Machine Learning vs Data Science - Basics and Differences

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Data science and machine learning are parts of technology that use data to improve products, services, and systems. These fields are popular and can lead to well-paying jobs in the future. Data scientists ranked as the third-best job in technology, and machine learning engineers had the top job in 2019. Learning programming and statistics can help you succeed in these careers. The following article will discuss machine learning vs data science in depth. Read the guide to know more about ML and DS as well as compare data science and machine learning.

What are Data Science and Machine Learning?

First, we will discuss all the concepts regarding data science. After that, we will elaborate on the machine learning

Understanding Data Science

Data science involves analyzing large amounts of data a company or organization possesses. However, it includes figuring out where the data came from, what it's about, and how it can help the company grow. Plus, the data in a company can be structured (organized) or unstructured (not organized). When we study this data, we can learn important things about how the business works or what's happening in the market. As a result, it helps the company do better than its competitors because it understands what's going on and can make savvy decisions.
 

Data scientists are experts at turning raw data into practical information for businesses. In addition, they know how to write code, use data mining and machine learning tools, and understand statistics. Many big companies like Amazon and Netflix, and even sectors like healthcare and fraud detection, rely on data science to improve their work and make better decisions.

Skills Required For Data Scientist

If you aspire to become a successful data scientist, it's crucial to acquire proficiency in programming and data analytics skills. In short, the following section will elaborate on the skills. Further, we will discuss the difference between data science and machine learning in depth.
 

• Know programming languages like Python, R, SAS, and others well.
• Get used to working with lots of organized and unorganized data.
• Be comfortable with processing and studying data for business purposes.
• Understand math, statistics, and probability concepts.
• Learn how to visualize data and manage it effectively.
• Know about machine learning algorithms and models.
• Have good communication and teamwork abilities.

Careers in Data Science

Here, we will discuss a list of careers in data science. Afterward, we will describe the essential concept of machine learning vs data science.
 

• Data Scientist: Uses data to understand things and help companies make better choices.
• Data Analyst: They clean and study data to solve business problems.
• Business Intelligence Analyst: Analyzes sales and customer data to help business teams understand and make decisions.
• Data Engineer: Creates systems to collect and organize data for analysts and scientists.
• Data Architect: Plans and manages databases for storing and handling data.

Salary of Data Scientist

In India, Data Scientists earn an average salary of ₹13,50,000 per year. On top of this, they often receive additional cash compensation averaging ₹1,50,000, ranging from ₹70,000 to ₹3,00,000. These Data Scientist salary estimates are based on information from 12,602 Data Scientist employees who shared their salaries anonymously. Here, we mentioned all the concepts of data science. Now, we will discuss the machine learning.

Understanding Machine Learning

Machine learning teaches computers to think and decide on their own without being told every single step. In addition, it's a part of artificial intelligence where computers learn from data, figure out patterns, and then use that knowledge to predict things or do tasks.
 

This technology is behind things like Netflix suggesting what to watch next or Amazon recommending products you like. Plus, it's also used in finance to spot fraud and in businesses to predict when customers might stop using their services. Understanding how machine learning works and what it can do can help you find ways to use it in your work or business to make things easier and savvier.

Machine Learning Engineer Skills

Here, we will discuss some necessary skills needed to become a machine learning engineer.
 

• Be good at computer science, like knowing about data structures and how computers work.
• Understand statistics and probability.
• Know about software engineering and designing systems.
• Learn programming languages like Python and R.
• Be able to analyze and model data.

Career in Machine Learning

Now, we will discuss the list of some careers in machine learning. Further, we will elaborate on Machine Learning vs data science to clear all your doubts.
 

• Machine Learning Engineer: Works on building and improving AI systems that learn from data.
• AI Engineer: Sets up the infrastructure for AI and makes it work.
• Cloud Engineer: Manages and builds cloud systems.
• Computational Linguists: They Design computers to understand human language better.

Machine Learning Engineer Salary

Machine Learning Engineers in India typically earn an average salary of ₹11,58,000 yearly. On top of this, they often receive additional cash compensation averaging ₹1,50,000, ranging from ₹95,000 to ₹2,50,000. These salary estimates are based on information from 1,989 Machine Learning Engineer employees who shared their salaries anonymously.

Machine Learning vs Data Science Comparison

Here, you will see the comparison of data science and machine learning in a tabular form.

 

Data Science or Machine Learning Which is Better?

Data science is ideal for individuals who are fascinated by data and enjoy uncovering meaningful insights from it. Moreover, they delve deep into datasets, analyze patterns, and extract valuable information that can guide decision-making and strategic planning. On the other hand, machine learning appeals to those who are passionate about creating and refining models that can learn from data and enhance their performance over time. In addition, these individuals thrive on developing algorithms, training models, and fine-tuning them to make accurate predictions or decisions based on the data they receive.

Which is More in Demand Data Science or Machine Learning?

• Data science and model development are still necessary. However, there's a growing need to create reliable infrastructure for delivering data science models to many customers.
• Machine learning engineers are now in higher demand than data scientists in the tech industry.
• Businesses have realized that having advanced machine learning models is good. However, deploying them effectively to serve customers is crucial for commercial success.
• Companies are increasingly becoming data-driven and forming data science and machine learning teams to ensure they get a good return on investment (ROI).
• Big tech companies that invested early in AI are now focusing on improving production capabilities and making money from the research done by scientists.
• While data scientists with advanced degrees are valuable, there's a shortage of skilled machine learning engineers. As a result, it makes them highly sought after in the job market.

Conclusion

In short, right now, the tech industry wants skilled machine learning engineers more than data scientists. However, they're focusing on ensuring the technology they build works well and helps their customers. As a result, it shows that the industry is serious about getting real value from AI and machine learning, which means being able to use these technologies effectively. In short, we’ve gone through machine learning vs data science in depth. You can check it out.