Machine Learning and Data Science: Best Explained 007ET

Today we shall be discussing the two significant terms Difference between Machine Learning and Data Science, where we see a considerable difference, their applications, Job trends, skills, and salaries based on your location; however, Machine Learning vs. Data Science is a much talk about without an in-depth analysis, that why Edurary Tech will be providing you a complete comparison of Machine Learning and Data Science.

Difference between Machine Learning and Data Science

Data Science is the study of data cleansing, preparation, and analysis, while machine learning is a branch of AI and a subfield of data science. Data Science and Machine Learning are the two popular modern technologies, and they are growing at an immoderate rate.

But these two buzzwords, along with artificial intelligence and deep learning, are very confusing terms, so it is essential to understand how they are different from each other. This topic will understand the difference between Data Science and Machine Learning and how they relate to each other.

Data Science and Machine Learning are closely related to each other but have different functionalities and different goals. At a glance, Data Science is a field to study the approaches to find insights from the raw data. At the same time, Machine Learning is a technique used by a group of data scientists to enable machines to learn automatically from past data. To understand the difference in-depth, let’s first have a brief introduction to these two technologies.

What is Data Science?

Machine Learning and Data Science
Difference between Machine Learning and Data Science

Data science, as its name suggests, is all about the data. Hence, we can define it as “A field of deep study of data that includes extracting useful insights from the data, and processing that information using different tools, statistical models, and Machine learning algorithms.” It is a concept used to handle big data, including data cleaning, data preparation, data analysis, and data visualization.

A data scientist collects the raw data from various sources, prepares and pre-processes the data, and applies machine learning algorithms, predictive analysis to extract valuable insights from the collected data.

For example, Netflix uses data science techniques to understand user interest by mining its users’ data and viewing patterns.

Skills Required to become a machine learning project vintage colorize or Data Scientist is:

  • An excellent programming knowledge of Python, R, SAS, or Scala.
  • Experience in SQL database Coding.
  • Knowledge of Machine Learning Algorithms.
  • Deep Knowledge of Statistics concepts.
  • Data Mining, cleaning, and Visualizing skills.
  • Skills to use Big data tools such as Hadoop.

What is Machine Learning?

Machine learning is a part of artificial intelligence and the subfield of Data Science. It is a growing technology that enables machines to learn from past data and perform a given task automatically. It can be defined as:

Machine Leaning allows the computers to learn from the past experiences by its own, it uses statistical methods to improve the performance and predict the output without being explicitly programmed.

The widespread applications of ML are Email spam filtering, product recommendations, online fraud detection, etc.

Skills Needed for the Machine Learning Engineer:

  • Understanding and implementation of Machine Learning Algorithms.
  • Natural Language Processing.
  • Good Programming knowledge of Python or R.
  • Knowledge of Statistics and probability concepts.
  • Knowledge of data modeling and data evaluation.

Note: Data Science and Machine Learning are closely related but cannot be treated as synonyms.

Where is Machine Learning used in Data Science?

The use of machine learning in data science can be understood by the development process or life cycle of Data Science. The different steps that occur in the Data science lifecycle are as follows:

Business Requirements: 

In this step, we try to understand the requirement for the business problem for which we want to use it. Suppose we want to create a recommendation system, and the business requirement is to increase sales.

Data Acquisition: 

In this step, the data is acquired to solve the given problem. For the recommendation system, we can get the ratings provided by the user for different products, comments, purchase history, etc.

Data Processing: 

In this step, the raw data acquired from the previous step is transformed into a suitable format so that the further steps can easily use it.

Data Exploration: 

It is a step where we understand the data patterns and try to find out valuable insights from the data.

Modeling: 

Data modeling is a step where machine learning algorithms are used. So, this step includes the whole machine learning process. The machine learning process involves importing the data, cleaning, building a model, training the model, testing the model, and improving the model’s efficiency.

Deployment & Optimization: 

This is the last step where the model is deployed on an actual project, and the performance of the model is checked.

Machine Learning and Data Science Salary

Salary Trends for Data Scientist

The Average Salary of Data Scientists is around $91,470 (US) or ₹693,637 (IND). Let’s have a look at the Salary of a Data Scientist according to the Experience.

ExperienceSalary
Entry Level – IND₹306,054 – ₹1,215,966
Entry Level – US$60,894 – $127,894
Experienced – IND₹972,106 – ₹2,928,194
Experienced – US$79,321 – $167,947

This figure also depends upon a few other factors like the Company one is working for or the Location. But majorly the above table depicts the average salary range for the different levels of experience.

Salary Trends for Machine Learning Engineer

The Average Salary of a Machine Learning Engineer is around $111,490 (US) or ₹719,646 (IND). Let’s see the Salary Compensation of a Machine Learning Engineer.

CompensationSalary
Salary$76,953 – $151,779
Bonus$2,974 – $25,541
Profit Sharing$1,934 – $51,285
Total Pay$76,184 – $162,727
Salary Trends for Data Scientist

So, if we compare the Salary Trends of Machine Learning engineers and Data scientists, we can see that, in general, a Machine Learning Engineer Earns a little more than a Data Scientist. Now one might ask why that is, so we need to look at the skills and the differences in roles between Machine Learning Engineer vs. Data Scientist. But first, let’s have a look at the Job Trends.

