What is Data Science? Introduction, Concepts & Process

By : Evelyn Clarke Evelyn Clarke
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⚡ Smart Summary

Data Science extracts insight from large volumes of structured and unstructured data using statistics, visualization and machine learning. Its six-stage process, core job roles, tool stack and main business applications are set out below.

  • 🔘 Definition: An interdisciplinary field that turns raw data into knowledge a business can act on.
  • ☑️ Four components: Statistics, visualization, machine learning and deep learning underpin every project.
  • ✅ Six stages: Discovery, preparation, model planning, model building, operationalize and communicate results.
  • 🧪 Roles: Data scientist, engineer, analyst, statistician, architect, admin, business analyst and analytics manager.
  • 🛠️ Tooling: R, Python, SAS and Spark for analysis; Hadoop and Hive for storage; Tableau for visuals.
  • ⚠️ Constraint: Talent shortages, restricted data access and privacy rules limit what teams can deliver.
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What is Data Science

What is Data Science?

Data Science is the area of study which involves extracting insights from vast amounts of data using various scientific methods, algorithms, and processes. It helps you to discover hidden patterns in the raw data. The term Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data.

Data Science is an interdisciplinary field that allows you to extract knowledge from structured or unstructured data. Data science enables you to translate a business problem into a research project and then translate it back into a practical solution.

Why Data Science?

Here are significant advantages of using Data Analytics Technology:

  • Data is the oil for today’s world. With the right tools, technologies and algorithms, we can use data and convert it into a distinct business advantage
  • Data Science can help you to detect fraud using advanced machine learning algorithms
  • It helps you to prevent any significant monetary losses
  • Allows you to build intelligence into machines
  • You can perform sentiment analysis to gauge customer brand loyalty
  • It enables you to take better and faster decisions
  • It helps you to recommend the right product to the right customer to enhance your business

The timeline below shows how the discipline grew out of statistics and analytics.

Timeline showing the evolution of data science from statistics to big data
Evolution of Data Science

Data Science Components

The diagram below summarises the four building blocks.

Four components of data science: statistics, visualization, machine learning and deep learning

Statistics

Statistics is the most critical unit of Data Science basics, and it is the method or science of collecting and analyzing numerical data in large quantities to get useful insights.

Visualization

Visualization technique helps you access huge amounts of data in easy to understand and digestible visuals.

Machine Learning

Machine Learning explores the building and study of algorithms that learn to make predictions about unforeseen or future data. It splits broadly into supervised and unsupervised techniques.

Deep Learning

Deep Learning is a machine learning approach in which layered neural networks learn the features themselves, so the algorithm selects the analysis model to follow.

Data Science Process

Now in this Data Science Tutorial, we will learn the Data Science Process. The six stages below run in the order shown:

Six-stage data science process from discovery to communicating results

1. Discovery

Discovery step involves acquiring data from all the identified internal & external sources, which helps you answer the business question.

The data can be:

  • Logs from webservers
  • Data gathered from social media
  • Census datasets
  • Data streamed from online sources using APIs

2. Preparation

Data can have many inconsistencies like missing values, blank columns and an incorrect data format, all of which need to be cleaned. You need to process, explore, and condition data before modeling. The cleaner your data, the better your predictions.

3. Model Planning

In this stage, you need to determine the method and technique to draw the relation between input variables. Planning for a model is performed by using different statistical formulas and visualization tools. SQL analysis services, R, and SAS/ACCESS are some of the tools used for this purpose.

4. Model Building

In this step, the actual model building process starts. Here, the data scientist splits the data set into training and testing subsets. Techniques like association, classification, and clustering are applied to the training data set. The model, once prepared, is tested against the “testing” dataset.

5. Operationalize

You deliver the final baselined model with reports, code, and technical documents in this stage. The model is deployed into a real-time production environment after thorough testing.

6. Communicate Results

In this stage, the key findings are communicated to all stakeholders. This helps you decide if the project results are a success or a failure based on the inputs from the model.

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Data Science Jobs Roles

Most prominent Data Scientist job titles are:

  • Data Scientist
  • Data Engineer
  • Data Analyst
  • Statistician
  • Data Architect
  • Data Admin
  • Business Analyst
  • Data/Analytics Manager

Let’s learn what each role entails in detail:

Data Scientist

Role: A Data Scientist is a professional who manages enormous amounts of data to come up with compelling business visions by using various tools, techniques, methodologies, algorithms, etc.

Languages: R, SAS, Python, SQL, Hive, MATLAB, Pig, Spark

Data Engineer

Role: The role of a data engineer is working with large amounts of data. They develop, construct, test, and maintain architectures such as large scale processing systems and databases.

