What is data science? an introductory guide to data science

In our technology-driven world, data is allowing businesses to grow like never before. This is why data science is a vital practice in every industry, as it helps organizations make better-informed decisions and predictions by evaluating the vast amount of data available.

Here’s the thing though: Unless you’re familiar with data science and its applications, you’re simply not utilizing your available data to its full potential. As a result, your business could be missing out on valuable opportunities to grow and expand your commercial revenue.

We’ve put this guide together to tell you everything you need to know about data science, including how it differs from similar fields of study, what it’s used for, its various applications, and why it should matter to your business.

What is data science?

Data science is a multidisciplinary field that brings together statistics, data analysis, informatics, and their related methods in order to understand and explore phenomena within structured and unstructured data. It’s closely related to areas including:

  • Data mining
  • Big data
  • Machine learning

Data science also uses practices and concepts borrowed from several other fields involving:

  • Mathematics
  • Statistics
  • Computer science
  • Information science
  • Domain knowledge

The word ‘data science’ has been around since the 1960s, but in the past, it was used to also mean ‘computer science.’ As a field of study, however, data science is considered to be young. It developed out of the disciplines of statistical analysis and data mining.

The Data Science Journal was released in 2002, and by 2008, the title of data scientist had been coined and the field quickly grew to prominence.

Structured vs unstructured data: what’s the difference?

As we mentioned, data science relies heavily on examining both structured and unstructured data to gain insights.

Structured data is quantitative data, meaning it’s in the form of numbers and values. It’s highly organized and easily searchable in databases that manage data in a traditional table format. Common examples of structured data include:

  • Names
  • Dates
  • Addresses
  • Product identification numbers
  • User ID numbers

Unstructured data, on the other hand, has no predetermined format or organization, making it much harder to gather, process, and examine. It’s referred to as qualitative data, meaning it’s made up of data in the form of text files, audio files, and video files.

Other common examples of unstructured data include:

  • Social media posts
  • Satellite imagery
  • Surveillance imagery
  • Sensors

The majority of data is unstructured and has been sourced from email messages, word-processing documents, PDF files, and more. Finding useful information buried within unstructured data is a difficult operation and requires advanced analytical skills and technical expertise.

Data science vs. data mining vs. machine learning vs. big data

Data science is closely related to areas including data mining, machine learning, and big data, but it’s important to point out the differences among these fields to avoid confusion.

Data science vs. data mining

Data mining is a process that involves searching through vast amounts of computerized data to discover useful patterns or trends. It’s also referred to as data archaeology, information harvesting, information discovery, or knowledge extraction.

Data mining is a technique, whereas data science is a field of study that uses this technique. Additionally, data mining mostly deals with structured data, but data science must utilize all types of data, including structured, unstructured, and semi-structured.

Data science vs. machine learning

Machine learning is a subset of artificial intelligence. It refers to the study and development of computer algorithms that are able to learn and adapt automatically through experience and by making judgments from patterns in data.

Machine learning powers many of the services we use today, such as Netflix, YouTube, Google, Facebook, and more. These platforms collect a lot of data about you, such as the genres you like watching and the links you are engaging with. They then use machine learning to make a highly informed guess about what you might want next.

Machine learning, therefore, refers to a group of techniques used by data scientists that allow computers to learn from data. Although data science includes machine learning, it’s still just a small area of this vast field of study.

Data science vs. big data

Big data refers to extremely large data sets that require specialized technologies and techniques to efficiently utilize the data. These diverse data types are generated from multiple sources and include all types and formats of data.

Data science, on the other hand, is a specialized area involving scientific programming tools, models, and techniques to process big data. As a field of study, data science provides techniques to extract insights and information from large datasets, which are then used to support organizations in making more informed decisions.

Why data science matters to business

As businesses increase their reliance on technology, they are also placing even more importance on the role that data science plays within their organization. Here are eight ways data science adds value to any business.

1. Data scientists empower management to make better-informed decisions

Part of a data scientist’s role is to measure, track, and record performance metrics and other information to improve decision-making practices across the whole company. Additionally, they utilize this data to ensure staff across the board are maximizing their capabilities. Data scientists are viewed as trusted advisors to those in management positions, and their work provides immense value to the overall success of the organization.

