The principal distinction: Machine learning Versus data science

One of the most popular confusions occurs among modern technologies which include artificial intelligence, data science, machine learning, deep learning and more. While they are all closely interconnected, each has a separate purpose and functionality. Over the recent years, the popularity of these technologies has expanded to such an extent that numerous companies have now woken up to their importance on huge levels and are increasingly looking to execute them for their business development.

However, among candidates, there seem to be clouds of misunderstandings surrounding these technologies, especially between Machine learning and Data Science. To clear this confusion you can join Data science course in Noida.

This post will help you get a clear picture of what the two diverse yet closely associated technologies are all about.

DIFFERENCE BETWEEN DATA SCIENCE AND MACHINE LEARNING                                  

Data science is an evolutionary branch of statistics able to deal with large amounts of data with the help of computer science technologies. Normally, Machine Learning is used synonymously with Data Science which is incorrect. Though Machine Learning is a significant area under Data science, it is not the only one. Data science covers a broad range of data technologies including Python, SQL, Hadoop, R, and Spark etc. You can learn more about both the fields by Machine learning training in Noida.

OVERLAP BETWEEN DATA SCIENCE AND MACHINE LEARNING

Because data science is a comprehensive term for various disciplines, machine learning matches within data science. Machine learning uses different techniques like supervised clustering and regression. On the other hand, ‘data’ in data science may or may not emerge from a machine or a mechanical method. So, the principal difference between the two is that data science as a wider term not only lay emphasis on statistics and algorithms but also takes care of the whole data processing methodology. Further understanding of data science can be gained by Data science training in Noida.

Thus, data science can be seen as the incorporation of various parental disciplines, including software engineering, data analytics, machine learning, data engineering, business analytics, predictive analytics, and more.  Machine learning and Data analytics are two of the many tools and methods that data science uses. You can join Machine learning course in Noida to learn this tool.

HEAD TO HEAD COMPARISON BETWEEN MACHINE LEARNING AND DATA SCIENCE

Scope: Scope of Data science includes creating insights from data dealing with all real-world complexities.  On the other hand, ML scope includes classifying and predicting accurately the outcome for new data point by learning patterns from historical data, using mathematical models.

Input Data: In Data Science, Most of the input data is generated as human consumable data which is to be read or analyzed by humans like tabular data or images. Input data for ML will be transformed particularly for algorithms used. Word embedding, Feature scaling, or adding polynomial features are some of the examples.

Preferred Skill set: Domain expertise, Strong SQL, ETL and data profiling, NoSQL systems are some of the skills required in Data Science whereas, in ML, skills required are Strong Math understanding, Data wrangling with SQL, Python/R programming and Data wrangling with SQL etc.

Hardware Description:  In Data Science, Horizontally scalable systems preferred to handle massive data and High RAM and SSDs used to overcome I/O bottleneck. In ML, GPUs are preferred for intensive vector operations and more powerful versions like TPUs are on the way.

 Machine learning and Data science and are some of the most in-demand domains in the industry right now. A combination of the right skill sets and real-world experience can help you can secure a strong career in these trending domains.

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