Data Scientist vs Machine Learning

Differences Between Data Scientist vs Machine Learning

Data Scientist

A Data Scientist is an expert responsible for collecting, examining and interpreting large volume`of data to recognize ways to help a business improve operations and gain a viable edge over rivals. It follows an interdisciplinary approach. It lies between the connection of Math’s, Statistics, Software Engineering, Artificial Intelligence and Design Thinking. It deals with data collection, cleaning, analysis, visualization, model creation, validation model, experiments prediction, designing, testing and hypothesis many further. The goal of all the steps is just to derive insights from data.

This Data Scientist role is a branch of the statistics role which includes the use of analytics technologies advanced version, including machine learning and predictive modeling, to provide visions beyond statistical analysis. The petition for data science skills has grown-up significantly in recent years as companies look to collect useful information from the huge amounts of structured, semi-structured and unstructured data that a large enterprise produces and collectively referred to as big data.

Standard tasks:

• Allocate, aggregate and synthesize data from various structured and unstructured sources
• Explore, develop and apply intelligent learning to real-world data, provide important findings and successful actions based on them
• Analyze and provide data collected in the organization
• Design and build new processes for modeling, data mining and implementation
• Develop prototypes, algorithms, predictive models, prototypes
• Carry out requests for data analysis and communicate their findings and decisions

In addition, there are more specific tasks depending on the domain in which the employer is working or the project is being implemented.

Raw Data —> Data Science —-> Actionable Insights

Machine Learning

The Machine Learning Engineer position is more “technical”. ML Engineer has more in common with classical Software Engineering than Data Scientist. It consists of a group of intelligent algorithms, machines and enabling them to learn without being clearly programmed for it. It helps you learn the objective function which plots the inputs to the target variable and/or independent variables to the dependent variables.

The standard tasks of ML Engineer are generally like Data Scientist. You also need to be able to work with data, experiment with various Machine Learning algorithms that will solve the task, create prototypes and ready-made solutions.

The required knowledge and skills for this position also overlap with Data Scientist. Of the key differences, I would single out:

• Strong programming skills in one or more popular languages (usually Python and Java), as well as in databases;
• Less emphasis on the ability to work in data analysis environments, but more emphasis on Machine Learning algorithms;
• R and Python for modeling are preferable to Matlab, SPSS, and SAS;
• Ability to use ready-made libraries for various stacks in the application, for example, Mahout, Lucene for Java, NumPy / SciPy for Python;
• Ability to create distributed applications using Hadoop and other solutions.

As you can see, the position of ML Engineer (or narrower) requires more knowledge in Software Engineering, and, accordingly, is well suited for experienced developers. Quite often, the case works when the usual developer must solve the ML task for his duty, and he starts to understand the necessary algorithms and libraries.

Head to Head Comparison Between Data Scientist vs Machine Learning

Below is the top 5 Differences Between Data Scientist vs Machine Learning engineer

Key Difference Between Data Scientist vs Machine Learning

Below are the lists of points, describe the key Differences Between Data Scientist vs Machine Learning engineer

1. Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters. This encompasses many techniques such as regression, naive Bayes or supervised clustering. But not all techniques fit in this category. For instance, unsupervised clustering – a statistical and data science technique – aims at detecting clusters and cluster structures without any a-prior knowledge or training set to help the classification algorithm. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit in this category.
2. Data science is much more than machine learning though. Data, in data science, may or may not come from a machine or mechanical process (survey data could be manually collected, clinical trials involve a specific type of small data) and it might have nothing to do with learning as I have just discussed. But the main difference is the fact that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. Data science also covers data integration, distributed architecture, automated machine learning, data visualization, dashboards and Big data engineering.

Data Scientist vs Machine Learning Comparison Table

Following  are the lists of points, describe the comparisons Between Data Scientist vs Machine Learning engineer:

Conclusion – Data Scientist vs Machine Learning

Machine learning is a division of artificial intelligence that is utilized by data science to attain its objectives

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A Data scientist does a lot of exploration of data and arrives at the broad strategy of how to tackle it. He is responsible for asking questions inside the data and find what answers can one reasonably draw from data. Feature engineering too belongs to a realm of Data Scientist. Creativity also plays a role here and An Machine Learning engineer knows more tools and can build models given a set of features and data – as per directions from the Data Scientist. The realm of Data pre-processing and feature extraction belongs to ML engineer.

Data science and examination utilize machine learning for this kind of archetypal validation and creation. It is vital to note that all the algorithms in this model creation may not come from machine learning. They can arrive from numerous other fields. The model desires to be kept relevant always. If the situations change, then the model which we created earlier may become immaterial. The model requirements to be checked for its certainty at different times and need to be adapted if its certainty reduces.

Data science is a whole big domain. If we try to put it in a pipeline it would have data acquisition, data storage, data preprocessing or data cleaning, learning patterns in data (via machine learning), using learning for predictions. This is one way to understand how machine learning fits into data science.

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