What is Data Science?
Data Science is a field that largely focuses on the derivation of meaningful information from a huge amount of raw data. It is considered to be the roof that encompasses several other technologies such as machine learning, data mining, and data analytics.
- Data Science deals with data that can be structured, semi-structured, and unstructured. More than 80% of the data produced by enterprises is either semi-structured or unstructured, which can not be processed by usual Business Intelligence Tools.
- In Data Science, various scientific disciplines and applications including Computer Science, mathematics, algorithms, and statistics are used to solve complex data problems.
- After a problem is identified, the data is first organized. This is the planning stage that decides how the actual action will be executed.
- The next step is to apply various technologies and strategies including models, algorithms, statistics, and probability to combine and transform the data. This stage is followed by the final stage in which the final result is delivered.
What is Machine Learning?
Machine learning is the branch that teaches a computer to learn and behave like humans and improve over time on the basis of algorithms used and the data provided to it. The accuracy of the decision-making depends upon the supervised algorithms.
- To prepare an ML model, certain specific steps are followed. These models are trained to identify various patterns.
- The foremost step is to prepare a data set for your model. It usually follows an 80-20 rule, which is using 80% data for training, and the rest of 20% data is used for testing and validation.
- The next step is to select the suitable algorithm for your model. It depends on the problem type and the data set type. The most common Machine Learning algorithms include decision tree, Linear Regression, Logistic Regression, neural networks, kNN, and Naive Bayes.
- Next comes the training of the model. It includes running the model against various inputs and re-adjusting it according to the results. This process is repeated until the most accurate results are achieved.
- After training the model, it is tested against new data sets and is improved accordingly to produce accurate results.
Data Science vs Machine Learning: Key Differences
1. Major Goal
Data Science aims to process raw data, refine it, and extract useful and meaningful information from it by using a combination of various tools and technologies including data mining and data analysis. Data Science drives innovation by discovering new questions.
Whereas, Machine Learning focuses on training the machine to behave humanly using various algorithms. It derives the conclusions and solutions using data that already exists.
2. Data Scientist vs Machine Learning Expert: Skillset
To become a Data Scientist, you should have the following skill sets:
- Strong knowledge of computer fundamentals.
- Expertise in skills such as data evaluation and data modeling.
- Clarity on the concepts of statistics and probability.
- Good knowledge of programming skills.
On the contrary, a Machine Learning engineer should have:
- Clarity on the concepts of Machine Learning.
- Understanding of various analytical functions.
- Good knowledge as well as hands-on training in SQL databases.
- Strong grasp of programming languages such as Python, R, Scala, etc.
3. Applications
There are various applications of both Data Science and Machine Learning. Data Science can be used for cybersecurity, fraud detection in banking systems, predicting potential problems in the manufacturing industry, and many more.
On the other hand, Machine Learning's applications are speech and image recognition, traffic prediction, self-driving vehicles, and virtual personal assistants like Siri and Cortana.
4. Future Scope
Data Science has been trending a lot in the market. There are a bunch of high-paying jobs that one can acquire with Data Science. Some of these career-defining jobs are Data Scientist, Data Analyst, Business Analyst, and Data Engineer.
Machine Learning is not behind in the market either. With having so many ML applications, there are so many lucrative career options in ML including Machine Learning Engineer, NLP Scientist, and Software Engineer/Developer.
Conclusion
- In this article, we have explored the two most trending topics of the big data era, i.e., Data Science and Machine Learning, and provided an insightful discussion on them.
- We also talked about their key differences concerning goals, skill sets, applications, and scope.
- Hence, we wrap up here with a conclusion that the future holds a lot of potential for these two fields, and even though Data Science and Machine Learning are said to be separate disciplines, Data Scientists will be having hands-on experience in ML as well.