2. They help to determine an organization’s goals

A data scientist works closely with a company’s data and uses this highly valuable, yet cryptic information to propose specific actions that will boost the business’s performance, customer engagement, and retention, and ultimately lead to increased profits.

3. They encourage staff to address key business challenges

A data scientist must also ensure a company’s staff understands and is familiar with the organization’s analytics product. They must demonstrate how to use the system successfully to extract insights and drive action. This allows employees to not only understand the product’s abilities but also shift their focus to addressing these key business hurdles.

4. Data scientists identify opportunities

A data scientist’s role requires them to constantly improve and increase the benefits produced by the organization’s data. To do this, they must question the current processes for the purpose of developing further methods and analytical algorithms.

5. They eradicate the potential for high-stake risks

Because data scientists gather and analyze data from multiple sources, they are able to use this existing data to create models that simulate a variety of possible actions. Thanks to this, an organization can learn which decisions will generate the best possible outcomes, therefore eliminating many potential risks.

6. They measure the success of decisions

While a data scientist uses an organization’s data to advise them about the best decisions to take, they then also utilize data to measure key metrics and determine if the outcome was successful in the end. They examine how new changes have affected the organization and calculate their success.

7. Data scientists create a more precise identification of a target audience

Data scientists take existing, one-dimensional customer data and combine it with other forms of data to generate more comprehensive insights about a company’s audience and target customers.

This in-depth knowledge allows a company to target their products or services more successfully to their target consumer, which in turn generates greater profits.

8. They can utilize data to find the right candidate for the job

Data scientists can actually utilize existing data on potential job candidates — through social media, corporate databases, and job search websites — to find the person who best fits the company’s requirements.

Data scientists can further make use of data to process applications and create data-driven aptitude tests and games. This leads to faster and more precise recruitment selections and further boosts a company’s potential for success.

What is data science used for?

At its very core, data science is mainly used to make better-informed decisions and predictions by evaluating data. The field of study does this through 4 different approaches:

  • Predictive causal analytics
  • Prescriptive analytics
  • Machine learning for making predictions
  • Machine learning for pattern discovery

1. Predictive causal analytics

Predictive causal analytics is an approach that’s used to predict the possibilities of a certain event occurring in the future. It can be used across many aspects of the company, from anticipating customer behavior and purchasing patterns to detecting trends in sales activities. Predictive causal analytics is also used by financial companies to determine a customer’s credit score, for example, along with the likelihood of if they will be able to make their repayments.

2. Prescriptive analytics

Prescriptive analytics is a relatively new process that’s all about providing advice. It attempts to calculate the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. A great example of prescriptive analytics is Google’s self-driving car. It’s a process that’s extremely complex to perfect and uses a mixture of techniques and tools such as business rules, algorithms, machine learning, and computational modeling procedures.

3. Machine learning for making predictions

Machine learning algorithms are sometimes more favorable in assisting with making predictions. Known as supervised learning, this process involves using data you already have and then use this to train the machines. One example might include using a data set from past marketing activities to determine the next best action in your current marketing campaign.

4. Machine learning for pattern discovery

Machine learning can also be extremely helpful when you don’t have the parameters required to make accurate predictions, as it can be used to find the hidden patterns within a dataset. This is known as unsupervised learning.

5. Data science applications and examples

The application of data science has changed almost every industry you can think of, and thankfully, it isn’t a field that is slowing down. In medicine, for example, data science can help identify the disease and predict a patient’s side effects. In the dating world, its using algorithms to help people find their perfect match, and in our everyday life, data science is being used in GPS applications to offer optimized route suggestions based on real-time traffic.

In this section, however, we’re focusing on the different use cases for data science in eCommerce and targeted advertising, as well as looking at two key examples within these industries.

The application of data science in targeted advertising

1. Smart bidding. Also known as “auction-time bidding,” smart bidding is a digital advertising tool from Google Ads that uses machine learning to optimize ads for conversions or specific conversion value in every auction. It uses machine learning, campaign data, and user data to optimize a user’s ad campaigns and help them get the best return on investment (ROI).

