What Is Data Science? Courses, Basics, Frameworks & Careers

The study of data science enables us to analyze data in order to address practical issues. It combines machine learning, statistics, and programming abilities to obtain information and facilitate wise choices. Data science is now one of the most popular and lucrative career paths due to the increasing need for data-driven solutions.

In this guide, you will discover what is data science and why it is important. We'll look at the best data science courses, their duration, the types of jobs they lead to, and their fee structures. 

Ready to start your data science journey? Join our best online Data Science Courses and learn from top universities, gain industry skills, and earn a globally recognized certification.

You can also explore a step-by-step guide on becoming a data scientist to strengthen your understanding and career plan.

What is Data Science?

The process of analyzing data to derive results, resolve issues, and help in decision-making is known as data science. To transform unprocessed data into useful information, it combines computer science, statistics, and business expertise.

Data science is important in various industries, including e-commerce, healthcare, and finance. Data science helps businesses make better decisions, from identifying fraud in banking to making product recommendations on online platforms.

Ready to build a future-proof career in data science? To help you succeed, we provide industry-relevant, expert-led courses with practical projects and career guidance. Explore our top programs below:

• Business Analytics Certification Program with PwC India
• Job-Linked Advanced Data Science Bootcamp
• Generative AI Mastery Certificate for Data Analysis

What Makes Data Science Important?

• Enhances Business Decisions: Encourages more informed, data-driven decisions.
• Improves Customer Experience: Customizes services by analyzing behavior.
• Boosts Efficiency: Enhances workflows and automates monotonous tasks.
• Fuels Innovation: Provides energy for new goods, equipment, and services.
• Provides Competitive Edge: By providing superior insights, it enables companies to surpass rivals.

Not sure how challenging data science might be? Explore How Hard Is It to Learn Data Science? and find out what to expect before you begin.

Skills Required for Data Science:

• Programming: Python, R, and SQL are necessary for data science programming.
• Data Analysis: NumPy, Excel, and Pandas
• Machine Learning: TensorFlow and Scikit-learn
• Data Visualization: Data visualization using Tableau, Power BI, and Matplotlib
• Mathematics & Statistics: Probability and linear algebra in mathematics and statistics
• Problem-Solving: The capacity to use information to address practical issues
• Communication: Communicating data insights to stakeholders who are not technical 

Want to understand how data science really works? Explore the Data Science Life Cycle to learn each step clearly.

What are the Top Data Science Courses in 2025?

If you’re wondering what is data science, it’s the field of studying large volumes of data to extract insights and support decision-making. Students who take a data science course learn how to work with data using tools and methods like statistics, machine learning, and Python.

These courses assist you in laying the proper basis, regardless of your interest in data science or artificial intelligence. These programs are open to students with a background in maths, technology, or even non-tech.

Selecting the appropriate data science course can lead to top data science positions in cutting-edge industries, regardless of your career path.

Here are some of the best courses in 2025, along with their duration and data science course fees:

Note: The fees mentioned in the table are approximate and may vary over time and University

What Are The Top Data Science Frameworks?

There are many data science frameworks available, but it is important to choose the right one that suits your needs. Like Python frameworks for data science, R frameworks for data science, etc. To help you decide, we have compiled a list of the top 10 data science frameworks that are currently in use.

Keras:

Keras is one of the most popular and widely used deep learning frameworks. It enables developers to rapidly create powerful neural networks that can be used for a variety of tasks, such as image recognition, natural language processing, and more. Keras is written in Python and can be integrated with other libraries like Theano, TensorFlow, and CNTK.

TensorFlow:

TensorFlow is an open-source machine-learning library developed by Google. It can be used to build powerful neural networks for a wide array of tasks, from image recognition and natural language processing to predictive analytics. TensorFlow also provides access to advanced AI tools like Google’s DeepMind and TensorFlow Serving. Besides this, Like python frameworks for data science, TensorFlow is also an ideal choice for deep learning and AI development tasks.

Pandas:

Pandas is one of the most popular data science frameworks. It provides convenient tools for data wrangling, analysis, and visualization. Pandas is written in Python and is integrated with other libraries such as NumPy, Matplotlib, Scikit-Learn, and Statsmodels. Mostly, the data science tools and frameworks are based on the Python language, and Pandas is one of them.

Scikit-Learn:

Scikit-Learn is a powerful Python library that enables developers to create and deploy machine-learning models with ease. It is built on top of NumPy, SciPy, and Matplotlib and provides access to a variety of pre-built algorithms for tasks such as predictive analytics, clustering, classification, and more. These data science tools and frameworks are widely used in various data science projects.

Numpy:

Numpy is an open-source library that enhances the computational power of Python with robust data structures designed for number-crunching applications such as Quantum Computing, Statistical computing, signal processing, image processing, graphs and networks, astronomy processes, cognitive psychology, and more, using the high-performance capabilities of C.

