Modern Data Scientist
- Machine Learning concepts
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.
Who this course is for:
- Data Scientists, ML Engineers, Science/Engineering students/graduates, Academic
- 22:28Data Scientist v/s Machine Engineer
- 01:04:13Data Scientist's Role in Emerging AI
- 03:04Summary and Future Directions
Poornachandra Sarang, with his 30+ years of IT tenure, possesses a pleasant blend of education and industry experience. He has been a consultant to various top-notch IT companies. He has been a professor of Computer Engineering at the University of Notre Dame and University of Mumbai. He is a Ph.D. advisor in Computer Science, a member of Thesis Advisory committee for students pursuing Ph.D. in Computer Engineering, a course-curriculum setter for undergraduate/graduate courses in Computer Science/Engineering. He has delivered several presentations and keynotes in International conferences across the globe.