Data Science and Machine Learning are both high in demand in the fields of data, big data, and technology. All organizations are including data scientists to improve their performances and the data scientists use both, data science and machine learning to use data in the growth of the organization. 

Data Science and Machine Learning are used to improve products, services, and systems using data. Both are highly paid careers. While Data Science and Machine Learning are interrelated, Machine Learning is an arm of Artificial Intelligence. Programming and statistics are studied and used in both fields. 

In this article, we will discuss a detailed comparison between Data Science and Machine Learning, focusing on skills, tools, courses, and career opportunities. 

Difference between Data Science and Machine learning

 The main difference between Data Science and Machine learning is that Data Science is a field of study, to study data and extract its meaning for business decisions. While Machine Learning is a method to develop a model based on algorithm which can help in improving performance of organizations. Machine learning is subset of Data Science. 

What is Data Science?

Data Science is a multidisciplinary field that studies data and takes out meaningful information to use in business decisions. In Data Science, data is collected, stored, processed, analyzed, and presented. It is given to stakeholders in a business, government agency, or other organizations to draw conclusions on how to improve their products or services. There are top domains that are benefiting from data science like healthcare, e-commerce, financial services, government sectors, etc. 

To understand what data science does and its various aspects, a basic knowledge of computer science, mathematics, and statistics is required. With a zest to work with big data and work on new ideas every day, you can go a long way in your career.  

Skills required in Data Science

• Knowledge of maths, computer science, and statistics. 
• Working with big data, structured, unstructured, and semi-structured data. 
• Understanding of programming languages like Python, R, Java, etc. 
• Communication skills, teamwork, and presentation skills. 
• Knowledge of Machine Learning and Deep learning. 
• Skills like data visualization, data wrangling, data analyzing, etc. 
• Understanding the business; its core, products or services, working system, motive, and future plans. 
• Familiarities with collecting, processing, and storing data.

Career in Data Science

Data Science is one of the top career choices with many positions to offer. It is best suited for science and business professionals. A few data science jobs are listed below:

Data Scientists

Data Scientists are involved in studying data as well as business for an organization to use the information in decision-making. Generally, they ask questions to be answered, find future trends, and write algorithms for the system. 

Data Analysts

Data Analysts analyze the data to find answers for businesses. A huge part of their job is to perform different analyses of data, like predictive analysis, to find patterns. 

Data Engineer

Data Engineers work on the initial levels of data science departments. Their job is to collect data from various sources and convert raw data into information for data scientists to use.

Data Architect

Data Architects work on the infrastructure of a business or organization, to manage and store data in the system.

Statistician

Statticians extract information from data clusters and develop new methods for engineers to apply in their process. 

Business Analysts

Business Analysts are directly involved in taking actions as per data insights and are responsible for business growth.

Data Science is a very large field that doesn’t stop here. To know more about data science and learn how to be a part of this world, watch this video:

What is Machine learning?

Machine Learning is a branch of Artificial Intelligence, which has vast use across different fields including data science. In data science, it extracts data and finds patterns using algorithms. It also develops different models to analyze data and predict future trends. For example, social media platforms use machine learning methods to understand customer behavior to suggest products, services, and other things available on social media to users. 

Skills required in Machine learning

• Understanding of computer science. 
• Working with data structure, data architecture, and algorithms. 
• Skilled in software engineering. 
• Skilled in system development. 
• Working knowledge of programming languages like Python, R, etc. 
• Knowledge of data modeling and data analysis. 
• Understanding the business; its infrastructure, system, and requirements.

Career in Machine learning.

 Machine Learning is useful in many disciplines, making it high in demand.  A few data science jobs are listed below:

Machine Learning Engineer

Machine Learning Engineers build and manage platforms for machine learning projects. 

NLP Scientists

Natural Language Processing Scientists work building and imitating human behavior on computers.

Human-Centered Machine Learning Designer

Human-Centered ML designers design, develop, and deploy systems that learn from humans.

BI Developers

Business Intelligence Developers analyze data sets and market trends. 

Computational Linguists

They teach computers to understand human language. 

Which is a better career option for you- Data Science or Machine Learning?

 Choosing a career between Data Science and Machine Learningis easy after reading this article. You can make a list of your skills, your educational qualification, and check job roles in both fields to decide which career path is best for you. As we know both careers are in demand and highly paid. For both fields, you require knowledge of programming and statistics, familiarity with Python and SQL, an understanding of different tasks with data, and building models using machine learning. 

Let us discuss Data Science vs Machine Learning with a comparison table

If you have machine learning skills and want to work in data science fields, you can look into Data Science Beginner Level , Intermediate Level , and Advanced Level courses and start your career in data science.

Conclusion

Data Science is a study of all types of data to extract information that helps organizations take business decisions. Machine Learning is building methods to use data for improving performance and informing future trends. Machine learning is a branch of AI but is also an integral part of data science. Both fields are popular among businesses and offer high-paying jobs. In Data Science vs Machine Learning you can choose the one based on your skills, qualifications, and career expectations. The commonality between these two is working with data, understanding business, and using machine learning models.

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