2. Programmatic advertising. Programmatic advertising allows marketers to deliver a targeted message to the right person, at the most optimal time, within the most desirable context. Advertising methods such as web banners and digital billboards, for example, use data science algorithms to determine where, when, and to whom they should be shown.

3. Visual merchandising. Store floor plans and 3D displays also utilize data science to determine their placement in order to maximize sales. Visual merchandising aims to attract, engage and motivate the customer towards buying a product or service but relies on relevant customer data to succeed.

4. Google Ads. Google also uses machine learning in PPC advertising, which allows advertisers to determine the impact of their campaigns across different channels, and to therefore adjust accordingly for the best results. With features such as AdWords, Google Analytics, and DoubleClick Search, users can gather data from all of their marketing channels.

Case Study: Opera Pay (O-Pay). O-Pay is a mobile payment platform and is one of the fastest scaling growth companies in Africa. Over 60% of people in Africa remain unbanked and can’t access the most basic financial services, however, O-Pay allows users to pay for goods and services electronically using their mobile phone via the O-Pay App. Along with mobile payments and transfers, users can also access ridesharing and food delivery.

O-Pay is one example of a company that has utilized data and targeted advertising to give its target market access to the right opportunities at the right time.

The application of data science in eCommerce

1. Recommendation engines. Recommendation engines use machine learning and deep learning algorithms and aim to track the individual behavior of a customer, including their consumption patterns. Using this data, they then offer targeted suggestions. Netflix and Amazon are just two of the major companies using this model.

2. Market basket analysis. Retailers have been using this traditional data analytics tool for many years. It analyses available data to predict what the chances are of a customer making a purchase and for what item. For example, if a customer buys a novel in a three-part novel series, they are more or less likely to buy the additional novels in that series.

3. Warranty analytics. Warranty analytics helps retailers and manufacturers monitor their products for their potential longevity, problems, returns, and possible fraudulent activity. Analyzing this data not only helps to reduce warranty costs but also can improve customer satisfaction.

4. Price optimization. Machine learning can also be used to determine the optimal price for a product that considers all relevant parties, including the customer, manufacturer, and competitor. The algorithm analyses data from factors such as price flexibility, customer demographics, the buying attitude of an individual customer, and competitor pricing to determine this figure.

5. Inventory management. In order to increase sales, confirm timely delivery, and manage their inventory stock, retailers can also utilize machine learning algorithms to analyze corresponding data and develop successful strategies.

6. Location of new stores. Before a business determines where they should open up their stores, they must rely on location analysis through data analytics. Relevant data includes zip code demographics as well as competitor locations. Through machine learning algorithms, an analyst can determine the potential of the market.

7. Customer sentiment analysis. Ultimately, this involves monitoring social media and processing the language used by consumers in regard to a brand. This language is closely tied to whether consumers have a negative or positive attitude towards the brand. Rather than perform this analysis manually, machine learning algorithms help simplify and automate the process, while giving accurate results.

8. Merchandising. Using machine learning algorithms, a company can also determine strategies for successfully promoting their product and increasing sales. Data such as insights about customer buying habits as well as account seasonality, relevancy, and trends are used to formulate this.

9. Lifetime value prediction. By taking data from customer preferences, spends, recent purchases, and behavior, two significant customer methodologies of lifetime prediction can be made: historical and predictive. The possible value of existing and potential customers can be determined, as well as any relationships between the customer’s characteristics and their choices.

Case study: Airbnb

Airbnb is a great example of an eCommerce company using data science to boost its business. Specifically, the company uses data to improve its search function and ensure it’s user-data-driven. They used a rich dataset that consisted of guest and host interactions and built a model that projected a conditional probability of booking in a location, given where the person searched.

Airbnb also uses data to tailor its search experience demographically. Users in some Asian countries, for example, are shown the top traveling destinations in China, Japan, Korea, and Singapore, rather than the typical “Neighborhood” link. Using data to inform this decision, Airbnb saw a 10% lift in conversions from users from those countries.

Why is data science important?

By now, you’re probably starting to understand the significant role data science plays on a global scale. Let’s take a look at some additional reasons why data science is so important.

