December 4, 2022

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To become a Post Data Scientist and Engineer! Appeared for the first time on Finstats.

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If you are interested in learning more about data science, you can find more articles on finstats here.

To become a data scientist and engineer?, what to study when it came time to choose the academic program we wanted to enroll in the university.

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Yes, a degree that includes many lessons on cutting-edge machine learning techniques will teach us how to work successfully in this field.

These degrees will teach us about tools that have the potential to transform society and business in a big way.

Which devices are these?

First, we must recognize that graduation is a combination of four important pillars:

1. Statistics and Mathematics:

To rigorously handle data and modeling complex systems.

2. Calculations:

Become proficient with the techniques used in Data Science and Machine Learning while learning about their wide range of applications.

3. Signal Processing:

Signal processing is the process of handling data that is received from digitally encoded sources of images, audio, and video.

4. Entrepreneurship:

Participation in specialized courses and cross-disciplinary projects to promote entrepreneurship will be supported.

early years

As with many engineering degrees, a mathematical foundation is necessary to understand some of the sophisticated processes.

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Because we also need to learn how to program in this profession, they also provide a coding base.

As we can see, the fundamentals of Machine Learning in the first year include Arithmetic, Statistics, Probability and Coding.

It’s really impossible to really understand this area without knowing all this information.

2nd year

This year, specialization begins to take shape, and we can see that the advanced courses of previous years are now providing deeper insights.

For example, data analysis and machine learning are now possible because we are able to understand the underlying mathematics and how to use the approaches.

The first is the encounter with the ML community!

The dualities between the disciplines of data science and data analysis as well as data engineering and databases are equally fascinating to watch.

third year

Required specializations, since you can choose to specialize in any subject in the fourth year, let’s look at the topics that are very specific to ML this year:

The most interesting year, in our opinion, was when we learned how to use cutting-edge methods to solve real issues, including data retrieval and analysis, artificial vision and image processing (computer vision), and spoken and written Major topics such as language processing are covered. (NLP).

Data storytelling, effective visualization, the chance to work with a real organization on engineering projects, and the ability to apply our knowledge to a real issue were all great benefits of data visualization.

Finally, they teach us about the ethics and ethics of these strategies in Advantage Topics in Data Engineering because using biased models can have extremely negative effects on society and discriminate against too many people.

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fourth year

You have the option to decide what you want to do with the previous year. To earn credits and complete your degree, you can either complete an internship in a business or enroll in elective courses as you did.

There were several topics really fascinating, one of which was reinforcement learning, which allows you to train a model like a game with the goal of maximizing a reward.

Also, some models are really challenging to understand, but there are ways to make them easier to understand, one of the courses is Interpretable Machine Learning.

Image Analysis II, a course in computer vision, gives you an in-depth knowledge of CNNs and image processing methods.

Thesis You can choose automatic bias detection in journalism, which will be challenging because we had to use most of the information you’ve got on this extensive journey exclusively using natural language processing.

conclusion

Learning machine learning is not an easy subject. As a professional in the field, we would suggest that it is not possible to learn data science and engineering in one year.

You can never master everything in this discipline because it is so vast, so learning is always on. We recommend that you always continue with your education, as it will advance your profession.

additional resources:-

Boost Your Resume With Machine Learning Portfolio Projects

The impact of machine learning on your daily life! ,

Training and Testing Data in Machine Learning »

If you are interested in learning more about data science, you can find more articles on finstats here.

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To become a Post Data Scientist and Engineer! Appeared for the first time on Finstats.

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