PG Program in Data Science, Machine Learning & Neural Networks in collaboration with IBM
- 11 Months
Best-in-class content by leading faculty and industry leaders in the from of videos, cases and projects, assigments and live sessions
Python is commonly used for developing websites and software, task automation, data analysis, and data visualization. Since it's relatively easy to learn, Python has been adopted by many non-programmers such as accountants and scientists, for a variety of everyday tasks, like organizing finances
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The course is good. It gives a detailed description of sqoop and flume. Every concept is very easy and simple to understand.
Instructor has a good command over the subject. Very nicely explained Excellent content, Every Hadoop developer much visit this course even if you are a experience one.
Currently, he is driving several productivity programs - using data analytics to drive insights from business operations and implementing optimizations such as streamlining workflows, improving service levels, and ultimately reducing cost.
Dr. Deepika Sharma has been associated with academics /corporate education for more than 10 years. She has a deep passion in the field of Artificial Intelligence, Data Science, and Machine Learning.
Shankar is a data Scientist with 14 Years of Experience. His current employment is with Accenture and has experience in telecom, healthcare, finance and banking products.
High-level programming language: With Python, the code looks very close to how humans think. For this purpose, it must abstract the details of the computer from you: memory management, pointers,… Hence, it is slower than “lower-level language” like C;
Python is interpreted and not compiled: Python code is interpreted at runtime instead of being compiled to native code at compile time;
Python is a dynamically typed language: Unlike “statically-typed” languages like C, C++ or Java, you don’t have to declare the variable type like String, boolean or int. The less you do, the more your computer has to work. For each attribute access, tons of lookup is required. In addition, being very dynamic makes it incredibly hard to optimize Python;
Global Interpreter Lock (GIL): This GIL basically prevents multi-threading by mandating the interpreter only execute a single thread within a single process (an instance of the Python interpreter) at a time.
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