1. Deeper connections with customers. Data science allows companies to connect with their customers on a deeper and more meaningful level than what was once possible, thanks to the analysis of available data. This data paints a more comprehensive picture of a company’s target customer, and this understanding plays an essential role in the success of a product or service.

2. More powerful marketing. It also creates better product connections by providing the data needed for an organization to tell its brand story more powerfully. Data science can answer a myriad of questions about a company’s target audience, therefore allowing them to tweak their marketing messages and brand identity accordingly.

3. Applicable to every industry. Data science and its results can be applied to any industry, whether it’s education, travel, healthcare, and more. With the help of a data scientist, every field can use data to make better-informed decisions for their customers and address challenges more successfully.

4. Data is an unlimited resource. The availability of data on a global scale is increasing by the second, so it’s a resource that will never become limited. When this data is utilized correctly and to its full potential by a data scientist, it holds the key to unlimited potential and growth.

5. Utilized across every department. Data science can also be utilized across every department of an organization, meaning that it has the potential to assist every team. From human resources and IT to resource management and customer service — data science isn’t just a field of study that assists senior leadership roles only. Everyone has the potential to benefit.

6. Can cut down existing costs. Along with helping a company to make more money by boosting its sales, data science can also reveal how a company can save money by cutting down on existing costs. A data scientist can use data to quantify the success of current procedures, tools, or technology. Using this data, they can also suggest alternative methods which are more successful, yet also most cost-effective.

Data science model

A data science model organizes data elements and regulates how the data elements relate to one another. Put simply, a data model represents reality, as its data elements document people, places, events, actions, and just about everything else related to real life.

Take your morning commute, for example. After driving the same route to work over and over again, you begin to modify your own driving behavior to optimize your journey, whether it’s locating the fastest lane or leaving slightly earlier to avoid traffic congestion.

When comparing this to a scientist’s data model, your experience as a driver equals the data and your brain developing better driving patterns is just like a computer. The data model, therefore, is an equation of data inputs affecting how long it takes you to get to work.

How to build a data science model

There are 6 key steps involved in building a data model. These include:

1. Data Extraction

Start collecting data relevant to the business problem you are about to solve. There are numerous online data repositories that you can utilize, including:

  • Kaggle, for data science projects
  • UCI ML Repository, a machine learning archive
  • Dataset Search Engines, the Google-based dataset search
  • NCBI, the academic research platform for Biotechnology

2. Data cleaning

Data cleaning removes errors that can negatively impact your data model. These might include:

  • Duplicated entries
  • Inaccurate input data
  • Data entries that were modified, updated, or deleted
  • Missing values

You can effectively remove these errors through:

  • Filtering out duplicates by referring to the common IDs
  • Paying attention to the date data was updated
  • Filling in any missing entries with the average value

3. Analyze the essential patterns involved

Build an interactive dashboard to see how your data reflects important insights. This will allow you to analyze what is guiding the variable features of your business, such as increases or decreases.

Two helpful tools you can use to complete this step include:

  • Tableau
  • Micro strategy

4. Identify the critical features

The prerequisite for finalizing a suitable machine learning algorithm is to identify critical features across two categories:

  • Constant features that are less likely to change over time
  • Variable features whose values fluctuate periodically

Known as feature engineering, this step requires a data scientist to decide which factors to test, and ultimately include or exclude, when building their data model.

5. Utilize a machine learning algorithm

A machine learning algorithm will help you to build a functioning data model. An algorithm is simply a set of instructions provided to the computer system to execute a particular task. There are multiple machine learning algorithms to choose from, including:

  • Supervised Learning, which uses algorithms known as linear regression, random forest, and support vector machines
  • Unsupervised Learning, which uses algorithms known as k-means and apriori algorithm
  • Reinforcement Learning, which uses algorithms known as Q-Learning, State-Action-Reward-State-Action (SARSA), and Deep Q Network

6. Evaluate and deploy the model

You need to authenticate the algorithm to check if it generates the desired results for your business. Techniques such as cross-validation or ROC (Receiver operating characteristic) curve can prove to be helpful.

If the model is functioning correctly, then you may go ahead and implement it. If there appeares to be issues, however, revise the previous steps to determine where something may have gone wrong.

What is a data scientist?

