{"id":938,"date":"2018-10-23T03:16:37","date_gmt":"2018-10-23T00:16:37","guid":{"rendered":"http:\/\/kusuaks7\/?p=543"},"modified":"2021-12-15T04:42:17","modified_gmt":"2021-12-15T04:42:17","slug":"whats-the-secret-sauce-to-transforming-into-a-unicorn-in-data-science","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/whats-the-secret-sauce-to-transforming-into-a-unicorn-in-data-science\/","title":{"rendered":"What\u2019s the secret sauce to transforming into a Unicorn in Data Science?"},"content":{"rendered":"

Ready to learn Data Science? Browse\u00a0Data Science Training and Certification<\/a> courses developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n

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Roles in analytics and picking up the skills to become \u2018priceless\u2019<\/h3>\n

Every aspirant in data science has the question\u00a0\u201cWhat skills do I need to enter the industry?\u201d<\/em>, closely followed by\u00a0\u201cHow do I become highly sought after in this job market?\u201d<\/em>\u00a0While the industry is hot with a skewed demand-supply that\u2019s in favour of trained professionals, getting the mix of skills right is not easy.<\/p>\n

Now it\u2019s common knowledge that\u00a0\u2018data scientist<\/em>\u00a0is the sexiest job of the century\u2019<\/em>. But what role does this exactly refer to? The very mention of this title conjures up images of math wizards sweating it out in multivariate calculus and linear algebra, or of geeks coding to create the next general artificial intelligence.<\/p>\n

And then, one is also thrusted upon with busy venn diagrams that call for mastery of a laundry list of skills. These add up to areas that a team of people may have mastered amongst them, over years.\u00a0Data scientist<\/em>\u00a0is a loosely used term, a title that\u2019s heavily abused in the industry. Quite like\u00a0Big Data\u00a0<\/em>or, say\u00a0AI<\/em>.<\/p>\n

In practice, the title is often used as an umbrella term for related roles and is variously interpreted by companies in the industry. I\u2019ve come across many people who\u2019ve confessed to me in private,\u00a0\u201cGive me any job and role, but please coin me a job title with some play of these 2 words \u2014 \u2018data\u2019 and \u2018scientist\u2019<\/em>!<\/p>\n

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Photo by\u00a0frank mckenna\u00a0on\u00a0Unsplash<\/a><\/p>\n

So, what does it take to enter and succeed in Data\u00a0science?<\/h4>\n

Fuelled by such confusions, people wonder whether they must learn programming to have a go at a career in data science. For others, statistics or machine learning may not be their cup of tea. These then appear to be stumbling blocks for making any advances into the analytics field.<\/p>\n

This particularly perplexes laterals who have developed an interest in data, but, say have 10 years in an unrelated role, in a different industry. The assumption of having to learn coding or design afresh to restart their career stumps them. These misconceptions must be forcefully put to rest, lest they continue crushing dreams of a career in data science.<\/p>\n

So, what\u2019s a realistic expectation on the skills needed to make a career in data science? And, can aspirants pick and choose skills of interest to carve out a preferred role, one that builds on strengths, while also being in demand?<\/p>\n

Yes!<\/strong><\/p>\n

We\u2019ll first present the spectrum of skills that are needed in data science, like a buffet menu. Then we\u2019ll construct the key industry roles that deliver analytics value, by picking and choosing from amongst these skills, like a customised meal. And yes, we\u2019ll also unwrap the secret sauce to becoming a unicorn in this industry.<\/p>\n

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A buffet menu of Data science Skills (Photo by\u00a0Dan Gold<\/a>\u00a0on\u00a0Unsplash<\/a>)<\/p>\n

The spectrum of Skills in Data\u00a0science<\/h4>\n

There are 5 skills that are central to data science. To emphasise this again, no, one doesn\u2019t need to learn them all. We\u2019ll cover the roles and the mix of skills that each role entails, in the next section. First, lets talk about the complete listing of competencies needed in a project, in order to deliver business value.<\/p>\n

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Spectrum of the key data science\u00a0skills<\/p>\n

1. Data Bootstrap skills<\/strong><\/h4>\n

Passion for numbers is a pre-condition for success in data science, and a great asset. One must pickup data wrangling skills to get a feel for data \u2014 compute averages, fit cross tabs and extract basic insights through exploratory analysis. It\u2019s the approach that matters and any tool, say Excel, R or SQL will do.<\/p>\n

Insights from analysis and results of data techniques are like an unpolished diamond. They are valuable to a trained eye, but worthless in a marketplace. Its invaluable to pickup the presentation and basic design skills to polish those nuggets of insights. This makes one\u2019s efforts effective and worthwhile.<\/p>\n

Data handling and basic design must be topped up with a good orientation of a chosen domain. Techniques with data are only as good as their adaptation to a business problem. This basic knowledge can\u2019t be outsourced to a business analyst, so anyone serious about data science should pickup domain basics.<\/p>\n

In short, this skill requires one to befriend data and train their eyes to spot patterns in numbers. This is a fundamental skill that is non-negotiable in analytics.<\/p>\n

2. Information Design and Presentation<\/strong><\/h4>\n

Information design<\/a>\u00a0is the presentation of data in a way that fosters effective and efficient understanding. The emphasis is on visual design that enables consumption of data, rather than just beautification. Visualisation is that last mile of communication that enables users to get value from analytics.<\/p>\n

To become an expert in this area, one must master the design skills spanning the realms of interaction design, user experience and data visualisation. This calls for expertise in user mapping, information architecture, data representation, wire framing, high fidelity design and visual aesthetics.<\/p>\n

