Editor's Note: Kirk will present his talk "Adapting Machine Learning Algorithms to Novel Use Cases" at ODSC West 2019. If there was a metric for success in the data science profession, it would require a multi-dimensional scoring model. This metric would cover a data scientist's technical skills and talents, analytic literacies and ways of thinking, and soft skills and aptitudes. Soft skills include a collection of aptitudes that I call the "seven C's of successful data scientists": Collaboration (data science as a team sport), Communication (data storytelling), Computational thinking, Critical thinking, Creativity, Curiosity, Continuous lifelong learning, Complex problem-solving, Compassion (design thinking), Consultative (active listening), Community-focused, and Cool under pressure ("tolerance for ambiguity"). Okay, that's more than seven things, but they represent my perspective on the journey to data science maturity as "sailing on the seven seas".

I was recently asked five questions by Alex Woodie of Datanami for the article, "So You Want To Be A Data Scientist" that he was preparing. He used a few snippets from my full set of answers. The longer version of my answers provided additional advice. For aspiring data scientists of all ages, I provide here the full, unabridged version of my answers, which may help you even more to achieve your goal. My number one piece of advice always is to follow your passions first.

Neural one-unit learning rules for the problem of Independent Component Analysis(ICA) and blind source separation are introduced. In these new algorithms, every ICA neuron develops into a separator thatfinds one of the independent components. The learning rules use very simple constrained Hebbianjanti-Hebbian learning in which decorrelating feedback may be added. To speed up the convergence of these stochastic gradient descent rules, a novel computationally efficientfixed-point algorithm is introduced. 1 Introduction Independent Component Analysis (ICA) (Comon, 1994; Jutten and Herault, 1991) is a signal processing technique whose goal is to express a set of random variables aslinear combinations of statistically independent component variables. The main applications of ICA are in blind source separation, feature extraction, and blind deconvolution.

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