Data Science Courses

897 Courses

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Semantic Segmentation in Satellite Imagery

Explore advanced techniques in deep learning for image segmentation and object detection, focusing on practical applications in autonomous driving.

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Lessons From Fast Growing Companies in Adopting and Accelerating AI

Discover strategies for AI adoption in fast-growing companies. Learn how to capitalize on AI's potential, overcome challenges, and drive innovation and growth in startups and scale-ups.

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MLOps Without Much Ops - Building Efficient Machine Learning Systems

Discover modern, no-nonsense data pipelines for efficient machine learning systems. Learn PaaS advantages and explore real-world applications with open-source code. Gain insights on ML's future for organizations of all sizes.

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Introduction to TensorFlow - Hands-on Workshop

Explore TensorFlow through hands-on practice, completing an end-to-end tutorial to gain practical skills in building and deploying machine learning models.

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Responsible AI: Principles and Practices for Ethical Machine Learning

Explore AI's impact on business strategy and innovation with insights from industry expert Kathryn Hume at TMLS2019.

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Machine Learning in One Health

Explore machine learning applications in One Health, addressing interconnected human, animal, and environmental health issues with Graham Taylor, a leading AI researcher and entrepreneur.

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How to Set Your Internal AI Initiative for Production-Level Success

Explore strategies for maximizing AI project success in production, building credibility with decision-makers, and selecting high-impact initiatives within organizations.

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Enabling a Data-Driven Enterprise - Part 1

Discover strategies for building a data-driven organization, reducing AI bias, and fostering human-machine collaboration to drive innovation in enterprise settings.

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AI and Advanced Analytics in Retail

Explore key ML, DL, RL, and Graph Analytics use cases in Retail, with a demo of Microsoft Azure AI platform's MLOps capabilities in a Retail environment.

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Retail AI Use Case: Building a Demand Prediction Model

Explore retail AI demand prediction models, KPIs, and implementation strategies for effective business use cases in the retail industry.

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Learning Paths for Career Advancement

Data Scientist works with company data, analyses it, looks for potential dependencies, draws conclusions on this basis and, if necessary, builds visualisations. To solve such tasks, the specialist uses mathematical algorithms, development tools and special programmes. A Data Scientist understands how to build a forecast and helps to make the right decisions.

This specialist uses Data Science methods to process large amounts of information. He builds and tests models of data behaviour. This is how he finds patterns in them and predicts future values. For example, knowing everything about the demand for a product earlier, Data Scientist helps the company to make a forecast about sales in the near future. All models are built thanks to machine learning algorithms.

Usually Data Scientists become for the following reasons:

There is a desire to learn a promising and highly paid profession.

There is experience in a related industry, but you want to move to a new, more demanded direction. Data Scientists are often programmers, marketers, financiers and business analysts.

In professional activities or scientific research, it is necessary to apply innovative technologies: big data, neural networks, artificial intelligence.

To work in Data Science you need find best data science and ai courses for improving programming skills and knowledge of maths beyond the school curriculum.

Course Benefits and Features

Why choose online artificial intelligence and data science courses?

Classes are taught by experienced programmers and analysts who explain complex material, such as probability theory or mathematical analysis in an easy-to-understand way with examples.

Recordings of all lessons are stored in your personal account. You will not have gaps in your knowledge, because webinars can be watched repeatedly at any time. The videos will stay with you forever.

After each topic there is a practical task. The tasks gradually become more complex, and by the end of the course you will build a neural network or recommendation system. That is, you will have projects ready for your portfolio.

If you cannot cope with a question on your own, the tutor will help you. He will point out mistakes and give recommendations.

Many schools help with finding a job for free, and also invite students for internships, so you will have the opportunity to gain experience in a real project.

Excellent knowledge of maths, statistics, programming languages, English, as well as creativity, communication and critical thinking: employers are willing to pay more for specialists with this set of skills.

How to Enroll in Data Science and AI Courses?

Let's tell you in detail what steps you need to go through to become a Data Science specialist:

Study mathematics and linear algebra. If you have knowledge within the school curriculum, you can start with books that explain the basic concepts in simple language: derivative, differential, matrix and so on.

Any analytics uses mathematical statistics and probability theory - these are the next big topics you should familiarise yourself with.

Working in Data Science is impossible without knowledge of programming languages. For a beginner, Python is suitable - it is relatively simple, flexible and feature-rich.

The next step is to learn machine learning algorithms: "with a teacher", "without a teacher", "with reinforcement". You need to learn how to collect data for analysis and visualise it.

Having sorted out the theory, move on to practice. For example, you can look for an assistant position or an internship in large IT companies!

Data Science Courses

Semantic Segmentation in Satellite Imagery

Lessons From Fast Growing Companies in Adopting and Accelerating AI

MLOps Without Much Ops - Building Efficient Machine Learning Systems

Introduction to TensorFlow - Hands-on Workshop

Responsible AI: Principles and Practices for Ethical Machine Learning

Machine Learning in One Health

How to Set Your Internal AI Initiative for Production-Level Success

Enabling a Data-Driven Enterprise - Part 1

AI and Advanced Analytics in Retail

Retail AI Use Case: Building a Demand Prediction Model

How to train in Data Science and become one from scratch?

Learning Paths for Career Advancement

Course Benefits and Features

How to Enroll in Data Science and AI Courses?