Data science empowers organisations to make better decisions, by transforming raw data into actionable insights and business value.

Wikipedia describes it as:

An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.

A data science project requires a skilled, cross functional team of data scientists, data engineers, software engineers, business analysts, cloud engineers and more…

Always begin with a business goal in mind! It helps to try imagine a dashboard with one or two gauges, and needles measuring some performance indicators. Now ask yourself, what is the gauge measuring and what needle would you want to move?

It’s been said that Data Scientist is the “sexiest job title of the 21st century.”

Why is it such a demanded position these days? The short answer is that over the last decade there’s been a massive explosion in both the data generated and retained by companies. Sometimes we call this “big data,” and like a pile of lumber we’d like to build something with it. Data science teams are the people who make sense out of all this data, and figure out just what can be done with it. It all starts with a clear business goal.

What are the use cases for Data Science?

It depends on the industry, and there are too many to list. If you are still defining your business goals and don’t have a clear use case in mind, you are probably not ready for a data science project, we recommend you first research the use cases for your industry, or particular business challenge.
Always begin with a business goal in mind! It helps to try imagine a dashboard with one or two gauges, and needles measuring some performance indicators. Now ask yourself, what is the gauge measuring and what needle would you want to move?
To inspire you, refer to the image below depicting typical use cases by industry.

Finance & Banking

  • Credit scoring
  • Fraud detection
  • Risk analysis
  • Client analysis
  • Optimised debit order strikes

Retail & E-Commerce

  • Demand forecasting
  • Price optimization
  • Recommendations
  • Fraud detection
  • Customer segmentation

Marketing & Sales

  • Marketing and customer
  • Price optimization
  • Churn rate analysis
  • Customer lifetime value prediction
  • Upsell opportunity analysis
  • Sentiment analysis in social networks

Travel & Booking

  • Demand forecasting
  • Price optimization
  • Price forecasting (for dynamically changing prices)

Health Care & life Science

  • Increase in diagnostic accuracy
  • Identifying at-risk patients
  • Insurance product cost optimization

Other

  • Object recognition (photo and video)
  • Content recommendations (movies, music, articles and news)
  • Chabot’s and virtual assistants
  • And more

Data Science Lifecycle

Doing data science is like preparing a meal, it begins with a decision “What are we cooking?”.
Thereafter begins data preprocessing, which includes but is not restricted to ETL (extract, transform, and load), data cleansing, data debugging, data merging etc. This step is similar to preparing a meal, where you rinse and clean the vegetables, the meat, the rice, chop the food sources into reasonably sized pieces, and put them aside.
After that is done, you are ready to cook the ingredients, which corresponds to data exploration, feature construction, feature reduction, running and ensembling the algorithms, etc. This is when you cook the vegetable and meat in a step-by-step fashion, adding ingredients and sources on calculated timings, and watching the raw material turn into edible pieces.
The last step is to serve the food, when you arrange the cooked food in artistic ways and serve them in a particular sequence of first course, second course, etc, to customers who ordered the food to begin with. This is when you prepare your data mining results in artistic visualization and create reports or data stories, to send to the business users who wanted this piece of data science work to be done in the first place.
Below image from Microsoft depicts the lifecycle of a typical Data Science project. It is an iterative process of learning, and improvements.

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Looking for more information? See our machine learning and deep learning page for an explanation, and example uses.