Data Science Vs Machine Learning… What to choose?

Data Science and Machine Learning are the most popular buzzwords that we hear nowadays in the field of Computer Science. Many people are obscure about the differences and similarities these two fields possess between them.

Data Science Vs Machine Learning

In this present article, you will get the answers to all the below questions:

1. What actually data science and Machine Learning are?
2. What are the skill requirements for both the fields?
3. What are the limitations of the individual field?
4. How are these two fields different from each other?
5. And What job positions do you get in each field?

What is Data Science?

Data Science is a broad field of study pertaining to data systems and processes, aimed at maintaining data sets and deriving meaning out of them. Create insights from data dealing with all real-world complexities…is what data science does. To put in simple words - it is a field that unifies statisics, data analysis and other related methods inorder to analyze the real world phenomena of the data.

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. In a nutshell Machine Learning is :

ML WORKFLOW

Technical Skill Requirements

Data Science:

• Powerful knowledge of Python, SAS, R , Scala programming language.
• Good understanding of Statistics.
• Hands-on experience in SQL database coding.
• Data Mining, Cleaning and Data Visualization.
• Understand SQL databases.
• Unstructured data management techniques.
• Use big data tools like Hadoop, Hive and Pig.

Machine Learning:

• Knowledge of probability and statistics.
• Understanding and application of algorithms.
• Data modeling and evaluation skills, Data architecture design.
• Natural language processing.
• Text representation techniques.
• Expertise in computer fundamentals.

Limitations

Data Science:

• Data dependent
• Small datasets, messy data, and incorrect data can waste a lot of time, creating models that produce meaningless or misleading results.
• If the data doesn’t capture the actual cause of variation, data science will fail.

Machine Learning:

• Requirement of different types of new algorithm for new problems.
• Optimization of Algorithms is quite a tough task.
• Require large amounts of hand-crafted, structured training data.

Differences

Difference based upon certain parameters

Job Positions

Data Science:

• Data Scientist
• Data Analyst
• Data Science Engineer

Machine Learning:

• Machine Learning Engineer
• Artificial Intelligence Architect (AI knowledge required)
• AI Research Specialist (AI knowledge required)

References

Most Useful Difference Between Data Science vs Machine learning

Advantages and Disadvantages of Machine Learning Language - DataFlair

What is Machine Learning? A definition

Data Science vs. Machine Learning - Master's in Data Science