Python is a bomb in the world of coding. It’s an all-in-one language that can do data mining, handle embedded systems, construct web apps and much more. The big daddies of the tech industry including Facebook have chosen Python for data analysis.
It is easy to learn, extremely scalable and it comes with a myriad of data science libraries. Overall, Python is a must-have in every data scientist’s toolbox.
Big Data & Hadoop
Big Data is massive! It is difficult for traditional systems like RDBMS to handle such colossal volumes of data. Secondly, Big Data involves both structured and unstructured data. The traditional systems were only able to process the structured part. This is when Hadoop came in to save the day.
Hadoop is an open-source framework released by Apache to process heterogeneous unstructured data. Hadoop is doing really well and is used by the tech giants like Yahoo, Facebook, Amazon, AOL, New York Times, etc.
Data Science Specialization (DCDS)
Machine learning (ML) algorithms observe the data patterns and take actions based on them. The text prediction functionality in your smartphone keyboard is an example of machine learning. It learns from your typing patterns, analyses your user behavior and predicts what you are going to type next.
This is called machine learning. Deep Learning (DL) is a type of machine learning that is more accurate and precise. In data science, ML and DL are used for predictive analytics.
Neural Networks & Deep Learning
The course is designed to give a conceptual understanding of how neural networks learn from data, followed by practical implementations using real-world problems. A natural extension to its predecessor course in Machine Learning, we begin at the natural shift from traditional machine learning models to network representations.
Building upon this, we gradually move from shallow to deep neural networks as we deal with examples of increasing complexity throughout the course. With this course, one can learn to design and realize primitive networks and begin to solve problems that increasingly resemble real-world scenarios.
With all the benefits of digital transformation comes their fair share of security risks. With a new malware emerging every day, data scientists must really pay attention to protecting the confidential information. This is why you should take cybersecurity courses.
Learning cybersecurity will help you manage security risks, predict threats and even visualize them using simulations. Becoming a certified cyber security professional will also open new doors for you in the ethical hacking space.
R is all about statistics and graphical representations. Presenting your results in an understandable form is as important as the analysis itself. R helps you do that. R is used for preparing data and presenting it effectively. R has applications in machine learning too.
With an extensive catalog of statistical methods, collection of libraries, a wide array of tools to capture various forms of data and integrate them into meaningful reports, R has become one of the hottest programming languages.