Data Science 101 - A Simplified Guide Developed For Business

Data Science can be defined as the process of using data, statistical concepts and computational tools to help inform decision-making processes.

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What is Data Science

Data scientists are often the resources companies use to interpret data, solve business problems and create opportunities. Data science is best used to support personal, organizational, or industry goals.

Data Science includes knowledge about how datasets can be combined or joined together so that they become more useful for analysis. Data scientists are interested in understanding how different models might impact their ability to detect patterns in data sets.

Generally speaking - skills within this industry include proficiency with basic statistics as well as programming languages like Python. Data engineers will seek out and solve problems by using visualizations, statistical models and machine learning.

This is an interdisciplinary practice that combines technical and business skills with the ability to define and ask complex questions of the available data.

Data science can be used in a wide variety of industries and more commonly even by small and medium-sized businesses wanting to gain a competitive advantage.

Data scientists help companies to make sense of seemingly random pieces of data, usually with the goal of revealing new insights about markets or target user behavior.

History of Data Science

Data Science has been around since the dawn of civilization. Data has been collected and analyzed for centuries, but Data Science as a formal discipline is a relatively new concept.

In the 1970s, statisticians such as John W. Tukey began writing about Data Analysis and Data Science concepts in academic journals. During this time period Data Science was often referred to as Data Analysis or Data Theory.

In the 1980s, Data Science began to be taught in universities such as the University of California at Irvine and Stanford University.

The term "Data Scientist" was not coined until much later when DJ Patil and Jeff Hammerbacher both worked for LinkedIn in 2008 and used the term in their job descriptions:

A data scientist is someone who takes these vast amounts of data, applies a combination of programming, statistics, machine learning and domain knowledge to find nuggets of information that will help drive the business forward.
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The Data Scientist is one of the most important roles in LinkedIn as it affects almost every strategic and tactical decision we make from how to best structure our web pages and APIs to understanding which new markets we should enter. The Data Scientist role touches everything from product marketing all the way through to what features we build into our mobile applications.
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In 2012, Data Science was proclaimed on Ycombinator for Data Analysis as a distinct profession stating: "A Data Scientist is a person who is better at statistics than any software engineer and better at software engineering than any statistician." Data scientists were also mentioned in an article published by McKinsey & Company called Data scientist: The Sexiest Job of the 21st Century.

Data Science is now considered a high-demand, hot career that attracts graduates from various scientific disciplines.

Data Science tends to focus on business or marketing problems rather than Data Engineering which focuses more on the technical aspects of Data Management.

Machine Learning models are being used to predict click-through rate for online ads, power consumption based on weather data, customer churn and much more.

Data Scientists not only look at patterns within large datasets but they build mathematical representations of these patterns so that systems can learn how to make predictions about future datasets without being told what the right answer might be.

When is Data Science most commonly used?

Data Science is used when Data Scientists work closely with Data Engineers, Data Analysts and Data Visualization Specialists.

Data Scientist = Data + Science

Data: Digital data in a variety of formats including structured and unstructured data which comes from a variety of sources such as mobile devices, social media platforms, IoT sensors and much more.

Science: Data Scientist uses methods from statistics, machine learning, pattern recognition and predictive analytics to find actionable insights within large datasets.

Are there any rules to consider when using Data Science for your business?

You can use Data Science to understand how customers are using your product, find areas where you can reduce costs, find ways to increase revenue and improve customer happiness.

The following is some do's and do not items to keep in mind when thinking about commencing a Data Science Strategy:

The Do's

  • Data Scientists should work with Data Engineers, Data Visualization Specialists and Data Analysts to help solve business problems using Data Science.
  • Data Scientists must think about statistical significance when looking for patterns within data.
  • Data Scientists must have a good understanding of the type of questions being asked so they can find relevant data from relevant sources.
  • Scientists must have a good intuition of how Data Engineers might structure Data so Data Scientists can build Data Science models.
  • Data Scientists should plan to automate their Data Science model when possible because Data is constantly changing and Data scientists won't always be around to update the Data Science model with newer data.

Do NOT's

  • Do not start Data Science by building models first without having a clear idea of what goals your projects need to accomplish.
  • Do not base decisions solely on the results obtained from predictive analytics models alone since you need an understanding of how well these models will perform.
  • Be careful when ignoring data.

What do Data Scientists actually do?

