Data Science Courses

909 Courses

YouTube

High Performance Machine Learning, Deep Learning, and Data Science - Principles and Practice

Master high-performance computing principles for machine learning and deep learning, focusing on practical implementation strategies and advanced techniques for optimizing data science workflows.

YouTube

Free Video

3 hours 6 minutes

On-Demand

YouTube

Parameter-Efficient Automation of Data Wrangling Tasks with Prefix-Tuning

Explore how prefix-tuning offers an efficient alternative to LLM fine-tuning for data wrangling tasks, requiring minimal parameter updates while maintaining comparable performance in data integration and cleaning.

YouTube

Free Video

17 minutes

On-Demand

YouTube

Humans and AI: Understanding LLM Impact on Human Decision Making

Explore the synergistic potential between humans and LLMs in decision-making tasks, examining active learning, ML fairness, and collaborative approaches to AI implementation.

YouTube

Free Video

1 hour 2 minutes

On-Demand

YouTube

Synthetic Data Generation and Applications in Python

Discover how to generate synthetic data for machine learning and data analysis projects, with practical Python examples and implementation strategies.

YouTube

Free Video

18 minutes

On-Demand

YouTube

Creating Your AI/BI Genie Space for Business Data Analytics

Discover how to empower business teams with AI/BI Genie - a conversational interface that enables natural language data exploration and self-service analytics for instant insights.

YouTube

Free Video

13 minutes

On-Demand

FutureLearn

Practical Machine Learning for AI: Foundational Skills and Experiments

Discover machine learning principles and applications on this course for non-ML specialists.

FutureLearn

Free Online Course (Audit)

2 weeks, 3 hours a week

On-Demand

YouTube

The Importance of Arts and Crafts in ThreatOps - Effective Visual Communication for Cybersecurity Analysis

Master effective visualization techniques for cybersecurity incidents and threat intelligence, enhancing your ability to communicate complex security concepts through clear, standardized diagrams.

YouTube

Free Video

44 minutes

On-Demand

YouTube

Deep Learning and Process Understanding for Data-Driven Earth System Science - Lecture 37

Explore deep learning applications in Earth System Science, focusing on process understanding and data-driven approaches to environmental research and analysis.

YouTube

Free Video

1 hour 4 minutes

On-Demand

YouTube

Visualization for Data Science - Creating Views and Dashboards

Master data visualization techniques for creating impactful views and dashboards, focusing on best practices for effective data presentation and communication in data science applications.

YouTube

Free Video

1 hour 17 minutes

On-Demand

YouTube

Introduction to Machine Learning Model Explanations and Interpretability

Explore key concepts in data science explanations, from gradient-based highlighting to contrastive editing, covering essential techniques for understanding and interpreting complex models.

YouTube

Free Video

1 hour 21 minutes

On-Demand

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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

High Performance Machine Learning, Deep Learning, and Data Science - Principles and Practice

Parameter-Efficient Automation of Data Wrangling Tasks with Prefix-Tuning

Humans and AI: Understanding LLM Impact on Human Decision Making

Synthetic Data Generation and Applications in Python

Creating Your AI/BI Genie Space for Business Data Analytics

Practical Machine Learning for AI: Foundational Skills and Experiments

The Importance of Arts and Crafts in ThreatOps - Effective Visual Communication for Cybersecurity Analysis

Deep Learning and Process Understanding for Data-Driven Earth System Science - Lecture 37

Visualization for Data Science - Creating Views and Dashboards

Introduction to Machine Learning Model Explanations and Interpretability

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?