The Wayback Machine - https://web.archive.org/web/20250210125852/https://www.geeksforgeeks.org/data-science-with-python-tutorial/
Open In App

Learn Data Science Tutorial With Python

Last Updated : 10 Jan, 2025
Summarize
Comments
Improve
Suggest changes
Like Article
Like
Share
Report
News Follow

Data science enables organizations to make informed decisions, solve problems, and understand human behavior. As the volume of data grows, so does the demand for skilled data scientists. The most common languages used for data science are Python and R, with Python being particularly popular as:

  • Easy to Learn: Python’s readable syntax makes it accessible to beginners.
  • Rich Library Ecosystem: Python provides extensive libraries such as Pandas and NumPy, essential for data analysis and machine learning.
  • Strong Community Support: Python boasts a large and active community, offering ongoing support and learning opportunities.

The Data Science with Python tutorial will guide you through the fundamentals of both data science and Python programming.

Before starting the tutorial, you can refer to these articles:

Python Libraries for Data Science

Data Loading

Data Preprocessing Using Python

Data Analysis

Related Courses: Machine Learning is an essential skill for any aspiring data analyst and data scientist, and also for those who wish to transform a massive amount of raw data into trends and predictions. Learn this skill today with Machine Learning Foundation – Self Paced Course , designed and curated by industry experts having years of expertise in ML and industry-based projects.

Data Visualization

Importance of Data Visualization in Data Science

Data Visualization using Matplotlib

Data Visualization using Seaborn

Machine Learning

Supervised Learning

1. Regression

2. Classification Algorithms

Unsupervised Learning

Python Data Science Tutorial

Applications of Data Science

Data science is used in every domain.

  • Healthcare : Healthcare industries uses the data science to make instruments to detect and cure disease.
  • Image Recognition : The popular application is identifying pattern in images and finds objects in image.
  • Internet Search : To show best results for our searched query search engine use data science algorithms. Google deals with more than 20 petabytes of data per day. The reason google is a successful engine because it uses data science.
  • Advertising : Data science algorithms are used in digital marketing which includes banners on various websites, billboard, posts etc. those marketing are done by data science. Data science helps to find correct user to show a particular banner or advertisement.
  • Logistics : Logistics companies ensure faster delivery of your order so, these companies use the data science to find best route to deliver the order.

Career Opportunities in Data Science

  • Data Scientist : The data scientist develops model like econometric and statistical for various problems like projection, classification, clustering, pattern analysis.
  • Data Architect : The Data Scientist performs a important role in the improving of innovative strategies to understand the business’s consumer trends and management as well as ways to solve business problems, for instance, the optimization of product fulfilment and entire profit.
  • Data Analytics : The data scientist supports the construction of the base of futuristic and various planned and continuing data analytics projects.
  • Machine Learning Engineer : They built data funnels and deliver solutions for complex software.
  • Data Engineer : Data engineers process the real-time gathered data or stored data and create and maintain data pipelines that create interconnected ecosystem within an company.

FAQs on Data Science Tutorial

What is data science?

Data science is an interconnected field that involves the use of statistical and computational methods to extract insightful information and knowledge from data. Data Science is simply the application of specific principles and analytic techniques to extract information from data used in planning, strategic , decision making, etc.

What’s the difference between Data Science and Data Analytics ?

Data Science Data Analytics
Data Science is used in asking problems, modelling algorithms, building statistical models. Data Analytics use data to extract meaningful insights and solves problem.
Machine Learning, Java, Hadoop Python, software development etc., are the tools of Data Science. Data analytics tools include data modelling, data mining, database management and data analysis.
Data Science discovers new Questions. Use the existing information to reveal the actionable data.
This domain uses algorithms and models to extract knowledge from unstructured data. Check data from the given information using a specialised system.

Is Python necessary for Data Science ?

Python is easy to learn and most worldwide used programming language. Simplicity and versatility is the key feature of Python. There is R programming is also present for data science but due to simplicity and versatility of python, recommended language is python for Data Science.

GeeksforGeeks Courses

Machine Learning Foundation

Machines are learning, so why do you wish to get left behind? Strengthen your ML and AI foundations today and become future ready. This self-paced course will help you learn advanced concepts like- Regression, Classification, Data Dimensionality and much more. Also included- Projects that will help you get hands-on experience. So wait no more, and strengthen your Machine Learning Foundations.

Complete Data Science Program

Every organisation now relies on data before making any important decisions regarding their future. So, it is safe to say that Data is really the king now. So why do you want to get left behind? This LIVE course will introduce the learner to advanced concepts like: Linear Regression, Naive Bayes & KNN, Numpy, Pandas, Matlab & much more. You will also get to work on real-life projects through the course. So wait no more, Become a Data Science Expert now.


Get IBM Certification and a 90% fee refund on completing 90% course in 90 days! Take the Three 90 Challenge today.