Data Scientists use their skills to translate statistical analysis into natural language that can be presented in business presentations or articles. In addition, they also have the opportunity to develop agile processes which enable the team to iterate quickly when discovering insights within data.

Data Scientists also assist Data Engineers in exploring new Data Infrastructure options to provide flexibility when working with multiple analytics tools, frameworks and technologies.

Understanding this... let's take a look at some of the key qualities required to be a professional Data Scientist:

  1. Data Mining - Harvesting large sets of data.
  2. Data Cleaning - Fixing anomalies and discovering patterns within data.
  3. Data Visualization - Data Scientists use data visualization to communicate findings to those who might not have been able to discover these insights by themselves.
  4. Data Storytelling - Creating compelling visualizations that facilitate the understanding of results from the analysis on large datasets. The can help the critical business decision-making process.
  5. Data Analysis and Data Processing - Pattern recognition, regression testing and exploratory analysis are a few examples of what Data Scientists do when analyzing data sets.

Why does modern-day business love Data Science?

Data Science allows businesses to make predictive decisions regarding customers.

Data Scientists are the ones behind what makes Google, Amazon and Netflix so successful because they use Data Analytics to understand consumer behavior and predict purchasing patterns.

Data Scientists were involved with developing Data Products like Siri (Apple), Nest (Google) and Facebook Newsfeed which all won awards in 2013. Data Scientists also helped with Data Product design for LinkedIn, Pandora and Expedia.

Data Science can be used to help marketing teams understand which marketing strategies are the most effective. Data Scientists use Data Analytics working hand in hand with User Experience Teams to develop customer personas, uncover value proposition patterns and determine trends in product usage by analyzing data from internal tools like Data Warehouse.

Modern marketing would not exist without Data Science because they put together large quantities of information into meaningful insights that allow brands to better understand their customers so that they can connect with them on an emotional level.

Data scientists have played a pivotal role in shaping the way modern-day marketers approach their jobs - they provide insight into how people think, feel and behave based on available data sets.

Is Data Science an expensive exercise?

Data Scientists have a higher salary compared to Data Engineers in America. Data Science is not a cheap exercise because Data Scientists need to understand the business problems before applying Data Analytics and Data Mining techniques to discover any patterns within large datasets.

Data Scientists also need Data Mining, Data Visualization and Data Storytelling tools.

There are multiple business advantages to using Data Science including:

Data Scientists have a higher salary compared to Data Engineers in America. Data Science is not a cheap exercise because Data Scientists need to understand the business problems before applying Data Analytics and Data Mining techniques to discover any patterns within large datasets.

  • Better Business Decisions - by using Data Analytics, Data Mining and Data Visualization in order to turn large sets of data into valuable insights.
  • More Efficiency - Data Scientists use Data Analytics tools to help Data Engineers build Data Products that will better serve an organization's needs.
  • Better Communication - Data Storytelling promotes collaboration between business users and data scientists, allowing each party to communicate even complex results in a clear way.
  • Business Growth - Data Infrastructure allows businesses to extract value from Data Analytics. This results in data-driven product development, allowing organizations to innovate and grow.
  • New Customers - Data Mining techniques help marketers connect at a more emotional level with existing customers while Data Analytics tools can provide insight into customer behavior which can be used in order to drive new business opportunities.
  • Improved Customer Experience - Marketing teams will be able to understand how users interact with products by analyzing feedback given through Data Visualization tools. They are then able to apply this knowledge across the entire customer experience journey, including web design, marketing campaigns, email nurturing etc...
  • Data Management - Data Engineering plays a huge role in Data Science because Data Engineers are responsible for building Data Infrastructure that allows Data Scientists to process their data easily, quickly and securely.


Data science is a field of computer science that focuses on extracting knowledge from data, whether it's big or small. Data Scientists are able to find patterns within large sets of data in order to gain insights an organization can actually use.

Data analysis is the core component of Data Science and Data Mining provides Data Scientists with tools they need in order to analyze their data effectively - including Data Visualization for business intelligence and Data Storytelling for better communication.

Data Science not only helps businesses make better decisions but also increases efficiency by making Data Engineers more productive while improving customer experience across entire customer journeys (web design, marketing campaigns, email nurturing etc.)

Once Data Scientists have finished exploring the dataset, they generally produce a report for decision-makers that outlines their findings and recommendations.

Skill Tags

Data Science Programming (Python) Statistics Machine Learning Data Modelling Data Mining Problem Solving Storytelling Business Analysis

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