Mit Masters In Data Science

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Mit Masters In Data Science

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At Marseille Provence Airport, we believe that talent and creativity are the main drivers of our progress. This is… yes! Data has just completed his science course at the Massachusetts Institute of Technology (better known as MIT). Unfortunately, I have not yet visited the campus in Cambridge, Massachusetts, USA, but I found this course very rewarding and participated in the MIT Professional Education Program “Data Science: Data to Insights”, a six-week online course. bottom. It’s all about solving complex data problems, as explained in the primer. This topic is of increasing importance today as 90% of the world’s data was created in the last few years.

You may wonder whether you should take such a reservation. So I think it’s only fair that I share some insights with you.To help you understand my point of view, I would like to share some personal background on my IT career and Artificial Intelligence (AI) background. I’ll give you some information.

I have a degree in Computer Science and a Master’s degree in Digital Media. When I was in school, AI was not as popular a field as it is now. But that was my personal focus for his 2005 senior thesis. Unfortunately, I was only able to work as a developer for a short period of time and was unable to work in another area within AI. For almost 10 years, he has focused on IT consulting, primarily IT services, sourcing and vendor integration. I chose this online class because I want to understand the possibilities and limitations of methods and techniques.

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Each unit comes with 10-20 videos (mostly ending with a multiple-choice assessment) and 1-7 case studies. A student will receive a certificate and her CEU (1.3) only after completing all assessments.

To successfully complete the module, the (assistant) teacher provides students with her online tutorials enhanced with animations and graphs. Attendees can discuss open topics and questions in an online forum. Algorithms and concepts are always explained using industry or real-world examples. netflix or facebook.

In the first module, you will learn how to discover patterns and hidden structures in your data. For example, you can learn how to organize all the text files on your laptop based on a certain theme. Or, if you want to discover hidden communities on social networks (also called clusters), you can do that.

The focus is on bivariate and multivariate regression for predictive and causal inference, followed by logistic and nonlinear regression. Learn how to use high-dimensional data to solve prediction problems. Lasso, Ridge, Regression Trees, Boosting Trees, Random Forests, and more.

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It begins with statistical techniques for classification, hypothesis testing, and their application, including statistical anomaly detection, fraud, spam, and other malicious behavior detection. For example, a binary classification such as email is classified as spam or non-spam. Neural networks, perceptrons (a binary classifier supervised learning algorithm), deep learning, and their limitations are discussed.

Describes how to discover relevant information in large amounts of data. Learn how Netflix recommends new movies to users. How Amazon, Facebook, or Spotify recommends to their users. Learn different rules and algorithms for recommendations, ranking, collaborative filtering, and personalized recommendations.

Helps you understand network behavior. For example, how information and ideas are distributed on social networks is not only important in a marketing sense, but also how it can be used for other purposes, such as crime detection. Learn algorithms for analyzing large networks and methods for modeling network processes.

I love this program because it opens the door to unknown possibilities in technology.The instructors teach at the highest level and speak for all of us who take the course. I can honestly say that we learned something new.

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It was a lecture (which lasted an hour and a half) and was conducted by Victor Chernozhukov (http://web.mit.edu/~vchern/www/), a young professor of Russian/American heritage in the Faculty of Economics. .

His talent lies in his ability to lecture using only mathematical formulas (in addition to his immense talent for regression and prediction). He reads formulas like other people read news headlines! It’s not hard not to respect him completely. Only after watching the video so many times did I understand what it was about. The Internet, especially YouTube videos on math, have been very helpful. And that’s exactly the difference there. At school, the teacher explained a topic to him only once, but now he can practice it again and again until the topic is established.

If you can get past that difficult lecture, everyone else will have a total fun. This is especially true for deep learning courses. It was so eye-opening that I saw it twice since then. In deep learning, developers simply teach the system how to learn and how to solve problems. The system receives a set of training information to explore and learn on its own. The program is learning from previous data.

For example, the system reads animal pictures and animal names in a set test. So, learn how to identify dogs in photos. The system learns dog features based solely on images. As a result, computers have entered the realm of mental her work, hitherto only the realm of humans.

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Algorithms of this kind allow machines to take over new classification jobs previously done only by humans. For example, in interpreting medical images and diagnosing cancer, images of a dog are used only in place of her x-rays. Educational kits contain information about whether cancer has been diagnosed. After learning how to read and interpret x-rays, the system can reduce the time doctors spend analyzing images. Machines are climbing up the ladder and taking over the mental work.

Case studies are included after each unit for hands-on experience. Some research requires development skills in Python or “R”. If you’re unfamiliar with the programming language, case study documentation is supported with code snippets. For example, code is provided for how to read from an external file and how to display the data. Develop your skills in a realistic environment. For example, build your own movie recommendation system, similar to Netflix’s recommendation system.

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