EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login

Data Science vs Web Development

By Priya PedamkarPriya Pedamkar

Home » Data Science » Data Science Tutorials » Head to Head Differences Tutorial » Data Science vs Web Development

Data Science vs Web Development

Difference between Data Science and Web Development

Investments are crucial for individuals and businesses. They lessen the risk in our lives and act as a cushion in times of need. When it comes to businesses, investments are not just financial but also the ones made of its employees i.e. building teams and image building. There is a quote by Warren Buffet which says-“Someone’s sitting in the shade today because someone planted a tree a long time ago.” True to this quote, businesses have to invest in today to reap the benefits tomorrow. Going by recent trends, we will be discussing two types of investment Data Science and Web Development.

Data Science is the interdisciplinary science if data analysis using statistics, algorithm building, and technology. With recent Data Science trends like Machine Learning and Artificial Intelligence, more companies want to invest in a Data Science team to understand their data better and make wise decisions. Web development is the creation of a website for the internet or intranet. Since a website is the face of a company, it is necessary for companies to invest in one. Also, Web Development companies need to match their skills with the coming trends as businesses have become more E-Based i.e. E-Commerce and E-Learning. This, in turn, is a driving factor for setting up Data Science teams in businesses

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Head to Head Comparison between Data Science and Web Development (Infographics)

Below is the Top 8 comparison between Data Science and Web Development:

Data Science vs Web Development Infographics

Key Differences between Data Science vs Web Development

  • Data Science is the process of analyzing data using specialized skills and technology whereas Web Development is the creation of a website for the internet or intranet using company details, client requirement, and technical skills.
  • Data Science is a relatively new concept having been introduced in 2008 whereas Web development has been around since 1999.
  • Python is used by both Data Scientists and Web Developers. However, in Data Science it is used for analyzing data whereas in web development it is used in creating a website.
  • Data Science uses coding widely but also includes other elements whereas the whole of Web Development is based on coding.
  • There is statistics involved in Data Science whereas in Web Development there is no use for statistics.
  • Data Scientists try to answer business related questions at the end of the analysis whereas Web Developers try to cater to the client requirement while building a website.
  • Data Science depends on the availability of data whereas Web Development depends on close interaction with the client to understand needs and to get the required information.
  • The budget for Data Science is steep but is fixed whereas the budget for Web Development keeps changing with the changing requirement and the additional features.
  • Data Scientists work for a shorter period of time on data to get results in comparison to Web Developers who take a long time to launch a website.
  • Data Scientists work with structured and unstructured data whereas Web Developers work with Company information.
  • With the coming of E-Commerce, Data Scientists have an understanding of websites whereas Web Developers do not possess the skills to work with data.
  • There are a lot of future trends in Data Science like Machine Learning and Artificial Intelligence whereas not many trends in Web Development.

Data Science and Web Development Comparison Table

The differences between Data Science and Web Development are explained in the points presented below:

Basis For Comparison Data Science Web Development
Coining of Term DJ Patil and Jeff Hammerbacher who were employees of LinkedIn and Facebook respectively gave the term Data Science in 2008. The term was popularized by Tim O’Reilly and Dale Dougherty in late 2004. It was initially coined by Darcy DiNucci in 1999.
Concept It is a combination of statistics, algorithms, and technology to analyze data. It is the creation of websites for the intranet which is a public platform or the intranet which is a private platform.
Coding Coding is used widely to feed the computer with commands to analyze data and give the end output. The entire process of web development involves coding.
Languages Recommendations C/C++/C#, Haskell, Java, Julia, Matlab, Python, R, SAS, Scala, SQL, Stata Photoshop, HTML, CSS, JavaScript, JQuery, PHP, Python, Ruby
Statistics Uses statistics to a certain extent. Uses no statistics
Work Challenges
  • Data Science results are not used in business decision making.
  • Inability to apply findings into organizations decision-making process.
  • Low clarity on the questions that need to be answered with the given data set.
  • Unavailability or difficult access to data.
  • Data Security is of top priority.
  • Need to coordinate with IT.
  • The client requirement is never clear and keeps changing till the end site is launched.
  • Need to work closely with a client for site content and requirement.
  • Need to coordinate with IT
  • The budget for the web site building keeps increasing with more features. So no set budget.
  • It takes time to launch a new website.
  • Security factors have to be considered before launching.
Data Needed Structured and unstructured data. No data is required. Only company details is required for web site.
Future Trends Machine Learning and Artificial Intelligence. E-Commerce and E-Learning

Conclusion

Careers are built based on the passion, drive, skills, and opportunities that a person has. In the case of the comparison between Data Science and Web Development, both are in trend and provide students, fresher and experienced professionals a lot of scopes to learn. Data Scientists need to have a sound understanding of statistics and computer science. Coupling this with the voluminous data at hand that the different verticals generate every day, Data Scientists have the opportunity to explore different data sets and help businesses forecast their data to get valuable insights.  Data Science openings are the most sought after openings of today. Web Development, on the other hand, is taking slow steps but the end product of creating a website is fascinating and thrills many. With websites acting as platforms for businesses i.e. E-Commerce, the latter has been a driving factor for the setting up of Data Science Teams. Data Scientists are experts at working with Internet-based data. The comparison of these Data Science and Web Development work areas cannot be done except for a few similarities. However, both Data Science and Web Development keep up with trends and offer great opportunities. 

