What is Data Science ?
Data Science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. It helps you to discover hidden patterns from the raw data.
Data Science is an interdisciplinary field that allows you to extract knowledge from structured or unstructured data. This technology enables you to translate a business problem into a research project and then translate it back into a practical solution. The term Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data.
What is Machine Learning ?
Machine Learning is a system that can learn from data through self-improvement and without logic being explicitly coded by the programmer. The breakthrough comes with the idea that a machine can singularly learn from the example (i.e.,data) to produce accurate results.
Machine learning combines data with statistical tools to predict an output. This output is then used by corporations to make actionable insights. Machine learning is closely related to data mining and Bayesian predictive modeling. The Machine receives data as input, uses an algorithm to formulate answers.
Check the following key differences between Machine Learning vs Data Science.
KEY DIFFERENCE
- Data Science extracts insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. On the other hand, Machine Learning is a system that can learn from data through self-improvement and without logic being explicitly coded by the programmer.
- Data science can work with manual methods, though they are not very useful while Machine learning algorithms are hard to implement manually.
- Data science is not a subset of Artificial Intelligence (AI) while Machine learning technology is a subset of Artificial Intelligence (AI).
- Data science technique helps you to create insights from data dealing with all real-world complexities while Machine learning method helps you to predict the outcome for new database values.
Roles and Responsibilities of a Data Scientist
Here, are an important skill required to become Data Scientist
- Knowledge about unstructured data management
- Hands-on experience in SQL database coding
- Able to understand multiple analytical functions
- Data mining used for Processing, cleansing, and verifying the integrity of data used for analysis
- Obtain data and recognize the strength
- Work with professional DevOps consultants to help customers operationalize models
Role and Responsibilities of Machine Learning Engineers
Here, are an important skill required to become Machine learning Engineers
- Knowledge of data evolution and statistical modelling
- Understanding and application of algorithms
- Natural language processing
- Data architecture design
- Text representation techniques
- In-depth knowledge of programming skills
- Knowledge of probability and statistics
- Design machine learning systems and knowledge of deep learning technology
- Implement appropriate machine learning algorithms and tools
Difference Between Data Science and Machine Learning
Here are the major differences between Data Science vs Machine learning:
| Data Science | Machine Learning |
| Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge from many structural and unstructured data. | Machine learning is the scientific study of algorithms and statistical models. This method is used to perform a specific task. |
| Data science technique helps you to create insights from data dealing with all real-world complexities. | Machine learning method helps you to predict the outcome for new databases from historical data with the help of mathematical models. |
| Nearly all of the input data is generated in a human-readable format, which is read or analyzed by humans. | Machine learning algorithms are hard to implement manually. |
| Data science can work with manual methods as well, though they are not very useful. | Machine learning algorithms are hard to implement manually. |
| Data science can work with manual methods as well, though they are not very useful. | In Machine Learning, GPUs are used for intensive vector operations. |
| Data science is not a subset of Artificial Intelligence (AI). | In Machine Learning, GPUs are used for intensive vector operations. |
| In Data Science, high RAM and SSD are used, which helps you to overcome I/O bottleneck problems. | In Machine Learning, GPUs are used for intensive vector operations. |
Challenges of Data Science Technology
Here, are important challenges of Data Science Technology
- The wide variety of information & data is needed for accurate analysis
- Not adequate data science talent pool available
- Management does not provide financial support for a data science team.
- Unavailability of/difficult access to data
- Data Science results not effectively used by business decision-makers
- Explaining data science to others is difficult
- Privacy issues
- Lack of significant domain expert
- If an organization is very small, it can’t have a data science team.
Challenges of Machine Learning
Here, are primary challenges of Machine learning method:
- It lacks data or diversity in the dataset.
- Machines can’t learn if there is no data available. Besides, a dataset with a lack of diversity gives the Machine a hard time.
- A machine needs to have heterogeneity to learn meaningful insight.
- It is unlikely that an algorithm can extract information when there are no or few variations.
- It is recommended to have at least 20 observations per group to help the Machine learn.
- This constraint may lead to poor evaluation and prediction.
Data Science or Machine Learning – Which is Better?
The machine learning method is ideal for analyzing, understanding, and identifying a pattern in the data. You can use this model to train a machine to automate tasks that would be exhaustive or impossible for a human being. Moreover, machine learning can make decisions with minimal human intervention.
On the other hand, data science can help you to detect fraud using advanced machine learning algorithms. It also helps you to prevent any significant monetary losses. It helps you to perform sentiment analysis to gauge customer brand loyalty.