Job Trends for Machine Learning and Data Science

Data Scientist Job Trends

LocationNo. of Jobs
Seattle, WA2065
New York, NY1189
San Francisco, CA1107
Bengaluru, Karnataka1101
Data Scientist Job Trends

Machine Learning Engineer Job Trends

LocationNo. of Jobs
New York, NY1813
Seattle, WA1544
San Francisco, CA1487
Cambridge, MA936
Machine Learning Engineer Job Trends

On the one hand, Machine Learning Engineers get slightly more paid than Data scientists; on the other hand, the demand or the Job openings for a Data Scientist is more than that of an ML Engineer. This is because ML Engineers work on Artificial Intelligence, which is comparatively a new domain.

Skills Requirements for Machine Learning Engineer and Data Scientist

Now the skill requirements for Machine Learning Engineer vs. Data Scientist are very similar, so let’s start with the Common Skillsets.

Programming Languages: 

The first and foremost requirement is to have a good grip on a programming language, preferably python, as it is easy to learn and its applications are more comprehensive than any other language.

Although Python is a perfect Language, it alone cannot help you. You will probably have to learn all these languages like C++, R, Python, Java and also work on MapReduce at some point.

Statistics: 

Wikipedia defines it as studying the collection, analysis, interpretation, presentation, and organization of data. Therefore, it shouldn’t be a surprise that Data Scientists and Machine Learning Engineers need to know statistics. Familiarity with Matrices, Vectors, and Matrix Multiplication is required.

Data Cleaning and Visualization: 

Data cleansing is a valuable process that can help companies save time and increase their efficiency. Telling a compelling story with data is crucial to getting your point across and keeping your audience engaged.

If your findings can’t be easily and quickly identified, then you’re going to have a difficult time getting through to others. For this reason, data visualization can have a make-or-break effect when it comes to the impact of your data.

Machine Learning and Neural Network Architectures: 

Machine Learning and predictive modeling are quickly becoming two of the hottest topics. You need to know Machine learning techniques such as supervised machine learning, decision trees, logistic regression, etc. These skills will help you solve different data analytical problems based on predictions of primary organizational outcomes.

Deep Learning has taken traditional Machine Learning approaches to the next level. It is inspired by biological Neurons (Brain Cells). The idea here is to mimic the human brain. An extensive network of such Artificial Neurons is used; this is known as Deep Neural Networks.

Big Data Processing Frameworks: 

A considerable amount of data is required to train Machine Learning/ Deep Learning models. Because of the lack of data and computational power, creating precise Machine Learning/ Deep Learning models was impossible. Nowadays, a considerable amount of data is generated at a reasonable velocity.

Therefore, we require frameworks like Hadoop and Spark to handle Big Data. Nowadays, most organizations are using Big Data analytics to gain hidden business insights. It is, therefore, a must-have skill for Data Scientists and Machine Learning Engineers.

Industry Knowledge: 

The most successful Projects out there are going to be those that address actual pain points. Whichever industry you’re working for. You should know how that industry works and what will be beneficial for the business. Suppose a Machine Learning Engineer or a Data Scientist does not have business acumen and the know-how of the elements that make up a successful business model. In that case, all those technical skills cannot be channeled productively.

You won’t discern the problems and potential challenges that need solving to sustain and grow. You won’t be able to help your organization explore new business opportunities.

Comparison Between Machine Learning and Data Science

The below table describes the fundamental differences between ML and DS:

Data ScienceMachine Learning
It deals with understanding and finding hidden patterns or valuable insights from the data, which helps to make smarter business decisions.It is a subfield of data science that automatically enables the machine to learn from past data and experiences.
It is used for discovering insights from the data.It is used for making predictions and classifying the result for new data points.
It is a broad term that includes various steps to create a given problem and deploy the model.It is used in the data modeling step of data science as a complete process.
A data scientist needs to have skills to use big data tools like Hadoop, Hive, and Pig, statistics, programming in Python, R, or Scala.Machine Learning Engineer needs to have computer science fundamentals, programming skills in Python or R, statistics and probability concepts, etc.
It can work with raw, structured, and unstructured data.It mainly requires structured data to work on.
Data scientists spent lots of time handling the data, cleansing the data, and understanding its patterns.ML engineers spend a lot of time managing the complexities that occur during the implementation of algorithms and the mathematical concepts behind them.
Comparison Between Machine Learning and Data Science

FAQ – Machine Learning and Data Science

Is data science related to machine learning?

Because data science is a broad term for multiple disciplines, machine learning fits within data science.

What are AI ML and data science?

Data science involves analysis, visualization, and prediction; it uses different statistical techniques. AI uses logic and decision trees; it makes use of models that make machines act like humans.

Should I learn machine learning or data science?

Data science is a broad term. As a result, actual data scientists must possess a broad skillset, including programming, math/statistics, and domain knowledge of the desired field of application.

What is data science vs. machine learning?

Data Science is a field about processes and systems to extract data from structured and semi-structured data. Machine Learning is a field of study that gives computers the capability to learn without being explicitly programmed.

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