Recommended Articles

This has been a guide to Data Science vs Web Development. Here we discuss head to head comparison, key dIfferences along with infographics and comparison table. You may also look at the following articles to learn more –

  1.  10 Best Web Development Interview Questions
  2. Data Science Vs Data Engineering – Which One Is More Useful
  3. Amazing Guide On Drupal Web Development
  4.  9 Awesome Difference Between Data Science Vs Data Mining
  5. Get Started With Python and Django for Web Development
  6. Drupal vs Joomla: Functions
  7. SASS Interview Questions: Amazing Questions

All in One Data Science Bundle (360+ Courses, 50+ projects)

Popular Course in this category
Sale
All in One Data Science Bundle (360+ Courses, 50+ projects)360+ Online Courses | 1500+ Hours | Verifiable Certificates | Lifetime Access
4.7 (3,220 ratings)
Course Price

View Course

Related Courses
Data Scientist Training (85 Courses, 67+ Projects)Tableau Training (7 Courses, 8+ Projects)Azure Training (6 Courses, 5 Projects, 4 Quizzes)Hadoop Training Program (20 Courses, 14+ Projects, 4 Quizzes)Data Visualization Training (15 Courses, 5+ Projects)

360+ Online Courses

1500+ Hours

Verifiable Certificates

Lifetime Access

Learn More

5 Shares
Share
Tweet
Share
Primary Sidebar
Head to Head Differences Tutorial
  • Differences Tutorial
    • ArangoDB vs MongoDB
    • Cloud Computing vs Big Data Analytics
    • PostgreSQL vs MariaDB
    • Domo vs Tableau
    • Data Scientist vs Data Engineer vs Statistician
    • Big Data Vs Machine Learning
    • Business Intelligence vs Data Warehouse
    • Apache Kafka vs Flume
    • Data Science vs Machine Learning
    • Business Analytics Vs Predictive Analytics
    • Data mining vs Web mining
    • Data Science Vs Data Mining
    • Data Science Vs Business Analytics
    • Analyst vs Associate
    • Apache Hive vs Apache Spark SQL
    • Apache Nifi vs Apache Spark
    • Apache Spark vs Apache Flink
    • Apache Storm vs Kafka
    • Artificial Intelligence vs Business Intelligence
    • Artificial Intelligence vs Human Intelligence
    • Al vs ML vs Deep Learning
    • Assembly Language vs Machine Language
    • AWS vs AZURE
    • AWS vs Azure vs Google Cloud
    • Big Data vs Data Mining
    • Big Data vs Data Science
    • Big Data vs Data Warehouse
    • Blu-Ray vs DVD
    • Business Intelligence vs Big Data
    • Business Intelligence vs Business Analytics
    • Business Intelligence vs Data analytics
    • Business Intelligence VS Data Mining
    • Business Intelligence vs Machine Learning
    • Business Process Re-Engineering vs CI
    • Cassandra vs Elasticsearch
    • Cassandra vs Redis
    • Cloud Computing Public vs Private
    • Cloud Computing vs Fog Computing
    • Cloud Computing vs Grid Computing
    • Cloud Computing vs Hadoop
    • Computer Network vs Data Communication
    • Computer Science vs Data Science
    • Computer Scientist vs Data Scientist
    • Customer Analytics vs Web Analytics
    • Data Analyst vs Data Scientist
    • Data Analytics vs Business Analytics
    • Data Analytics vs Data Analysis
    • Data Analytics Vs Predictive Analytics
    • Data Lake vs Data Warehouse
    • Data Mining Vs Data Visualization
    • Data mining vs Machine learning
    • Data Mining Vs Statistics
    • Data Mining vs Text Mining
    • Data Science vs Artificial Intelligence
    • Data science vs Business intelligence
    • Data Science Vs Data Engineering
    • Data Science vs Data Visualization
    • Data Science vs Software Engineering
    • Data Scientist vs Big Data
    • Data Scientist vs Business Analyst
    • Data Scientist vs Data Engineer
    • Data Scientist vs Data Mining
    • Data Scientist vs Machine Learning
    • Data Scientist vs Software Engineer
    • Data visualisation vs Data analytics
