IBM Data Science

Find out what data science is, what data scientists do, and whether it's the right career for you. No experience required. Data science is one of the fastest-growing fields in the job market. The U.S. Bureau of Labor Statistics projects 34% employment growth for data scientists from 2024 to 2034, much faster than average, with a median annual wage of $112,590.¹ That works out to about 23,400 openings projected each year over the decade. The World Economic Forum's Future of Jobs Report 2025 also ranks big data specialists and AI and machine learning specialists among the fastest-growing jobs through 2030.² This course is where you get a clear picture of the field before you commit to a learning path. This IBM course gives you a practical foundation in how data science works and how organizations use it to make decisions. Across four modules, you'll learn what data science is, how big data and cloud computing power it, and how data scientists use machine learning, deep learning, and generative AI to find patterns and make predictions. You'll see how companies apply those methods to real problems, from improving efficiency to saving lives, and what it takes to get hired as a data scientist. You'll learn from people doing the work. Working data scientists share how they define the field, how they got started, and what they'd tell someone just beginning. You'll explore data in a hands-on IBM Cloud lab, follow a case study of one data scientist's journey, and complete a final project where you analyze a real data scientist job posting against what you've learned. By the end, you'll be able to explain what data science is, describe how data scientists work, and map out your next step with confidence. Whether you're considering a career change, exploring data science for the first time, or want to work more effectively with data teams in your current role, this course gives you the foundation to start. Skills you'll gain include: - Defining data science and explaining why it matters in a data-driven world - Describing how machine learning, deep learning, and generative AI fit into data science - Explaining how big data, data mining, and cloud computing support data-driven decision-making - Understanding how data is generated, stored, and organized, including databases, data lakes, and data pipelines - Identifying the skills, backgrounds, and career paths that lead to data science roles - Evaluating real job postings and positioning yourself for the role you want Join 1.3 million+ learners (4.7 stars from 77,000+ reviews, and 97% of learners liked this course). Finish with a shareable certificate you can add to your LinkedIn profile, resume, or CV. ¹ U.S. Bureau of Labor Statistics, Occupational Outlook Handbook: Data Scientists. Median wage as of May 2024; projected growth 2024 to 2034. This course provides an introduction to data science and is a first step toward data roles, not a guarantee of employment or salary. ² World Economic Forum, Future of Jobs Report 2025. Rankings reflect employer survey expectations for 2025 to 2030.

If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. In this course, you will learn and then apply this methodology that you can use to tackle any Data Science scenario. You’ll explore two notable data science methodologies, Foundational Data Science Methodology, and the six-stage CRISP-DM data science methodology, and learn how to apply these data science methodologies. Most established data scientists follow these or similar methodologies for solving data science problems. Begin by learning about forming the business/research problem Learn how data scientists obtain, prepare, and analyze data. Discover how applying data science methodology practices helps ensure that the data used for problem-solving is relevant and properly manipulated to address the question. Next, learn about building the data model, deploying that model, data storytelling, and obtaining feedback You’ll think like a data scientist and develop your data science methodology skills using a real-world inspired scenario through progressive labs hosted within Jupyter Notebooks and using Python.

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R, or Scala. Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

Whether you want to become a Data Scientist, Data Analyst, or Data Engineer, knowing how to work with SQL (Structured Query Language) is an essential skill. SQL is a powerful language used for communicating with and extracting data from databases. In this hands-on course, you’ll learn how to retrieve, organize, and manage data using SQL, starting with the fundamentals and moving to more advanced techniques. You’ll also explore how to combine SQL with Python to work with databases and analyze data. By the end of this course, you’ll be able to: - Write SQL queries using SELECT, INSERT, UPDATE, and DELETE to retrieve and manage data. - Filter and organize data using WHERE, COUNT, DISTINCT, LIMIT, ORDER BY, GROUP BY, and built-in database functions. - Create and manage database tables using CREATE, ALTER, and DROP, and understand the differences between DDL and DML. - Query complex datasets using subqueries, string patterns, ranges, and INNER and OUTER JOINs to work with data across multiple tables. - Connect SQL with Python using Jupyter notebooks to access databases and integrate SQL into your data science workflow. - Explore advanced database concepts such as Stored Procedures, Views, and ACID transactions. Throughout the course, you will complete hands-on labs, work with real databases on the Cloud, and gain experience with real data science tools. In the final project, you’ll analyze multiple real-world datasets to demonstrate your ability to query and work with data. Ready to build your SQL skills? Join over 670,000 learners who have enrolled in this course. Earn a shareable certificate to showcase your data science skills on LinkedIn, your resume, or your CV.

