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Modern Data Scientist

What the industry needs?

4.33 / 5.0
1475 students1 hours 40 minutes

Created by Prof Poornachandra Sarang, Ph.D., offered on Udemy

bestcourses score™

Student feedback

4.6/10

To make sure that we score courses properly, we pay a lot of attention to the reviews students leave on courses and how many students are taking a course in the first place. This course has a total of 1475 students which left 9 reviews at an average rating of 4.33, which is average.

Course length

8.6/10

We analyze course length to see if courses cover all important aspects of a topic, taking into account how long the course is compared to the category average. This course has a length of 1 hours 40 minutes, which is pretty short. This might not be a bad thing, but we've found that longer courses are often more detailed & comprehensive. The average course length for this entire category is 7 hours 54 minutes.

Overall score

5.4/10

This course currently has a bestcourses score of 5.4/10, which makes it an average course. Overall, there are probably better courses available for this topic on our platform.

Description

These days, you find thousands of job openings for the position of a Data Scientist. The AI and machine learning has surely taken this world by storm. Though AI was invented several decades ago, we started seeing its practical usefulness in just last few years. With applications as simple as predicting house prices to sophisticated ones like person detections in real time videos for surveillance, real time traffic monitoring and those based on textual data like ratings the hotels on the basis of their past customer reviews to areas like topic modeling, real time language translations and so on. As the requirements evolved so did the technology of machine learning. Earlier, the statistical based techniques were used for predictions, recommendations etc. These were purely mathematical and statistical implemented in languages like Python, C++, Java and others. Amongst this, Python is the most widely accepted language for machine learning. The earlier Data Science was based on numeric data that is abundantly available in the industry. The business people wanted a data scientist to analyze such numeric data to give better insights into their business, customers and provide them future directions for growth.

With the introductions of artificial neural networks, the whole game has now changed. The data scientists are now expected to develop machine learning models based on ANN. As a matter of fact, ANN has solved many AI problems which were impossible to solve using the classical ML. Also, the industry came up with the requirement of model development based on new data types - that is image and text data. The size of such datasets goes probably millions of time more than than the traditional datasets which we used as a part of relational and non-relational databases. With these new datatypes, the new challenges came up in processing them and developing the models with the available resources. Though we see a huge surge in the availability of processing resources, these still are inadequate in developing models on datasets of peta byte sizes.

In this two lecture course, the first lecture defines the role of a traditional data scientist and the new role of a machine learning engineer. In the second lecture, I go deeper to explain you what the industry is asking for, what are the exceptions out of a modern data scientist.

This free course would surely help you in planning your career as a Modern Data Scientist. I look forward to you joining this course and gain lots of knowledge in this emerging AI.



What you will learn

  • Understand the role of a Data Scientist in today's Emerging AI.

Requirements

  • Machine Learning concepts
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Frequently asked questions

  • Price: Free
  • Platform: Udemy
  • Language: English
  • 1 hours 40 minutes
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bestcourses score: 5.4/10

There might be better courses available for this topic.