    • Data vs Information
    • Data Warehouse vs Data Mart
    • Data Warehouse vs Database
    • Data Warehouse vs Hadoop
    • Data Warehousing VS Data Mining
    • DBMS vs RDBMS
    • Deep Learning vs Machine learning
    • Digital Analytics vs Digital Marketing
    • Digital Ocean vs AWS
    • DOS vs Windows
    • ETL vs ELT
    • Small Data Vs Big Data
    • Apache Hadoop vs Apache Storm
    • Hadoop vs HBase
    • Between Data Science vs Web Development
    • Hadoop vs MapReduce
    • Hadoop Vs SQL
    • Google Analytics vs Mixpanel
    • Google Analytics Vs Piwik
    • Google Cloud vs AWS
    • Hadoop vs Apache Spark
    • Hadoop vs Cassandra
    • Hadoop vs Elasticsearch
    • Hadoop vs Hive
    • Hadoop vs MongoDB
    • HADOOP vs RDBMS
    • Hadoop vs Spark
    • Hadoop vs Splunk
    • Hadoop vs SQL Performance
    • Hadoop vs Teradata
    • HBase vs HDFS
    • Hive VS HUE
    • Hive vs Impala
    • JDBC vs ODBC
    • Kafka vs Kinesis
    • Kafka vs Spark
    • Cloud Computing vs Data Analytics
    • Data Mining Vs Data Analysis
    • Data Science vs Statistics
    • Big Data Vs Predictive Analytics
    • MapReduce vs Yarn
    • Hadoop vs Redshift
    • Looker vs Tableau
    • Machine Learning vs Artificial Intelligence
    • Machine Learning vs Neural Network
    • Machine Learning vs Predictive Analytics
    • Machine Learning vs Predictive Modelling
    • Machine Learning vs Statistics
    • MariaDB vs MySQL
    • Mathematica vs Matlab
    • Matlab vs Octave
    • MATLAB vs R
    • MongoDB vs Cassandra
    • MongoDB vs DynamoDB
    • MongoDB vs HBase
    • MongoDB vs Oracle
    • MongoDB vs Postgres
    • MongoDB vs PostgreSQL
    • MongoDB vs SQL
    • MongoDB vs SQL server
    • MS SQL vs MYSQL
    • MySQL vs MongoDB
    • MySQL vs MySQLi
    • MySQL vs NoSQL
    • MySQL vs SQL Server
    • MySQL vs SQLite
    • Neural Networks vs Deep Learning
    • PIG vs MapReduce
    • Pig vs Spark
    • PL SQL vs SQL
    • Power BI Dashboard vs Report
    • Power BI vs Excel
    • Power BI vs QlikView
    • Power BI vs SSRS
    • Power BI vs Tableau
    • Power BI vs Tableau vs Qlik
    • PowerShell vs Bash
    • PowerShell vs CMD
    • PowerShell vs Command Prompt
    • PowerShell vs Python
    • Predictive Analysis vs Forecasting
    • Predictive Analytics vs Data Mining
    • Predictive Analytics vs Data Science
    • Predictive Analytics vs Descriptive Analytics
    • Predictive Analytics vs Statistics
    • Predictive Modeling vs Predictive Analytics
    • Private Cloud vs Public Cloud
    • Regression vs ANOVA
    • Regression vs Classification
    • ROLAP vs MOLAP
    • ROLAP vs MOLAP vs HOLAP
    • Spark SQL vs Presto
    • Splunk vs Elastic Search
    • Splunk vs Nagios
    • Splunk vs Spark
    • Splunk vs Tableau
    • Spring Cloud vs Spring Boot
    • Spring vs Hibernate
    • Spring vs Spring Boot
    • Spring vs Struts
    • SQL Server vs PostgreSQL
    • Sqoop vs Flume
    • Statistics vs Machine learning
    • Supervised Learning vs Deep Learning
    • Supervised Learning vs Reinforcement Learning
    • Supervised Learning vs Unsupervised Learning
    • Tableau vs Domo
    • Tableau vs Microstrategy
    • Tableau vs Power BI vs QlikView
    • Tableau vs QlikView
    • Tableau vs Spotfire
    • Talend Vs Informatica PowerCenter
    • Talend vs Mulesoft
    • Talend vs Pentaho
    • Talend vs SSIS
    • TensorFlow vs Caffe
    • Tensorflow vs Pytorch
    • TensorFlow vs Spark
    • TeraData vs Oracle
    • Text Mining vs Natural Language Processing
    • Text Mining vs Text Analytics
    • Cloud Computing vs Virtualization
    • Unit Test vs Integration Test?
    • Universal analytics vs Google Analytics
    • Visual Analytics vs Tableau
    • R vs Python
    • R vs SPSS
    • Star Schema vs Snowflake Schema
    • DDL vs DML
