Compare Data Science and Machine Learning (5 Key Differences)
Even after years of schooling, there are most common confusion that some students still face is the basic variance between Data Science and Machine Learning while they are for the most part firmly interconnected but each has a separate determination. Numerous companies are being utilized these technologies due to their gigantic dimensions and progressively hoping to execute for their business development.
This article will be helpful for students as well as general reader to clear the concept of what the two various yet nearly related innovations.
Data Science is the mining and analysis of relevant information from data. It is most widely used technique among Artificial Intelligence and Machine Learning.
For example, when you logged on any e-commerce website and browsed some categories and products before purchase, you are generating data which will be helpful for analysts to know your behavior about purchase.
Data Scientist must have the basic knowledge of mathematics, computer programming and statistics to solve the complex data problems in an efficient way to boost the business revenue.
Key Differences
There is how you can compare between data science and machine learning.
Basic Concept
Data Science is the extraction and analysis of relevant information from data. It deals with the processes of data mining, cleansing, analysis, visualization, and actionable insight generation.
Machine Learning is the part of Data Science, which enables the system to process datasets autonomously without any human interference by utilizing various algorithms to work on massive volume of data generated and extracted from numerous sources.
Benefits
- Data Science helps in finding and refining of target viewers
- Ensure better communication between service providers and service utilizers
- Improved business value and better risk analysis
- Machine Learning is supportive in marketing and predicting accurate sales forecasts
- Helpful in accurate medical diagnoses
- Supportive in elimination of data duplication and erroneous-ness
- Caring in spam detection
- Provide appropriate product recommendation
Required Expertise
- Data Scientist needs
- Programming Skills
- Data warehousing
- Mathematics
- Statistics
- Machine Learning
- Data visualization & communication
- Software Engineering
- Skills for Machine Learning
- Computer Science basics programming skills
- Mathematics
- Statistics
- Data modeling & its assessment
- Software Engineering (SE) and system design
Applications
- Application of Data Science
- Recommender Systems
- Internet Search Engines
- Image Recognition
- Speech Recognition
- Gaming
- Airline route planning
- Comparative analysis of Price
- Fraud and risk detection
- Robotics
- Self-driving Cars
- Application of Machine Learning
- Virtual Personal Assistant
- Video Surveillance
- Online fraud detection
- Social Media Services
- Email Spam and Malware filtering
- Operational Client Support
- Refine Search Engine’s Results
- Product recommendation
Top Tools
- Data Science Tools
- Python
- R (a language for statistical computing and graphics)
- Jupyter Notebook
- Tableau
- Keras
- Machine Learning Tools
- Python
- C++
- R (a language for statistical computing and graphics)
- Jupyter Notebook
- Tableau
Conclusion
Machine learning is a modern technology of data science and it plays a very important role in decision making of the organizations. The importance of data science and machine learning can easily be measured as you can see an upward trend in job creation and number of increasing students who are ready to learn data science as well as machine learning.
Advanced machine learning models provide valuable insights in terms of comparing and analyzing the data through implementing algorithms. Data Science is a very vast field. It deals with whole data processing methods including algorithms, statistics etc. On the other hand, Machine Learning is a part of Data Science.
Data Science uses the machine learning for the purpose of analysis of data and making predictions. Data Scientists use data science technologies combined with other discipline big data to obtain the optimum results.
