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Explore different Data Science career paths and how to get started

What is Data Science?

Data Science – The Boring Definition

Let’s take a look at the textbook definition of Data Science. It reads something like this:

Data science is an interdisciplinary field focused on extracting meaningful information from large sets of data. It combines the scientific method, math and statistics, programming, advanced analytics, AI, and even storytelling to uncover and explain the business insights buried in data.

It’s well said but this definition is somewhat abstract. Understanding a couple of use cases will for sure help clear some of the doubts one may have. We will introduce some real-life use cases later in this chapter.

Simply put, data science is the process of discovering insights from data and using it for better changes.

  • In a business setting, data science is the processing of collecting, preparing, analyzing, and mining the business data to help make better decisions or build better data-driven products and therefore resulting in better business outcome.
  • In social science and public sectors, data science is applied to different kinds of data and the analytics results are used to benefit society. For example, criminal justice, education, economic and workforce development, energy, environment, public health, transportation and infrastructure, as well as public safety.

Data Science has a different meaning to companies depending on the stage of their data science maturity curve.

  • In startups where data is not the core product, data science probably means applying data analytics to keep the business teams informed and support decision making.
  • For consumer-facing businesses that has established data teams, data science is usually applied as data-driven processes to optimize key business metrics such as revenue, daily active users, and retention.
  • For companies that consider data as a core business strength, data science may be applied in product design and as a result it directly impact the user experience and can lead to company growth.

Regardless of the company sizes, maturity stages, and use cases, data scientists seem to be doing pretty high-impact projects!

Data Science Lifecycle

Data Science is more than just data analysis. A typical data science project involves a few stages in the lifecycle. A general lifecycle may include:

  • Business Understaning
  • Data Acquisition and Understanding
  • Data Analysis and ML Modeling
  • Model Validation and Interpretation
  • Model Deployment
  • Monitoring & Refinement

In layman’s terms, this is how one can understand the data science lifecycle:

  • Business has challenges and raises some assumptions
  • Data science team tries to understand the business challenges and figure out the type of data to use for analysis
  • Data scientists apply the analytics magic to come up with data solutions
  • The outcome gets interpreted to the business team that leverages the magic work done by DS to improve the business product/process
  • After business user acceptance, data science insights get turned into a product that requires care and maintenance in production (monitoring & data ops)
  • Data scientists analyze the feedback data and continuously iterate on the magic tricks to keep refining the entire process

Here are a few popular data science lifecycles:

In practice, a data scientist doesn’t always get to work on the entire lifecycle. Depending on the data science team and the maturity, data scientists may focus on specific stages of a lifecycle. For example,

  • Some data scientists might focus on machine learning and advanced analytics,
  • Some data scientists might focus more on the ML engineering side
  • Some citizen data scientists may spend more time on data exploration, visualization, as well as business communications.

Data Science Use Cases

Data Science has so many use cases. That’s one of the reasons why it’s such an appealing career for many. Let’s take a look at some of the real industry projects that WeCloudData students have worked on in the past.

Data Science in Healthcare

  • Our students helped medical doctors apply machine learning on a small sample of patient data to predict heart failure. The key research was included in a publication.
  • In another project, our students and project managers helped a healthcare startup build a knowledge graph that’s used as a backend tool to power knowledge search in an application built for nurses.
  • Our students also helped another healthcare start collect web data through scraping and build the company’s first visualization dashboard.

Data Science in VR/AR

  • Our students worked with AR game player engagement data and GPS data to help the client build an interactive dashboard for AR player engagement insights

Data Science in Digital & Media

  • Our students helped a digital & media company build audience look-alike models using big data tools such as Snowflake and Apache Spark

Data Science in Accounting & Productivity

  • Our students helped a Receipt Management app startup classify and categorize scanned receipt images using machine learning and NLP

Data Science in Personalization

  • WeCloudData students helped a Media & Publishing company build personalized recommendation engines to help improve user engagement and retention

Data Science in Consumer Electronics

  • Our students helped a consumer electronics company’s marketing team build customer and store segmentation models and also created predictive churn models for better user retention management

Data Science in Supply Chain

  • Our students helped several supply chain clients improve the time series forecasting models using deep learning techniques that result in better inventory management as well as revenue forecasting

Data Science in Sports & Entertainment

  • Our students helped a sport analytics startup build company-wise and client-facing visualization dashboards to help analyze and monitor the AI sports game prediction models

[Webinar] Introduction to Data Science

To learn more about data science use cases, check out our info session below.

Hope this article helped you understand the lifecycle of data science and its use cases. Read on to learn more about a career in Data Science and how WeCloudData can support you on your journey into Data Science.

Check out other career guides

Our Career Guide provides all the resources you will need to help you get started in navigating data careers. We include free resources, guides, and tools to help you get started.

WeCloudData

WeCloudData is the leading data science and AI academy. Our blended learning courses have helped thousands of learners and many enterprises make successful leaps in their data journeys.

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