    • R vs R Squared
    • ActiveMQ vs Kafka
    • TDM vs FDM
    • Linear Regression vs Logistic Regression
    • Slf4j vs Log4j
    • Redis vs Kafka
    • Travis vs Jenkins
    • Fact Table vs Dimension Table
    • OLTP vs OLAP
    • Openstack vs Virtualization
    • Cluster v/s Factor analysis
    • Informatica vs Datastage
    • CCBA vs CBAP
    • SPSS vs EXCEL
    • Excel vs Tableau
    • Cassandra vs MySQL
    • RabbitMQ vs Kafka
    • SAAS vs Cloud
    • RabbitMQ vs Redis
    • AMQP vs MQTT
    • Forward Chaining vs Backward Chaining
    • Google Data Studio vs Tableau
    • ActiveMQ vs RabbitMQ
    • Cloud vs Data Center
    • Cores vs Threads
    • Inner Join vs Outer Join
    • ZeroMQ vs Kafka
    • Mxnet vs TensorFlow
    • Redis vs Memcached
    • RDBMS vs NoSQL
    • AWS Direct Connect vs VPN
    • Cassandra vs Couchbase
    • Elegoo vs Arduino
    • Redis vs MongoDB
    • Chef vs Puppet
    • GSM vs GPRS
    • Keras vs TensorFlow vs PyTorch
    • Cloudflare vs CloudFront
    • Bitmap vs Vector
    • Left Join vs Right Join
    • IaaS vs PaaS
    • Blue Prism vs UiPath
    • GNSS vs GPS
    • Cloudflare vs Akamai
    • GCP vs AWS vs Azure
    • Arduino Mega vs Uno
    • Qualitative vs Quantitative Data
    • Arduino Micro vs Nano
    • PIC vs Arduino
    • PRTG vs Solarwinds
    • PostgreSQL vs SQLite
    • Metabase vs Tableau
    • Arduino Leonardo vs Uno
    • Arduino Due vs Mega
    • ETL Vs Database Testing
    • DBMS vs File System
    • CouchDB vs MongoDB
    • Arduino Nano vs Mini
    • IaaS vs PaaS vs SaaS
    • On-premise vs off-premise
    • Couchbase vs CouchDB
    • Tableau Dimension vs Measure
    • Cognos vs Tableau
    • Data vs Metadata
    • RethinkDB vs MongoDB
    • Cloudera vs Snowflake
    • HBase vs Cassandra
    • Business Analytics vs Business Intelligence
    • R Programming vs Python
    • MongoDB vs Hadoop
    • MySQL vs Oracle
    • OData vs GraphQL
    • Soft Computing vs Hard Computing
    • Binary Tree vs Binary Search Tree
    • Datadog vs CloudWatch
    • B tree vs Binary tree
    • Cloudera vs Hortonworks
    • DevSecOps vs DevOps
    • PostgreSQL Varchar vs Text
    • PostgreSQL Database vs schema
    • MapReduce vs spark
    • Hypervisor vs Docker
    • SciLab vs Octave
    • DocumentDB vs DynamoDB
    • PostgreSQL union vs union all
    • OrientDB vs Neo4j
    • Data visualization vs Business Intelligence
    • QlikView vs Qlik Sense
    • Neo4j vs MongoDB
    • Postgres Schema vs Database
    • Mxnet vs Pytorch
    • Naive Bayes vs Logistic Regression
    • Random Forest vs Decision Tree
    • Random Forest vs XGBoost
    • DynamoDB vs Cassandra
    • Looker vs Power BI
    • PostgreSQL vs RedShift
    • Presto vs Hive
    • Random forest vs Gradient boosting
    • Gradient boosting vs AdaBoost
    • Amazon rds vs Redshift
    • Bigquery vs Bigtable
    • Data Architect vs Data Engineer
    • DataSet vs DataTable
    • dataset vs dataframe
    • Dataset vs Database
    • New Relic vs Splunk
    • Data Architect and Management Designer
    • Data Engineer vs Data Analyst
    • Grafana vs Tableau
    • MySQL text vs Varchar
    • Relational Database vs Flat File
    • Datadog vs Prometheus
    • Neo4j vs Neptune
    • Data Mining vs Data warehousing
    • DocumentDB vs MongoDB
    • PostScript vs PCL
    • QRadar vs Splunk
    • Qlik Sense vs Tableau
    • DigitalOcean vs Google Cloud
    • PostgreSQL vs Elasticsearch
    • Redshift vs blueshift
    • Gitlab vs Azure DevOps

Related Courses

Online Data Science Course

Online Tableau Training

Azure Training Course

Hadoop Certification Course

Data Visualization Courses

All in One Data Science Course

Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more

Special Offer - All in One Data Science Bundle (360+ Courses, 50+ projects) Learn More