Data Science vs. Machine Learning: Differences and Challenges

Indeed Editorial Team

Updated 9 July 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data science and machine learning are complex technologies used to analyse data and help improve decision-making processes. Due to its use in data, it may be hard to distinguish between its application. Learning the differences between data science and machine learning may help you make an informed choice to pursue a career in this field. In this article, we discuss the definition of data science and machine learning, their advantages, differences, skills and various career opportunities in this field.

Data science vs. machine learning

It can be easier to understand the key differences between data science vs. machine learning if you review their definitions. Data science involves the study of data and deriving methods to extract insights from it. Machine learning focuses on building models that can learn using different tools and techniques without human intervention. Since data science and machine learning involve using data, it may seem hard to distinguish between the two. Here are some key differences between data science and machine learning:

Technological goal

Data science tests various data sources to prove or disprove a hypothesis. It derives insights that aid business decisions. Data science uses scientific methods and algorithms to achieve this. Machine learning develops an algorithm that learns to read and extract meaning from data. It requires data feeding to improve accuracy. Machine learning helps make predictions based on past data using statistics, probability and mathematical models.


Data science uses machine learning tools to work with structured and unstructured data. Its process involves data acquisition, validation, evaluation and cleaning. Machine learning uses algorithms and analytics models to analyse data. It adapts supervised, unsupervised or reinforcement learning methods to produce an outcome for a specified task.


All data used in data science is in the human-readable form to perform analysis. Data science may also work with manual methods. Machine learning requires data to transform into a language that the algorithm understands. Manual implementation of data in machine learning is difficult.

What is data science?

Data science is a complex examination of large amounts of data to facilitate business decisions. It uses mathematical and statistical modelling to study data present in the company archives. Scientists receive structured and unstructured data, check its quality and relevancy to the study. They understand the origin of the data, study it and derive insights that may benefit the company. Identifying business and market patterns through this study helps businesses make informed decisions and build a strategic advantage over their competition.

Data scientists transform raw data into business insights using data processing, validation, artificial intelligence and statistics.

Skills required in data science

Here are some of the fundamental skills necessary in the field of data science:

  • programming knowledge of Python, R, SAS

  • SQL database coding

  • data mining, data validation, data cleaning and data visualisation skill

  • using BigData tools such as Apache Hadoop and Apache Spark

  • ability to manage structured and unstructured data

  • knowledge of data evaluation tools and techniques

  • ability to use artificial intelligence, machine learning and deep learning models

  • ability to automate data processing and data computation

  • ability to use data visualisation tools such as Tableau and Microsoft Power BI

Related: What Is Big Data Hadoop? (Definition And Career Opportunities)

Challenges of data science

Here are some important challenges in using data science:

  • requires immense data for accurate results

  • requires significant financial support to implement data science technologies

  • the business decision-making process does not use it efficiently

  • lack of talent with domain expertise

  • issues with data privacy

Applications of data science

Search engines use data science technology to provide quick results. Digital marketing thrives on data science technology. Data science technologies help understand user behaviour and provide personalised music, movies, products and brands recommendations. Newer technologies like image and speech recognition require data science to identify faces, places, voices and images. Data science also helps perform sentiment analysis to gauge customer brand loyalty.

What is machine learning?

Machine learning is the field of study that enables computers to learn through data without explicit coding. Algorithms for machine learning use neural networks that stimulate the functioning of a living human brain. These networks analyse large data sets and extract patterns from them. Different networks help solve various tasks. These algorithms process data and deliver future predictions. The greater the amount of data, the greater is the accuracy of machine learning systems.

Machine learning combines data with statistical tools and modelling to deliver results. It uses various learning methods like supervised, unsupervised and reinforcement. The insights derived through them help businesses make informed decisions. Any machine learning model aims to be reproducible to evaluate more than one dataset.

Skills required in machine learning

Here are some of the fundamental skills necessary in the field of machine learning:

  • programming knowledge of Python and R

  • understand statistics and probability

  • understand data modelling and data evaluation

  • understand application algorithms

  • understand data architecture design

  • text representation techniques

  • design machine learning tools

  • understand deep learning technology

Challenges with machine learning

Here are some of the fundamental challenges with machine learning technologies:

  • require vast and diverse data to study and provide results

  • machine-learning algorithms meet a specific objective

  • supervised learning process

  • each narrow application requires special training

  • machine-learning algorithms do not learn incrementally or interactively in real-time

Applications of machine learning

Machine learning thrives in several industries, including everything from healthcare to gaming. The healthcare industry uses machine learning technology to access patient health, identify patterns and develop better preventive care. Similarly, the gaming sector uses machine learning to create newer gaming levels without human intervention or code. The financial industry benefits from machine learning by identifying spending patterns and notifying in case of any suspicious behaviour to prevent fraudulent transactions.

Career options in data science and machine learning

Here are some career opportunities in data science and machine learning:

1. Data Analyst

National average salary: ₹20,775 per month

Primary duties: Data analysts work on raw data to derive business insights that help organisations make strategic decisions. They understand code and use their knowledge to identify bugs and fix errors. Additionally, data analysts also automated tools to ease their job.

Related: How Much Do Data Analysts Make? (Plus How To Become One)

2. Machine learning engineer

National average salary: ₹21,388 per month

Primary duties: Machine learning engineers build systems to run tests and experiments. They use statistics, programming and software engineering to create data funnels. These systems help deliver software solutions, check functionality and monitor performance as per the business requirement.

3. Data scientist

National average salary: ₹8,80,272 per year

Primary duties: Data scientists use mathematics, statistics and computer science to help businesses solve complex issues. They study vast volumes of company data, identify patterns and present them using visuals. Organisations use these insights to improve their product, service or customer experience.

Related: What Does A Data Scientist Do? And How To Become One

4. Hadoop developer

National average salary: ₹8,86,780 per year

Primary duties: Hadoop plays a vital role in data science. Hadoop developers understand organisational goals and develop Hadoop applications for data processing and managing data. They test the code, identify bugs and rectify it to meet the business goal.

Related: Top 20 Big Data Tools: Big Data And Types Of Big Data Jobs

5. Data engineer

National average salary: ₹10,57,213 per year

Primary duties: Data engineers are professionals in database design and coding. They understand business objectives and build algorithms to process data and produce the desired results. Data engineers build systems that ease data access, processing and visualisation.

6. Software architect

National average salary: ₹12,30,132 per year

Primary duties: A software architect uses computer code to design and plan software features and applications. They create architectural blueprints for the technology team to execute. A software architect collaborates with teams to ensure the quality of the final output.

7. Data warehouse architect

National average salary: ₹14,78,257 per year

Primary duties: The role of a data warehouse architect requires them to design and manage databases to store and organise vast volumes of data. They study the current data infrastructure at a company, identify gaps and recommend solutions. Once approved, a data warehouse architect takes the required measures to integrate the plan into the current systems.

Salary figures reflect data listed on Indeed Salaries at time of writing. Salaries may vary depending on the hiring organisation and a candidate's experience, academic background and location.

Please note that none of the companies mentioned in this article are affiliated with Indeed.

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