Data Science Courses

914 Courses

YouTube

Looker Studio Tutorial (2025) - Build Dashboards and Reports Step-by-Step

Master Looker Studio's latest features to build interactive dashboards and reports with data sources like GA4, custom visualizations, filters, and responsive layouts for impactful data storytelling.

YouTube

Free Video

23 minutes

On-Demand

YouTube

Segmentation-free Analysis of Submicron-resolution Spatial Transcriptomics

Discover a segmentation-free approach to analyzing submicron-resolution spatial transcriptomics in this short talk by Yichen Si from the Eric and Wendy Schmidt Center.

YouTube

Free Video

14 minutes

On-Demand

YouTube

TissueMosaic Enables Cross-Sample Differential Analysis of Spatial Transcriptomics Datasets Through Self-Supervised Representation Learning

Discover how TissueMosaic enables cross-sample differential analysis of spatial transcriptomics datasets through self-supervised representation learning in this research presentation.

YouTube

Free Video

17 minutes

On-Demand

YouTube

Partially Shared Multi-Modal Embedding Learns Holistic Representation of Cell State - Day 1

Discover insights into partially shared multi-modal embedding techniques for holistic cell state representation in this talk by Xinyi Zhang from MIT's Uhler Lab.

YouTube

Free Video

17 minutes

On-Demand

YouTube

Powerful and Accurate Case-Control Analysis of Spatially Resolved Molecular Data

Discover powerful techniques for case-control analysis of spatially resolved molecular data, focusing on accurate methods developed at the Raychaudhuri Lab.

YouTube

Free Video

18 minutes

On-Demand

YouTube

Painless Simulation of Multiple Pre-trained Protein Generative Models Through the Superposition Principle

Discover how to simulate multiple pre-trained protein generative models efficiently through the superposition principle in this insightful presentation from Joey Bose of Oxford's Michael Bronstein Lab.

YouTube

Free Video

17 minutes

On-Demand

YouTube

Controllable Sequence Editing for Counterfactual Generation

Discover controllable sequence editing techniques for counterfactual generation in biomedical AI, presented by Michelle Li from Harvard Medical School's Zitnik Lab.

YouTube

Free Video

16 minutes

On-Demand

Udemy

Professional Certificate: Finance Data Analysis & Analytics

Financial Data Analysis, Statistical Analysis in Finance, Analysis of Finance Markets data, Data Analytics in Finance

Udemy

Paid Course

7 hours 35 minutes

On-Demand

Udemy

Advanced Power BI: Expert Data Analysis and Visualization

From Data to Decisions with Advanced Modeling Techniques

Udemy

Paid Course

8 hours 16 minutes

On-Demand

YouTube

How to Use Tableau BI in 2025 - Step-by-Step Dashboard and Data Analysis Tutorial

Discover how to build dashboards, analyze data, and visualize insights with Tableau BI in this step-by-step tutorial for beginners.

YouTube

Free Video

20 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

Looker Studio Tutorial (2025) - Build Dashboards and Reports Step-by-Step

Segmentation-free Analysis of Submicron-resolution Spatial Transcriptomics

TissueMosaic Enables Cross-Sample Differential Analysis of Spatial Transcriptomics Datasets Through Self-Supervised Representation Learning

Partially Shared Multi-Modal Embedding Learns Holistic Representation of Cell State - Day 1

Powerful and Accurate Case-Control Analysis of Spatially Resolved Molecular Data

Painless Simulation of Multiple Pre-trained Protein Generative Models Through the Superposition Principle

Controllable Sequence Editing for Counterfactual Generation

Professional Certificate: Finance Data Analysis & Analytics

Advanced Power BI: Expert Data Analysis and Visualization

How to Use Tableau BI in 2025 - Step-by-Step Dashboard and Data Analysis Tutorial

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?