Analyzing data with Python is a key skill for aspiring Data Scientists and Analysts! This course takes you from the basics of importing and cleaning data to building and evaluating predictive models. You’ll learn how to collect data from various sources, wrangle and format it, perform exploratory data analysis (EDA), and create effective visualizations. As you progress, you’ll build linear, multiple, and polynomial regression models, construct data pipelines, and refine your models for better accuracy. Through hands-on labs and projects, you’ll gain practical experience using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, SciPy, and Scikit-learn. These tools will help you manipulate data, create insights, and make predictions. By completing this course, you’ll not only develop strong data analysis skills but also earn a Coursera certificate and an IBM digital badge to showcase your achievement.

This is the final course in the IBM Data Science Professional Certificate as well as the Applied Data Science with Python Specialization. This capstone project course will give you the chance to practice the work that data scientists do in real life when working with datasets. In this course you will assume the role of a Data Scientist working for a startup intending to compete with SpaceX, and in the process follow the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting your results to stakeholders. You will be tasked with predicting if the first stage of the SpaceX Falcon 9 rocket will land successfully. With the help of your Data Science findings and models, the competing startup you have been hired by can make more informed bids against SpaceX for a rocket launch. In this course, there will not be much new learning, instead you’ll focus on hands-on work to demonstrate and apply what you have learnt in previous courses. By successfully completing this Capstone you will have added a project to your data science and machine learning portfolio to showcase to employers.

Ready to build practical machine learning skills with Python? This intermediate-level course will teach you key ML concepts, help you build models with scikit-learn, and give you hands-on experience using Jupyter Notebook. You will start by exploring regression techniques, including linear, multiple linear, polynomial, and logistic regression. From there, you will learn to build supervised learning models using decision trees, K-Nearest Neighbors, and support vector machines. You’ll also discover how unsupervised learning techniques, such as clustering and dimensionality reduction with PCA, t-SNE, and UMAP, can help you uncover patterns in data. Put your knowledge into practice through hands-on labs where you’ll train and evaluate models, apply cross-validation, and optimize machine learning pipelines. You’ll bring these skills together in a final project focused on rainfall prediction, where you’ll apply machine learning techniques to a real-world problem. A course-wide exam will help reinforce your understanding of key concepts. By the end of the course, you’ll be able to: - Explain fundamental machine learning concepts and distinguish between supervised and unsupervised learning. - Build machine learning models in Python using scikit-learn, applying techniques such as regression, classification, clustering, and dimensionality reduction. - Evaluate and improve model performance using appropriate metrics, cross-validation, regularization, and optimization techniques. - Develop and assess end-to-end machine learning solutions using real-world datasets through hands-on labs and a rainfall prediction project. Join over 700,000 learners who have enrolled in this course! Earn a shareable certificate to showcase your machine learning skills on LinkedIn, your resume, or your CV.

