to Sentiment Analysis which is determining positive, negative, or neutral sentiment in a piece of text. Narrative Extraction is expected to revolutionize the process of human communication to which Narrative Economics is one of the applications.
Tell us about the course you are teaching in fall, Introduction to Data Science, and what students can expect to gain from taking this course:
Introduction to Data Science is the most critical course especially for those who are considering switching career to Data Science. The goal of this course is to expose students to skills including Programming (Python) and Mathematics that are necessary for a Data Scientist. This is not simply a Data Science awareness class. Students will need to program. They will do a basic NLP/human communication-related class project as well. We also discuss Machine Learning, Deep Learning, and Artificial Intelligence.
Today, to learn and do programming, Github and Stack Overflow are standard sources of knowledge. Be ready to explore and learn without hand holding.
What advice do you have for someone who is new to this field?
On last day of my class, I normally tell my students what they learned in the class may already have been obsolete. Data Science no longer is a new field but tools, techniques, programming languages, libraries, and even concepts are evolving very fast. So be ready to continue learning forever. Secondly, if you are switching your field, you will need time and hard work in long terms to become an expert in this field. Remember, you need 10,000 hours to spend in an area before being able to call yourself an expert and Data Science in a multidisciplinary field!
Thirdly, I want to say you can do it. I had a student who attended my Introduction to Data Science course. She was a medical doctor but after attending my class, she decided she wants to become a Data Scientists. After many discussions, she enrolled in Masters in Data Science from UC Berkeley and will be graduating next year. Another student who was an English major with High School mathematics and no programming exposure decided to become a Data Scientist. It took her two years of hard work to accomplish her goal but she is happily employed as a Data Scientist today.
Finally, your knowledge of Sociology and Psychology, and your awareness of a certain domain and of the society in general often are valuable skills in Data Science. For example, your knowledge and awareness about movies and entertainment will help you immensely to become a Data Scientist in this domain. Similarly, if you are a social butterfly, this can help you become a great Data Scientist in the social domain.