What are the best data science consulting firms

Career & Salary

Data scientists use methods, processes, algorithms and systems to draw knowledge and conclusions from structured and unstructured data. The data scientists generate information from large amounts of data and derive recommendations for the company so that it can work more efficiently in the future. Despite or perhaps because of the high demand, there is currently a shortage of data scientists.

Perhaps you have even considered continuing your education in data science yourself. The good news is that there are many helpful free resources online that will enable you to find out more about data science. In order not to lose the overview, we have put together the 15 best and free resources for you - both for beginners and "old hands" in data science.

Level 1 - The Basics

First of all, it is important to acquire the basic concepts of data science. In addition to the Python programming language, this also includes mathematical principles. Here are five very different resources, from movies to an online course:

  1. For a playful introduction, we recommend the film Moneyball with Brad Pitt and Jonah Hill. The film shows impressively which areas of daily life can be influenced by data analysis.

  2. Data science is based on mathematics. Hadrien Jean has compiled everything future data scientists need to know about mathematics on his blog hadrienj.github.io.

  3. The Python programming language is also a basic requirement for becoming a data scientist. At Dataquest.io, anyone can easily learn R, Python and SQL online without any prior knowledge.

  4. A holistic course on data science can be found at Julien Beaulieu at julienbeaulieu.github.io. Julien presents a really comprehensive curriculum with online resources to those interested. Goal: A comprehensive education in the field of data science.

  5. If you prefer to learn visually, Josh Starmer's YouTube channel StatQuest with Josh Starmer is the place for you. Josh divides the complex topic of data science into small and easy-to-understand steps in order to slowly but surely build an understanding of the topic.

Level 2 - Learn more

As soon as you have understood the basics of data science, you can delve deeper into the subject, learn from practice and understand exciting projects. The main aim here is to deepen and supplement existing knowledge.

  1. With the Data Science Weekly newsletter you are always up to date with the latest developments in data science. Current reports, articles and job offers are sent to subscribers on a weekly basis. There are more articles and interviews on the website.

  2. Grant Sanderson's YouTube channel 3Blue1Brown is a great way to deepen the math skills you have learned from Level 1. The videos are a mix of math and entertainment. The aim is to make difficult issues easy to understand with the help of animations.

  3. Advanced data scientists cannot avoid dealing with deep learning. With fast.ai, data scientist Jeremy Howard tries to make deep learning easily accessible through courses for programmers, a software library, own research and with a pronounced community aspect.

  4. Another online course is the MIT Deep Learning course. This is about deep learning methods with applications in the areas of computer vision, natural language and biology, among others. Participants learn the basics of deep learning algorithms and gain practical experience in building neural networks.

  5. Those who want to use their time as effectively as possible or learn best by listening can use the OCDevel podcast. Here, too, everything revolves around the topic of machine learning.

Level 3 - Stay on the ball

The world of data science is constantly on the move. That is why it is particularly important for data scientists to always stay up to date and learn new things.

  1. The Dataskeptic Podcasts brings out a new episode every week in which leading experts talk about data science, machine learning and AI.

  2. PyCon is the largest annual gathering of the Python community. The conference is organized from within the Python community. Recordings of conference contributions from all over the world can be found on the PyCon YouTube channel.

  3. The ML in Production blog presents best practices for the use of machine learning. The aim is to use application-oriented examples to help data scientists, machine learning engineers and AI product managers to build and use machine learning systems.

  4. The TWIML AI Podcast deals with machine learning and AI. Machine learning and AI have drastically changed the world of work, the podcast gives a voice to the latest trends in the person of scientists, data scientists and decision-makers in IT.

  5. Sebastian Ruder, a scientist in the field of natural language processing, blogs on ruder.io about machine learning, deep learning and natural language processing. The blog is particularly suitable for data science experts who want to stay up to date with the latest research. (mb)