The greatest value of a picture is when it forces us to notice what we never expected to see\u200a\u2014\u200aJohn\u00a0Tukey<\/p><\/blockquote>\n

3. Statistics and Machine\u00a0learning<\/strong><\/h4>\n

In most data science courses, this area is devoted a lion\u2019s share of attention. The focus here is on statistics and modelling, while scripting or programming is a secondary skill. While this is a key area for extraction of value from data, an over-emphasis here may take focus off the other 4 key skills in data science.<\/p>\n

Building upon the basic data wrangling skills, one must dive in deeper into statistics, probability and then branch out into the techniques and algorithms in machine learning. Deep learning and other trending AI techniques fall into this bucket as well, but these call for more extensive coding skills.<\/p>\n

4. Deep programming<\/strong><\/h4>\n

People with a core programming background have great use in data science, and it\u2019s not mandatory for them to pickup machine learning skills. Data applications call for backend coding to connect and handle data, need heavy lifting with data processing and building out internals of data science apps.<\/p>\n

There are also strong needs for front-end coding skills to showcase the data insights to users, which is where the rubber really meets the road. One must master processing and presentation of data onto a variety of UI and form factors. Languages like Python, Java, Javascript, R, SQL are popular.<\/p>\n

5. Domain expertise<\/strong><\/h4>\n

Deep domain skills help bring in meaning, interpretability and actionability to analytics. The importance of blending domain expertise with data skills cannot be overemphasised. Lack of sufficient attention in this area throughout a project is the most common cause of failure for initiatives.<\/p>\n

Picking up depth in a chosen domain and mastering the business flows is the first step here. One must then focus on data-literacy and a conceptual understanding of analytical techniques. This brings in the know-how to weave a tight fabric by combining domain skills with data chops, for superior value.<\/p>\n

\u201cIf you do not know how to ask the right question, you discover nothing.\u200a\u2014\u200aW. Edward\u00a0Deming\u201d<\/em><\/p><\/blockquote>\n

The Roles in Data\u00a0science<\/h4>\n
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Roles in data science \u2014 making oneself a customised meal (Photo by\u00a0Brooke Lark<\/a>\u00a0on\u00a0Unsplash<\/a>)<\/p>\n

Now that we\u2019ve seen the spread of skills, these combine to form the following 4 core roles in analytics. These roles can be found in every shop that is serious about data science offerings. They are usually carved out as separate roles as described here, or are blended into some overlapping combinations.<\/p>\n

Given the evolving state of the industry, the job designations are fairly fluid. While we\u2019ve seen that\u00a0\u2018data scientist\u2019<\/em>\u00a0is a loosely used title often to attract talent, what a role like\u00a0\u2018Data ninja\u2019<\/em>\u00a0may mean is anybody\u2019s guess! Here, I\u2019ve used titles closer to those we\u2019ve evolved over the past 7 years, at\u00a0Gramener<\/a>.<\/p>\n

1. Data science Engineer or Specialist<\/strong><\/h4>\n
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Data science Engineer or Specialist \u2014 Skill\u00a0mix<\/p>\n

Folks who combine the data bootstrap skills with deep programming (front-end, back-end or full-stack) fall under this title. These roles are critical in application programming, integration and cloud implementations of data science applications. Apart from deep coding skills, this calls for mastery in processing data, automating insights and end-to-end app implementations. Some organisations show data scientist as a senior role in this career path.<\/p>\n

2. Data\u00a0Analyst<\/strong><\/h4>\n
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Data analyst \u2014 Skill\u00a0mix<\/p>\n

These are people who top up the core data chops with skills in statistics and machine learning. While they are the authority in devising analytics approach and building out models, they leverage their basic programming skills and domain orientation to implement and evaluate model interpretability.<\/p>\n

3. Information Designer or Visualization Designer<\/strong><\/h4>\n
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Information designer or Visualisation \u2014 Skill\u00a0mix<\/p>\n

Designers with core UI and UX skills, who also bring in a strong grounding in fundamental data skills fit the bill of Information Designer. They play an important role from conceptualisation to creation of data stories. Keeping users at the centre of the universe, they iteratively drive design of the visual intelligence layer.<\/p>\n

4. Functional Data Consultant<\/strong><\/h4>\n
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Functional data consultant \u2014 Skill\u00a0mix<\/p>\n

Functional data consultants act as a bridge between business users and the data science team, by combining their domain mastery with the essential data skills. Acting as a strong influence in the team, they onboard clients on data-driven use cases, while keeping the analytics solution business-driven and actionable.<\/p>\n

Transforming into a Unicorn in Data\u00a0science<\/h4>\n
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Making of a unicorn \u2014 the secret sauce (Photo by\u00a0Caroline Attwood<\/a>\u00a0on\u00a0Unsplash<\/a>)<\/p>\n

These 4 key roles in data science show how people from varied backgrounds \u2014 programming, statistics, design and domain, can transform their innate skills into an influential role in analytics projects. It\u2019s the strong fundamentals topped up with a passion for data and business application, that makes them invaluable to organisations.<\/p>\n

If quality practitioners in these 4 roles are highly sought after in the industry, and in short supply, what then makes a Unicorn in data science? What is that magical mix that can make a person invaluable?<\/p>\n

Re-picturing our data science buffet menu, this superior ability is born when one can check off any 3 skills from here, with an exceptional level of mastery in each. As the saying goes, it always looks easier in hindsight. Lets go over a few skill mixes and see the areas where these people can be irreplaceable.<\/p>\n

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Making of a unicorn \u2014 the special mix of\u00a0skills<\/p>\n