Digital Transformation Hub

Jul 20 10 mins

Data Science Introduction and Online Courses


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Data Science is a fast-growing domain that has helped businesses to revolutionise their operations. In this article, we provide Data Science introduction and discuss its history, components and the role in Digital Transformation.

What is Data Science?

Some people equate Data Science to statistics since it involves working out insights from different data sets and behaviours. Data Science is a wide field that employs scientific methods, algorithms and different systems to extract knowledge and create inferences from a range of structural and unstructured data. It is an interdisciplinary field that uses techniques and theories related to Mathematics, Information Science, Statistics and Computer Science. Simply put, Data Science is the study of data which involves recording, storage and analysis of data in a bid to obtain helpful information and insights.

It also includes the preparation and presentation of findings, which influences and shape significant decisions in an organisation or business set-up. Data Science, therefore, mostly dwells on prediction and action while focusing on quantitative and qualitative data.

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Image by Gerd Altmann from Pixabay

History of Data Science

Data science dates back to the 18th century when the Bayes’ Theorem created a foundation for probability calculations which later powered Artificial Intelligence. The theorem gave room for simple statistic models which employed mathematics to collect, analyze and manage data throughout the 19th century. The scientific objectivity of data analysis was later explained in 1954 by the foundations of statistics.

The term Data Science, on the other hand, surfaced in 1996, at the International Federation for Classification Societies in Japan. It is however attributed to William S. Cleveland who published “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics” in 2001.

Some of the most significant events in the development of Data Science include the works of John Tukey in 1962, where he discussed the merging of computers and statistics which made it possible to table results in hours. John made a connection between electronic computing and Data Analysis, which he saw as an empirical science concept.

In 1974, Peter Naur published “Concise Survey of Computer Methods”, which delved deeper into data processing methods in his several publications. In his work, Peter used Data Science repeatedly, referring to it as the science of dealing with data. Three years later, in 1977, the International Association for Statistical Computer announced the combining of statistics with Computer technology and Subject Matter Expertise to build knowledge as one of their objectives.

The first Knowledge Discovery in Databases workshop was convened in 1989. Five years later, different forms of modern marketing began appearing. The next few years after 1996 saw the mentioning of Data Science in various works and conventions. Jeff Hammerbacher and DJ Patil coined the term Data Scientist whose job listing increased in 2011 and 2012. Data Science blew up in 2010 after brands such as Apple release iPhone 4 and iPad. The most significant breakthrough happened in 2013 when IBM, through its statistics, observed that 90% of the world data were created in 2011 and 2012 alone.

Data Science history infographic
A great Data Science history infographic by Capgemini

The Key Components of Data Science

Data Science is an interdisciplinary field that is made up of several segments. In this part, we will have a look at these components and their application in day to day life.

i) Big Data

One of the main components of Data Science is Big Data. Big Data, just like the name suggests, refers to large volumes of structured or unstructured data. Simply put, this is any data that cannot be handled easily. For data to qualify as Big Data, three characteristics must be present:

1. It should be large in volume, mostly in terabytes

2. It should have varying structure

3. Streaming of the data should be with high output.

Specific sectors that deal with Big Data include retail businesses, where information regarding employees, customers and sales are left in both structured and unstructured format. Big Data cannot be efficiently compressed, and therefore, certain technologies such as Hadoop and Spark work the magic. Some of the benefits of Big Data include its cost-effective approach to storing information. It also saves on processing & analysis time thanks to the technologies we mentioned above.

Through Big Data, businesses can effectively analyse their operation and identify areas that they need to improve on.

ii) Data Analysis

Data Analysis is an integral part of Data Science that plays a huge role in cleaning data. It involves removing the errors, and unwanted information is a given data set before establishing the right form for the data. It also requires structuring data from a set of raw files. Data Analysis employs different methods such as extraction, transformation and visualisation of data to establish useful conclusions and derivations that can help a company make informed decisions.

Data Analysis might also extend to historical data when trying to establish changes in market trends and product expectations. In short, Data Analysis is a mean of data exploration that yields important conclusions and derivations. Data visualisation based on Data Analysis allows businesses to identify relevant information for effective decision making without engaging in sophisticated scientific methods.

This component plays a significant role in data mobility too. You can go through your data and structure it the way you want anywhere in the globe and at any time thanks to the Data Analysis systems.

Data Analysis also helps businesses to weigh competition. By knowing how the competitors are fairing, companies can settle on the best marketing strategies and campaigns that will reach several customers. Lastly, Data Analysis plays a vital role in helping businesses to identify the most effective marketing actions since it is primarily based on the review of different insights. Practical marketing actions also improve sales.

iii) Data Analytics

Data Analytics is a broader term than Data Analysis and it is usually referred to as an overarching discipline. It consists of a set of processes and techniques which are utilised depending on a problem the organisation is aiming to solve. It is therefore essential that an organisation identifies the problem at hand before creating the right Data Analytics plan of actions.

Data Analytics can be descriptive, predictive or prescriptive and is used by businesses in all sectors to arrive at better and well-informed decisions. Some of the benefits of Data Analytics to a business include cost-effectiveness. It relies typically on Big Data, which is a cost-saving option of storing substantial assets of information.

Data Analytics also help in risk analysis and evaluation of different trends and changes that a business can hop on to if it needs to stay relevant in the market. Data Analytics help companies to identify the purchase patterns of different customers.

iiv) Data Mining

When businesses apply Data Analytics techniques, there will be a need for identifying hidden patterns in the used data sets. This is where Data Mining comes in. Companies, therefore, turn raw data into more useful and informative data through Data Mining. This component relies on specialized software in identifying the hidden patterns. The primary role of Data Mining, therefore, is to study, analyse and dissect data to help businesses arrive at effective marketing strategies for customer outreach.

