Data Science - my journey so far

A journey that started during COVID lockdowns has come to its academic conclusion with these lines that brings joy from successful completion of the degree as well as from learning about the opportunities and challenges for applying state of art theory, techniques, and tools to real world business needs.

What is Data Science? What makes one a Data Scientist? What makes one a Knowledge Scientist? What is the difference between Machine Learning and Artificial Intelligence? how much Big Data would I need to collect, wrangle and process? what will happen if I don’t use Big Data for my assignments and only relied on small data to apply the theory? how much of Mathematics and Statistics would I need to re learn to understand Data Science? Do I need to learn advanced Python, R, MATLAB, Java, PyTorch, SQL, PySpark coding skills to get the most from the learning experience? what role does Data Visualization play in Machine Learning? would it all be about numbers or text and languages too? all these questions and more were progressively answered during the journey. I thank City St George’s, University of London Data Science department and all lecturers , professors Aidan Slingsby @Calos Reyes Aldasoro Ernesto Jimenez-Ruiz Artur d'Avila Garcez Tillman Weyde @Kevin Ryan Pranava Madhyastha @Natalia Andrienko @Gennady Andrienko @Ahmad Abu-Khazneh @olga galkina @Sofoklis Kyriakopoulos @Daniil Osudin for the support and encouragement during the course. Thank you as well James Ashdown and all Full Time and Part Time Students for our study group chats , friendships and constant encouragement during the submission deadlines.

Special thanks to @ Oleksandr Shevchuk Galkin for the Data Bites sessions. They are awesome. Thank you to all past speakers and the future ones for keeping it alive.

I had taken a special interest in Timeseries datasets during the early part of this journey focussing on assignments for prediction of GDP and Asset pricing using Neural Networks (LSTM, CNN), Digital Signal Processing techniques (Fast Fourier Transform), Statistical Models (Random Forest, Na?ve Bayes) but towards the end of my journey, Knowledge Management and Natural Language Processing took over. Whilst the beginning was all about predictive ML and Data Visualisation, the end was very much about Logic, Reasoning and the Science of Knowledge Management through Ontologies and Network Graphs

None of my questions at the beginning of this journey was about the need or importance of understanding human vision, processing of sound or about what makes us - rational, logical, conscious or the role it might play in my learning journey but, this degree was as much about understanding how human see, hear, perceive, rationalise, decide as it was about modelling those attributes and features.

A degree in Data Science brings together the disciplines from so many domains of Formal, Natural and Social Sciences that I certainly will miss listing them all even if I tried.

All budding Data Science students or Data Science practitioners feel reach out if you want to discuss more or if you are thinking about undertaking a similar journey. If you love to read , I might have book / site recommendations for all levels and all types of Data Science applications. If you love to code, lets join forces ??

Joseph M. Marshall III?— 'Life is a circle. The end of one journey is the beginning of the next.'

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