Master Machine Learning, Data Science & AI with this complete program and also get a 90% refund. What more motivation do you need? Start the challenge right away!


Next Article
Data Analysis (Analytics) Tutorial
author
abhishek1
News
Improve
Article Tags :

Similar Reads

Is it Mandatory to Learn R Programming Language for Becoming a Master in Data Science and Big Data?
It is not mandatory to learn R programming to become a master in data science and big data, but it is highly beneficial. R is one of the leading languages for statistical analysis and has powerful tools for data visualization, which are crucial skills in data science. However, proficiency in other languages like Python can also provide a comprehens
3 min read
Data Science Degrees vs. Data Science Certificates
The demand for skilled data scientists is rapidly growing in today's data-driven society. This pressure leads many aspiring professionals to seek academic programs, such as degrees and certificates in data science. Both options are valuable for gaining the necessary skills and knowledge, but they differ significantly in terms of time commitment, co
9 min read
5 Best Books to Learn Data Science in 2020
Data Science is one of the in-demand technologies of 2020 and if we wish to learn and make a career out of it, then there is no great time than now. We are familiar with big data and how difficult is it to analyze and maintain the collected unstructured data. So every company will require data scientists to attain the maximum productivity out of th
5 min read
How to Learn Data Science in 10 weeks?
The magic of “Data Science” has exploded in the entire market and has become a major wagon for all scales of businesses. Today, the decisions companies are making along with the forecast are solely dependent on data science. The field of data science has grown more than 3x folds, especially during the recent pandemic, as everyone was forced to work
8 min read
Top 10 Data Science Skills to Learn in 2024
Do you know what is a "Unicorn Employee"? Well, in today’s times, that is someone who is multi-talented, works hard, and is ready to go the extra mile. While it is quite difficult to become a unicorn employee, you can become one in Data Science by understanding and learning at least the basics of all the important Data Science skills. Data Science
9 min read
Is Data Science Hard to Learn?
In today's era, the data is increasing day by day and the business analyze the data and apply machine learning and deep learning techniques to improve the growth of their business. Data Science involves collecting the data, analyzing the data, transforming the data, and extracting the information and insights from the data by applying machine learn
9 min read
Why Learn Data Science?
As we know nowadays data has become the lifeblood of virtually every industry. From healthcare to Finance, from marketing to entertainment, data drives decision-making, innovation, and progress. As the volume and complexity of data continue to grow exponentially, the demand for skilled professionals who can harness its power has never been higher.
6 min read
Data Science Vs Computer Science Salary: Key Difference
In India's rapidly evolving technological landscape, both Data Science and Computer Science have emerged as pivotal fields driving innovation and economic growth. As the country continues its digital transformation, professionals in these domains are increasingly in demand across various sectors. For many aspiring tech enthusiasts and students in I
5 min read
DIKW Pyramid | Data, Information, Knowledge and Wisdom | Data Science and Big Data Analytics
The term DIKW is derived from the field of "data science and big data analytics". The DIKW model is used for data enrichment. The DIKW model consists of four stages. The full form of every alphabet in the word DIKW has its own meaning. In DIKW, D stands for "Data", I stands for "Information", K stands for "Knowledge" and W stands for "Wisdom". The
2 min read
Difference Between Data Science and Data Visualization
Data Science: Data science is study of data. It involves developing methods of recording, storing, and analyzing data to extract useful information. The goal of data science is to gain knowledge from any type of data both structured and unstructured. Data science is a term for set of fields that are focused on mining big data sets and discovering t
2 min read
Data Science: Unleashing the Power of Data For Students and Professionals
The capacity to organize and make sense of massive volumes of data has grown in value in today's data-driven society. Data science provides a plethora of information and possibilities, whether you're a student studying for a future career or a seasoned professional trying to stay competitive. This article examines the convincing arguments for why d
3 min read
Ethics in Data Science and Proper Privacy and Usage of Data
As we know, these days Data Science has become more popular and it is one of the emerging technologies. According to the latest estimation 328.77 million terabytes are generated every day so just think of how large the volume is. , this data may also consist of your data such as your Identity cards or your Banking information or it may be any other
8 min read
Why is Data Visualization so Important in Data Science
Would you prefer to view large data tables and then make sense of that data or view a data visualization that represents that data in an easy-to-understand visual format? Well, most of you would prefer data visualization! That is because data visualization is extremely useful in understanding the data and obtaining useful insights. It can allow you
8 min read
Role of Data Science in Big Data Analytics
In today's data-driven world, the role of data science in big data analytics is becoming increasingly vital. With the vast amounts of data being generated every day, organizations are turning to data science to make sense of it all and extract valuable insights. Data science involves collecting, analyzing, and interpreting large volumes of data to
8 min read
Roles of Data Engineering and Data Science in Modern Analytics
In the rapidly evolving landscape of data analytics, two key players stand out: data engineering and data science. While distinct in their focus and responsibilities, these fields are deeply interconnected, forming the backbone of modern data-driven decision-making. In this article, we'll delve into the intricate relationship between data engineeri