Languages: SQL, Hive, R, SAS, MATLAB, Python, Java, Ruby, C++, and Perl

Data Analyst

Role: A data analyst is responsible for mining vast amounts of data. They will look for relationships, patterns and trends in data. Later they will deliver compelling reporting and visualization for analyzing the data to take the most viable business decisions.

Languages: R, Python, HTML, JS, C, C++, SQL

Statistician

Role: The statistician collects, analyses, and understands qualitative and quantitative data using statistical theories and methods.

Languages: SQL, R, MATLAB, Tableau, Python, Perl, Spark, and Hive

Data Administrator

Role: Data admin should ensure that the database is accessible to all relevant users. They also ensure that it is performing correctly and keep it safe from hacking.

Languages: Ruby on Rails, SQL, Java, C#, and Python

Business Analyst

Role: This professional needs to improve business processes. He/she is an intermediary between the business executive team and the IT department.

Languages: SQL, Tableau, Power BI, and Python

Also, read our Data Science Interview Questions and Answers for practice before an interview.

Tools for Data Science

The tools split into four groups, shown below.

Data science tool categories for analysis, warehousing, visualization and machine learning

Data Analysis Data Warehousing Data Visualization Machine Learning
R, Spark, Python and SAS Hadoop, SQL, Hive R, Tableau, Raw Spark, Azure ML Studio, Mahout

Difference Between Data Science with BI (Business Intelligence)

The table below shows how the two differ.

Parameters Business Intelligence Data Science
Perception Looking Backward Looking Forward
Data Sources Structured data, mostly SQL, and sometimes a data warehouse Structured and Unstructured data.
Like logs, SQL, NoSQL, or text
Approach Statistics & Visualization Statistics, Machine Learning, and Graph
Emphasis Past & Present Analysis & Natural Language Processing
Tools Pentaho, Microsoft BI, QlikView R, TensorFlow

Also, read the difference between Data Science and Machine Learning.

Applications of Data Science

Some applications of Data Science are:

Internet Search

Google search uses Data science technology to search for a specific result within a fraction of a second.

Recommendation Systems

To create a recommendation system. For example, “suggested friends” on Facebook or “suggested videos” on YouTube, everything is done with the help of Data Science.

Image & Speech Recognition

Speech recognition systems like Siri, Google Assistant, and Alexa run on the Data science technique. Moreover, Facebook recognizes your friend when you upload a photo with them, with the help of Data Science.

Gaming world

EA Sports, Sony, Nintendo are using Data science technology. This enhances your gaming experience. Games are now developed using Machine Learning techniques, and they can update themselves when you move to higher levels.

Online Price Comparison

PriceRunner, Junglee, Shopzilla work on the Data science mechanism. Here, data is fetched from the relevant websites using APIs.

Challenges of Data Science Technology

  • A high variety of information & data is required for accurate analysis
  • An adequate data science talent pool is not available
  • Management does not provide financial support for a data science team
  • Unavailability of, or difficult access to, data
  • Business decision-makers do not effectively use data science results
  • Explaining data science to others is difficult
  • Privacy issues
  • Lack of a significant domain expert
  • If an organization is very small, it cannot have a Data Science team

FAQs

Data analytics interrogates existing data to explain what happened and why, usually through reporting and dashboards. Data science builds models that predict what will happen next, and leans harder on programming, statistics and machine learning.

Employers look for a quantitative degree or an equivalent portfolio, fluency in Python or R, SQL and statistics. Domain knowledge often matters as much as the credential.

Python is the safer first choice because it also covers engineering, deployment and deep learning. R remains stronger for classical statistics and academic research. Most working teams read both.

You need descriptive statistics, probability, hypothesis testing and linear algebra. Calculus matters mainly when you design or tune models. Proofs are rare, but formulas must be readable to you.

Raw records arrive with missing fields, duplicates, inconsistent units and wrong types. Each flaw propagates into the model, so teams spend a large share of the schedule fixing them first.

A data scientist frames the question, explores data and validates models in a research setting. A machine learning engineer takes the validated model and makes it run reliably in production, with monitoring, retraining and scale in mind.

Generative AI drafts exploratory code, summarises data sets and suggests features, compressing routine analysis. Judgement about which question to ask, and whether a result can be trusted, stays human.

GitHub Copilot autocompletes pandas, scikit-learn and plotting code inside Jupyter and VS Code, and explains unfamiliar functions. Verify every suggestion against your own data — it cannot see your columns or their meaning.

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