Go from zero to working with real data in Python. No programming experience required. Data skills are some of the most in-demand skills in the job market. The U.S. Bureau of Labor Statistics projects data scientist employment to grow 34% from 2024 to 2034, much faster than average, with a median annual wage of $112,590.¹ Python is the language behind that work, and this course is where you start. This IBM course takes you from your first line of code to the skills data teams use every day. You'll start with Python basics, then build up through data structures, logic, functions, error handling, and object-oriented programming. From there, you'll work with real data using NumPy and Pandas, the industry-standard libraries for data analysis and AI, and learn to collect it from the web using REST APIs and web scraping with BeautifulSoup. You'll learn by doing. Across five modules, you'll complete more than 25 hands-on labs and practice projects in Jupyter Notebooks, the same environment professional data scientists use, with nothing to install or set up. By the end, you'll be able to write programs, clean and analyze datasets, pull data from live sources, and automate repetitive tasks, and you'll have practical work to point to in interviews. Whether you're aiming for a career in data science, data analytics, software development, data engineering, AI, or DevOps, or you want to add Python to the skills you already have, this course gives you the foundation to build on. Skills you'll gain include: - Writing Python programs using variables, data structures, loops, functions, and classes - Reading, writing, and processing data in CSV, JSON, and text formats - Analyzing and manipulating data with Pandas and NumPy - Collecting data from websites and services using REST APIs and web scraping - Working in Jupyter Notebooks to develop and document your code - Automating real-world tasks with Python scripts Join 1.5 million+ learners (4.6 stars from 43,000+ reviews, and 95% of learners liked this course). Finish with a shareable certificate you can add to your LinkedIn profile, resume, or CV. ¹ U.S. Bureau of Labor Statistics, Occupational Outlook Handbook: Data Scientists. Median wage as of May 2024; projected growth 2024 to 2034. This course builds foundational Python skills and is a first step toward data roles, not a guarantee of employment or salary.

This mini-course is intended to for you to demonstrate foundational Python skills for working with data. This course primarily involves completing a project in which you will assume the role of a Data Scientist or a Data Analyst and be provided with a real-world data set and a real-world inspired scenario to identify patterns and trends. You will perform specific data science and data analytics tasks such as extracting data, web scraping, visualizing data and creating a dashboard. This project will showcase your proficiency with Python and using libraries such as Pandas and Beautiful Soup within a Jupyter Notebook. Upon completion you will have an impressive project to add to your job portfolio. PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much instructional content. It is intended for you to apply prior Python knowledge.

Data science professionals are in high demand around the world, and the trend shows no sign of slowing. There are lots of great jobs available, but lots of great candidates too. How can you get the edge in such a competitive field? This course will prepare you to enter the job market as a great candidate for a data scientist position. It provides practical techniques for creating essential job-seeking materials such as a resume and a portfolio, as well as auxiliary tools like a cover letter and an elevator pitch. You will learn how to find and assess prospective job positions, apply to them, and lay the groundwork for interviewing. The course doesn’t stop there, however. You will also get inside tips and steps you can use to perform professionally and effectively at interviews. You will learn how to approach a code challenge and get to practice completing them. Additionally, it provides guidance about the regular functions and tasks of data scientists, as well as the opportunities of the profession and some options for career development. Let seasoned professionals share their experience to help you get ahead and land the job you want!

One of the most important skills of successful data scientists and data analysts is the ability to tell a compelling story by visualizing data and findings in an approachable and stimulating way. In this course you will learn many ways to effectively visualize both small and large-scale data. You will be able to take data that at first glance has little meaning and present that data in a form that conveys insights. This course will teach you to work with many Data Visualization tools and techniques. You will learn to create various types of basic and advanced graphs and charts like: Waffle Charts, Area Plots, Histograms, Bar Charts, Pie Charts, Scatter Plots, Word Clouds, Choropleth Maps, and many more! You will also create interactive dashboards that allow even those without any Data Science experience to better understand data, and make more effective and informed decisions. You will learn hands-on by completing numerous labs and a final project to practice and apply the many aspects and techniques of Data Visualization using Jupyter Notebooks and a Cloud-based IDE. You will use several data visualization libraries in Python, including Matplotlib, Seaborn, Folium, Plotly & Dash.