Data Science Goals and Deliverables

In order to understand the importance of these pillars, one must first understand the typical goals and deliverables associated with data science initiatives, and also the data science process itself. Let’s first discuss some common data science goals and deliverables.

Here is a short list of common data science deliverables:

  • Prediction (predict a value based on inputs)
  • Classification (e.g., spam or not spam)
  • Recommendations (e.g., Amazon and Netflix recommendations)
  • Pattern detection and grouping (e.g., classification without known classes)
  • Anomaly detection (e.g., fraud detection)
  • Recognition (image, text, audio, video, facial, …)
  • Actionable insights (via dashboards, reports, visualizations, …)
  • Automated processes and decision-making (e.g., credit card approval)
  • Scoring and ranking (e.g., FICO score)
  • Segmentation (e.g., demographic-based marketing)
  • Optimization (e.g., risk management)
  • Forecasts (e.g., sales and revenue)

Each of these is intended to address a specific goal and/or solve a specific problem. The real question is which goal, and whose goal is it?

For example, a data scientist may think that her goal is to create a high performing prediction engine. The business that plans to utilize the prediction engine, on the other hand, may have the goal of increasing revenue, which can be achieved by using this prediction engine.

While this may appear to not be an issue at first glance, in reality the situation described is why the first pillar (business domain expertise) is so critical. Often members of upper management have business-centric educational backgrounds, such as an MBA.

While many executives are exceptionally smart individuals, they may not be well versed on all the tools, techniques, and algorithms available to a data scientist (e.g., statistical analysis, machine learning, artificial intelligence, and so on). Given this, they may not be able to tell a data scientist what they would like as a final deliverable, or suggest the data sources, features (variables), and path to get there.

Even if an executive is able to determine that a specific recommendation engine would help increase revenue, they may not realize that there are probably many other ways that the company’s data can be used to increase revenue as well.

It can therefore not be emphasized enough that the ideal data scientist has a fairly comprehensive understanding about how businesses work in general, and how a company’s data can be used to achieve top-level business goals.

With significant business domain expertise, a data scientist should be able to regularly discover and propose new data initiatives to help the business achieve its goals and maximize their KPIs.

Data Scientist Pillars, Skills, and Education In-Depth

We’ve already discussed the business domain and communication pillars, which represent business acumen and top notch communication skills. This is very important for the discovery and goal phase. It’s also very helpful in that data scientists typically have to present and communicate results to key stakeholders, including executives.

So strong soft skills, particularly communication (written and verbal) and public speaking ability are key. In the phase where results are communicated and delivered, the magic is in the data scientist’s ability to deliver the results in an understandable, compelling, and insightful way, while using appropriate language and jargon level for her audience. In addition, results should always be related back to the business goals that spawned the project in the first place.

For all of the other phases listed, data scientists must draw upon strong computer programming skills, as well as knowledge about statistics, probabilities, and mathematics in order to understand the data, choose the correct solution approach, implement the solution, and improve on it as well.

One important thing to discuss are off-the-shelf data science platforms and APIs. One may be tempted to think that these can be used relatively easily and thus not require significant expertise in certain fields, and therefore not require a strong, well-rounded data scientist.

It’s true that many of these off-the-shelf products can be used relatively easily, and one can probably obtain pretty decent results depending on the problem being solved, but there are many aspects of data science where experience and chops are critically important.

Some of these include having the ability to:

  • Customize the approach and solution to the specific problem at hand in order to maximize results, including the ability to write new algorithms and/or significantly modify the existing ones, as needed
  • Access and query many different databases and data sources (RDBMS, NoSQL, NewSQL), as well as integrate the data into an analytics-driven data source (e.g., OLAP, warehouse, data lake, …)
  • Find and choose the optimal data sources and data features (variables), including creating new ones as needed (feature engineering)
  • Understand all statistical, programming, and library/package options available, and select the best
  • Ensure data has high integrity (good data), quality (the right data), and is in optimal form and condition to guarantee accurate, reliable, and statistically significant results
  • Avoid the issues associated with garbage in equals garbage out
  • Select and implement the best tooling, algorithms, frameworks, languages, and technologies to maximize results and scale as needed
  • Choose the correct performance metrics and apply the appropriate techniques in order to maximize performance
  • Discover ways to leverage the data to achieve business goals without guidance and/or deliverables being dictated from the top down, i.e., the data scientist as the idea person
  • Work cross-functionally, effectively, and in collaboration with all company departments and groups
  • Distinguish good from bad results, and thus mitigate the potential risks and financial losses that can come from erroneous conclusions and subsequent decisions
  • Understand product (or service) customers and/or users, and create ideas and solutions with them in mind

Education-wise, there is no single path to becoming a data scientist. Many universities have created data science and analytics-specific programs, mostly at the master’s degree level. Some universities and other organizations also offer certification programs as well.

In addition to traditional degree and certification programs, there are bootcamps being offered that range from a few days or months to complete, online self-guided learning and MOOC courses focused on data science and related fields, and self-driven hands-on learning.

No matter what path is taken to learn, data scientist’s should have advanced quantitative knowledge and highly technical skills, primarily in statistics, mathematics, and computer science.

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