Spark MLib:

Spark MLib is a framework that supports Java, Scala, Python, and R. It can be used on Hadoop, Apache Mesos, Kubernetes, and cloud services to handle various data sources. For example, it can be used to build streaming applications that process data in real-time. And it supports distributed machine-learning algorithms like decision trees, random forests, and more.

Theano:

Theano is a Python library that enables developers to build complex deep-learning models with ease. It is written in Python and based on NumPy and SciPy. Theano also provides access to GPU acceleration for faster model training.

MapReduce:

MapReduce in data science is an open-source framework used for data processing across large clusters of computers. It is built on the Hadoop Distributed File System (HDFS), which enables applications to store files in a distributed manner across multiple machines. MapReduce in data science can be used for tasks such as data aggregation, sorting, filtering, data mining, and machine learning. It divides the dataset into smaller chunks and distributes them across multiple computers, allowing them to process the data in parallel.

Why Is Data Science Important in Today's World?

Large databases of structured and unstructured data must be mined using data science techniques to find hidden patterns and derive useful insights. Data science is crucial because of the numerous applications it may be used for, ranging from simple activities, such as asking Siri or Alexa for recommendations, to more sophisticated ones, such as operating a self-driving automobile. Let's check out the importance of data science in society. 

Importance of Data Science in Business

Data science's goal is to assist organizations in comprehending the patterns of variance in data, including client information, business growth rates, data volume, or any measurable quantity. In data science, you use statistical and probabilistic models to analyze changes and improvements in historical or current data. Data science has transformed operations for businesses all over the world.   

The following are some of the main benefits of employing data science in business:  

• Giving management and officials the ability to foster new ideas: Data scientists are key to finding better solutions since they can use machine learning to identify complex business issues like operations research challenges. Data scientists are responsible for reporting on industry trends, internal resource expenditures, and profit estimates, reducing workflow bottlenecks, and improving the effectiveness of the business architecture with well-informed goals.  
• An improved user experience: Even while it might not be the first thing that comes to mind, it will ultimately influence everything else, including revenues and profits. Improved client satisfaction leads to increased sales. Even if your product or service is excellent, you'll lose out on chances if you don't consistently improve it.  

Importance of Data Science in Healthcare

The healthcare sector produces enormous datasets of valuable data on patient demographics, treatment plans, outcomes of medical exams, insurance, etc. The vast amounts of dispersed, structured, and unstructured data generated by healthcare systems can be processed, analyzed, assimilated, and managed with the help of data science. This data needs to be managed and analyzed effectively to obtain true findings. 

Big data analytics and data science can help with strategic decision-making for the healthcare system by offering useful insights. It aids in creating a complete picture of clients, patients, and professionals. Making decisions based on data opens up new opportunities to improve healthcare quality. The importance of data science in research proves to be beneficial for medical improvements.  

Data Science Applications in Real Life 

Data science has recently displaced almost every industry on Earth. Right now, data is essential to every sector in the world. We have included some examples of uses of data science and its applications in real life. 

Real-Life Examples of Data Science Applications

1. Medical Image Analysis 

Procedures like detecting neoplasia, artery stiffness, and organ delineation use methods and frameworks like MapReduce to find the best parameters for tasks like lung texture categorization. It employs machine learning methods for solid texture classification, including wavelet analysis, content-based medical image indexing, and support vector machines (SVMs).  

2. Fraud and Risk Detection  

Banking organizations have mastered the art of data science and control through the use of consumer profiling, historical purchases, and other crucial factors to calculate the likelihood of risk and default.  

3. Airline Route Planning 

The airline business has a reputation for overcoming challenges. A few airline service providers are trying to keep their working conditions and occupancy rates high. Airlines may utilize data science to establish marketing strategies like a customer loyalty program, anticipate flight delays, decide which aircraft to acquire, plan routes and layovers, and make strategic changes.  

4. Augmented Reality 

The data science application that appears to have the most promise for the future is augmented reality. Data science and virtual reality can interact because a VR headset uses computer expertise, algorithms, and data to provide the best viewing experience possible. The ability to navigate obstacles while viewing Pokemon on buildings, streets, and other fictitious objects. The locations of the Pokemon and gyms in this game were chosen using data from Ingress, the previous app from the same business. 

Top Career Paths in Data Science

With industries like tech, healthcare, finance, and e-commerce actively hiring qualified data science professionals, data science has become one of the fastest-growing fields in the country. 

The need for professionals who can analyze, model, and derive insights from data is increasing rapidly due to the rise of Artificial Intelligence and data science. Professionals with extensive expertise in Python, SQL, machine learning, data visualization, and cloud platforms are in high demand. 

Here are the top 10 career opportunities in data science, along with their average annual salaries in India:

Sourced By: Ambition Box

Note: The average annual salaries mentioned in the table are approximate and may vary depending on the employer, experience, and location.

Also Read: How to become a Data Scientist

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