5 min read
Difference Between Big Data and Data Science
The terms "Big Data" and "Data Science" often emerge as pivotal concepts driving innovation and decision-making. Despite their frequent interchangeability in casual conversation, Big Data and Data Science represent distinct but interrelated fields. Understanding their differences, applications, and how they complement each other is crucial for busi
4 min read
Data Science vs Data Analytics
In this article, we will discuss the differences between the two most demanded fields in Artificial intelligence that is data science, and data analytics. What is Data Science Data Science is a field that deals with extracting meaningful information and insights by applying various algorithms preprocessing and scientific methods on structured and u
3 min read
Handling Large data in Data Science
Large data workflows refer to the process of working with and analyzing large datasets using the Pandas library in Python. Pandas is a popular library commonly used for data analysis and modification. However, when dealing with large datasets, standard Pandas procedures can become resource-intensive and inefficient. In this guide, we'll explore str
5 min read
Data Science Blogathon 2024 - From Data to Intelligence
Attention, data science enthusiasts, AI aspirants, and machine learning masterminds! Are you passionate about delving into the fascinating world of data science? Do you desire to showcase your expertise and contribute to a vibrant community? Then the GeeksforGeeks Data Science Blogathon is your perfect platform to shine! Final Result Live - Click H
9 min read
Data Science Bootcamp Online | Become a Data Scientist [2025]
Want to get into data science or start a career in the data science field? An online Data Science Bootcamp is a great way to start. With flexible learning and practical experience, you’ll gain the skills needed to become a successful data scientist. Whether you're a beginner or want to improve your Data Science skills, an online Data Science bootca
7 min read
Probability Data Distributions in Data Science
To understand how variables relate to each other begins with looking at covariance and correlation. Once we see how they are connected the next step is to explore probability distribution as it helps to describe how data is spread out and reveal hidden patterns and provide better predictions. Let's suppose if you roll a die then probability of roll
14 min read
K-Means clustering on the handwritten digits data using Scikit Learn in Python
K - means clustering is an unsupervised algorithm that is used in customer segmentation applications. In this algorithm, we try to form clusters within our datasets that are closely related to each other in a high-dimensional space. In this article, we will see how to use the k means algorithm to identify the clusters of the digits. Load the Datase
5 min read
How to Normalize Data Using scikit-learn in Python
Data normalization is a crucial preprocessing step in machine learning. It ensures that features contribute equally to the model by scaling them to a common range. This process helps in improving the convergence of gradient-based optimization algorithms and makes the model training process more efficient. In this article, we'll explore how to norma
4 min read
Data Normalization with Python Scikit-Learn
Data normalization is a crucial step in machine learning and data science. It involves transforming features to similar scales to improve the performance and stability of machine learning models. Python's Scikit-Learn library provides several techniques for data normalization, which are essential for ensuring that models are not biased towards feat
7 min read
Using KNNImputer in Scikit-Learn to Handle Missing Data in Python
KNNimputer is a scikit-learn class used to fill out or predict the missing values in a dataset. It is a more useful method that works on the basic approach of the KNN algorithm rather than the naive approach of filling all the values with the mean or the median. In this approach, we specify a distance from the missing values which is also known as
4 min read
Pandas Cheat Sheet for Data Science in Python
Pandas is a powerful and versatile library that allows you to work with data in Python. It offers a range of features and functions that make data analysis fast, easy, and efficient. Whether you are a data scientist, analyst, or engineer, Pandas can help you handle large datasets, perform complex operations, and visualize your results. This Pandas
15+ min read
Medical Analysis Using Python: Revolutionizing Healthcare with Data Science
In recent years, the intersection of healthcare and technology has given rise to groundbreaking advancements in medical analysis. Imagine a doctor faced with lots of patient information and records, searching for clues to diagnose complex disease? Analysing this data is like putting together a medical puzzle, and it's important for doctors to see t
9 min read
Setting Up a Data Science Environment in Python
Data Science is the field of working with data using computational and statistical methods, which is becoming more relevant than ever before as more people are coming online and companies are generating terabytes of data based on their behavior and platform usage. Setting up a data science environment is a crucial step for anyone looking to dive in
5 min read
5 Python Projects for Data Science Portfolio
Building a portfolio with well-thought-out projects is crucial for anyone aspiring to enter the field of data science. It not only demonstrates your technical skills but also shows your ability to handle real-world data problems. In this article, we will explore 5 Python Projects for a Data Science Portfolio. Table of Content Exploratory Data Analy
5 min read
Is R Programming or Python better for Agriculture Data Science?
Answer: Both R and Python have their strengths in agriculture data science, but Python is generally better suited due to its versatility, extensive libraries for data analysis, machine learning, and ease of integration with big data tools. However, R is still valuable for specialized statistical analysis and data visualization tasks, making it a st
3 min read
We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy
Lightbox