Data Mining helps improve customer relations since it is aimed at coming up with the best marketing strategies for customer outreach. By reaching as many customers as possible, businesses can widen their profit base.

Data Mining entails loads of research, allowing businesses to gain useful insights for decision making and planning. A company can also easily identify fraud since it combs through historical data. By identifying fraud, companies will strive to find a way of reducing the chances, and therefore, mitigating future risks.

The last but not least, Data Mining is a vital trend identifier. To stay afloat, businesses need to remain relevant. Relevancy requires that one blends in with the recent trends. Data Mining allows firms to lay hands on the latest data and purchase trends.

machine learning abstract picture
Image by Pete Linforth from Pixabay

v) Machine Learning

Machine Learning is a unique component of Data Science and also one of its cornerstones. One of the most common ways of obtaining insight from a set of processed data is through predictions. To be sure, businesses usually use a mathematical model to arrive at different predictions. Machine Learning, therefore, makes it possible for such companies to work on future forecasts and achieve accurate values from a set of hypotheses.

This component takes advantage of the patterns found in the particular set of data to predict future events. Is short, Machine Learning uses Artificial Intelligence to analyse data through automated analytical models. It also translates a given collection of data into real-life situations. Machine Learning plays a vital role in business operation. It is dynamic and can, therefore, work in any environments. It also works with different data sets to obtain useful predictions and insights that businesses need to stay afloat.

Machine Learning brings in an automatic approach to business, easing task processing. On top of that, enterprises benefit from simple product marketing and accurate sales predictions. This component also helps in predicting future behaviours and customer trends that help the business to plan earlier and either mitigate the risks or maximize the profits.

Some of the advantages that we will look at in the other sections include medical and financial predictions, which are also crucial for businesses in the said sectors. Machine Learning is, therefore, used because of its accuracy and diverse operations. Do not forget that through data entry, this component helps firms to save time with documentations.

v) Other important Data Science components

There are many sources for different Data Science component structure. Some components that we did not mention in our list above are;

  • Data Strategy
  • Data Engineering
  • Mathematics / Statistics / Probability
  • Programming languages
  • Data Visualisation
  • Domain Expertise
MacBook Pro with business report on a glass table
Photo by Carlos Muza on Unsplash

To conclude; businesses should work on the insights that have been generated by the different components of Data Science. Before any business decisions are made, factors such as future predictions and data analysis must be taken into account. Some of its advantages are that it helps companies to personalize the services offered to the customers through digital technology. The world is fast-changing, and different business opportunities are also coming up.

Role of Data Science in Digital Transformation

Digital Transformation encompasses different areas of business such as operations, innovation, customer service, finance and market strategies. It requires businesses to not only identify areas that need reforms but also to manage risks and prevent resource wastage that comes with it. Data Science plays a huge role in Digital Transformation. Companies are effectively utilising Big Data, Machine Learning and Data Analytics which are all components of Data Science. Together with Artificial Intelligence and Cybersecurity, Data Science is the most significant digital trend used in Digital Transformation initiatives.

Here are a few roles of Data Science in Digital Transformation:

i) Identification of threats and opportunities

As information grows, so does opportunities. Data Science enables companies and businesses to have a more comprehensive and detailed view of the business environment. Through this, companies can create solutions and plan on how to avoid future problems associated with the business operation.

Such solutions help businesses to understand their customer base properly. It also helps vital determinants to tailor the internal operations of the company for maximum profit, which includes reducing operation costs and increasing flexibility of the business. By identifying threats early, businesses are in a better place of mitigating associated risks and improving their service provision.

ii) Improved decision making

Data science helps businesses to make proper data-driven decisions. Digital Transformation is a very complex topic, especially if a business largely relies on traditional approaches in its operation. Owning data about your customers and business operations puts the business at a better place of decision making and risk prevention. By using Data Science it is easier to spot an area of business that requires Digital Transformation.

Data Science offers access to a pool of experts who are fully equipped to give your business a swift operation. Remember, fast transformation keeps you ahead of the competition.

iii) Access to Machine Learning

Machine Learning plays a huge role in business transformation. Businesses can experience a rapid digital shift in sectors such as banking through this Data Science component. Machine Learning uses Big Data to ease the identification of different patterns and errors. It employs algorithms, which allows it to work without basic programming. Businesses, through data experts, can, therefore, use Machine Learning to create timelines and predict future disruptions.

iv) Providing new data sources

Data Science allows retailers and distributors to easily search and locate all the goods in their premises through RFID and in-store sensors. They can also monitor the movement of the products inside and outside of the store. Data Science allows warehouse or retail store owners to invest in better layouts through the customer foot patterns.

The same data gathered by the in-store sensors can also be used by fashion houses to assess how well their clothing pieces fit the customers. We can see how Data Science helps revolutionise businesses in several ways.

v) Enabling real-world applications

One of the most critical roles that Data Science plays in Digital Transformation is enhancing the development of future enterprise software which informs businesses of future occurrences and help them decide on how well these happenings can be controlled today. Through this, a business can plan prior and minimize losses. Stores also easily filter customers based on products by collecting information about what product they purchase and the frequency of purchase.

Businesses, through the help of a data expert, create supply chain timelines and predict future disruptions. The store will, therefore, be at a better place to mitigate risks when such disruption later occurs.

revenue chart digital transformation success story
A picture by Geralt at Pixabay

Learn Data Science

Data Science is at the centre stage of business transformation. It has helped organizations to apply new techniques for increased profits and insight predictions, that have revolutionized their overall performance and operation. It is a vast field that continues to grow every passing day.

In case you are interested in enriching your skills with Data Science knowledge, here are a few relevant links of Data Science online courses:



If you would like us to add a course to the above list, please leave the link in the comments below.



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