A data scientist examines, processes, and models data, then interprets the findings to create actionable plans for organizations. They must work with large sets of data — both structured and unstructured — from multiple sources, including social media feeds, mobile devices, emails, and more.

Often, this data can be complex and won’t fit neatly into a typical database. They must therefore draw on their knowledge from fields including computer science, statistics, and mathematics while utilizing their skills in both technology and social science to find trends among the data and uncover successful solutions to business challenges.

Data scientists work collaboratively with other departments throughout their organization, such as marketing, customer success, and operations.

Along with making data-driven organizational decisions, data scientists must also be able to communicate complex ideas, work as leaders and team members, and be high-level analytical thinkers.

Typically, a data scientist’s duties and responsibilities may include:

  • Resolving business challenges through undirected research and determining open-ended industry questions
  • Extracting large amounts of structured and unstructured data from relational databases, as well as unstructured data through web-scraping, APIs, and surveys
  • Organizing data for use in predictive and prescriptive modeling through complex analytical methods, machine learning, and statistical methods
  • Cleaning data to prepare it for pre-processing and modeling
  • Establishing how to manage missing data and look for trends and/or opportunities in datasets
  • Discovering new algorithms to assist with automating repetitive work and solving other business problems
  • Creating data visualizations and reports, as well as communicating predictions and results to management staff and other departments
  • Proposing cost-effective and time-saving changes to existing company procedures and strategies

Data Scientists vs. Data Analysts vs. Data Engineers

The role of a data scientist is often confused with other disciplines, including data analysts and data engineers. While each of these positions shares a number of similarities, they also differ in numerous ways. Let’s explore each of these below.

Data analysts

It’s easy to see why data scientists and data analysts are sometimes mistaken for each other. Data analysts not only have a similar educational background, but their duties also overlap and include the following:

  • Accessing and performing a range of simple to complex database queries
  • Resolving or removing incorrect, corrupted, improperly formatted, duplicate, or incomplete data within a dataset
  • Summarizing data
  • Understanding and using relevant statistics and mathematical techniques
  • Preparing data visualizations and reports

Data analysts also differ from data scientists in several ways, however, including:

  • Data analysts are typically not responsible for some of the tasks involved in the data science process, such as statistical modeling and machine learning.
  • Data analysts use different tools, including Microsoft Excel, Tableau, SAS, SAP, Qlik, IBM SPSS Modeler, Rapid Miner, and KNIME. Data scientists, on the other hand, utilize tools such as R and Python to perform their duties.
  • Data analysts are often given questions and goals from management, perform the analysis and then report their findings back to management. Data scientists, however, produce the questions themselves and are led by knowing which business goals are most important and how the data can be used to achieve these particular goals.
  • Data scientists usually utilize much more advanced statistics, analytics, and modeling techniques compared to data analysts

Data engineers

There is some overlap between data engineers and data scientists when it comes to their skills and required duties. These include:

  • A shared background in Computer Science
  • Intermediate to advanced data analysis skills
  • Experience and knowledge in programming
  • A natural talent for working with Big Data and unstructured datasets
  • Some tools, languages, and software overlap, such as Scala, Java, and C#

Although data engineers are often confused with data scientists, these two professions also share some differences.

  • Data engineers are more concerned with data architecture, computing and data storage infrastructure, and data flow, rather than the areas that data scientists concentrate on, such as statistics, analytics, and modeling.
  • A data engineer’s focus is also on creating infrastructure and architecture for generating data. Data scientists, on the other hand, focus on using advanced mathematics and statistical analysis on that generated data. 
  • A data engineer is responsible for moving and transforming this data into channels for data scientists. Data Scientists then must analyze, test, gather, and optimize the data, as well as present it to the company.
  • A data engineer’s toolkit also differs vastly from that of a data scientist’s. For example, a data engineer mostly uses SQL, MySQL, NoSQL, Cassandra, and other data organization services, whereas a data scientist will be working heavily with sophisticated analysis tools such as R, SPSS, Hadoop, and advanced statistical modeling.

The process of data science lifecycle

The lifecycle of a data science project is lengthy and can take several months to complete. This is why it’s essential for data scientists to have a recommended structure to follow for each step of the way. Known as the Cross-Industry Standard Process for Data Mining (or CRISP-DM framework, for short), this process is outlined below.

1. Business Understanding

Determine business objectives. The first step of the data science project’s life cycle is to understand the objectives and requirements from a business perspective. What does the customer really want to accomplish? You can then define the project success criteria based on this information.

Assess situation. Determine what resources are available, the requirements of the project, identify possible risks or obstacles, and perform a cost-benefit analysis.

Define data mining goals. What does success look like from a technical data mining perspective? Along with determining the business goals for the project, it’s also important to think about the task at hand from a data mining standpoint.

Create a project plan. Determine what technology and tools you’ll need to use, then produce detailed plans for each phase of the project. This groundwork is vital and will ensure you remain on track throughout the longevity of the data science project.

2. Data Understanding

Collect preliminary data. Obtain the necessary data for this project, and if required, load it into your analysis tool to begin working on it.

Describe data. Analyze the data and document what has been acquired, including the format of the data, the quantity of data, the identities of the fields, and any other important elements. Assess whether the data obtained satisfies the project’s requirements.

Explore data. Delve into the data even further and address data mining questions using techniques such as querying, visualization, and reporting.

Verify data quality. Answer important questions such as:

  • Is the data complete?
  • Is the data correct, or does it contain errors?
  • If there are errors in the data, and if so, how common are they?
  • Are there missing values in the data? If so, where do they occur, and how common are they?

3. Data Preparation

Select data. Decide which data sets will be used and write down your reasons for including or excluding certain data.

Clean data. Using appropriate analysis techniques, clean the data to correct, assign, or remove inaccurate values. This can be a lengthy task but is necessary for a successful outcome.

Construct data. Create derived attributes. These are new attributes that are constructed from one or more existing attributes in the same record. An example is using height and weight fields to determine someone’s Body Mass Index.

Integrate data. Combine data from multiple different sources to create new data sets.

Format data. Reformat the data in any way necessary. This might include removing commas from within text fields in comma-delimited data files or trimming all values to a maximum of 32 characters, for example.

4. Exploratory Data Analysis

Define factors. Examine the solution and factors affecting the data before building the actual model.

Data visualization. Use data visualization techniques to explore features of the data, such as bar graphs, scatter plots, and heat maps.

5. Data Modeling

Select modeling techniques. Decide which algorithms you will be using and record the specific modeling techniques to be used.

Produce test design. Create a procedure or mechanism to test the model’s quality and validity.

Build model. Run the modeling tool on the prepared dataset to create one or more models.

Evaluate model. Drawing on domain knowledge, the data mining success criteria, and the desired test design, assess the model’s success.

6. Model Evaluation

Evaluate results in regard to business objectives. Determine:

  • Do the models meet the business success criteria?
  • Are there any business reasons why this model is unsatisfactory?
  • Which one(s) should be approved for the business?

Review process. Evaluate the work completed so far and review quality assurance issues. Determine if any important factor or task has been overlooked. Ask:

  • Did we correctly build the model?
  • Were all steps properly executed?
  • Did we use only the attributes we are allowed to use and are these accessible for future analyses?

Correct any issues that you’ve discovered.

Determine next steps. Depending on your findings during your review, you must decide what the next logical step in this process should be: proceed to deployment, go over the process further, or commence new projects.

7. Model Deployment

Plan deployment. Develop your deployment strategy, including the required steps and how you will carry them out.

Plan monitoring and maintenance. Develop a detailed monitoring and maintenance plan to avoid problems during the operational or post-project stage of a model.

Create a final report. The science data project team must write up a summary of the project, including its experiences, which may also include a final and in-depth presentation of the data mining results.

Review project. The final step in the science data project lifecycle is to think retrospectively about the project and determine:

  • What went right?
  • What went wrong?
  • What was done well?
  • What needs to be improved?


Data science might be a relatively new field of study, but it has quickly become a significant one in the business world. Not only is it being utilized across every industry on a global scale, but it also provides the key to overcoming a number of business problems and achieving success like never before. 

Through understanding what data science is, why it matters to businesses, and the various applications and examples of this field, you now have the thorough knowledge under your belt to start embracing data science and its benefits in your own venture. 

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