How easy it is to write efficient code? In this post, I am adding Python to the mix. The language of choice of Data Scientists has a word to say

If you are a Data Scientist, chances are that you program in either Python or R. But there is a new kid on the block named Julia that promises C-like performance without compromising the way Data Scientists write code and interact with data. In my last post, I have compared R to Julia, showing how Julia brings a refreshening programming mindset to the Data Science community. The main takeaway is that with Julia, you no longer need to vectorize to improve performance. In fact, good use of loops might deliver the best performance. In this post, I am adding Python to the mix. The language of choice of Data Scientists has a word to say. We will solve a very simple problem where built-in implementations are available and where programming the algorithm from scratch is straightforward. The goal is to understand our options when we need to write efficient code.

ðŸ”µ Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...

Learn the essential concepts in data science and understand the important packages in R for data science. You will look at some of the widely used data science algorithms such as Linear regression, logistic regression, decision trees, random forest, including time-series analysis. Finally, you will get an idea about the Salary structure, Skills, Jobs, and resume of a data scientist.

In this video on Data Science with R full course, weâ€™ll start by learning data science from an animated video, essential concepts in data science, important packages in R, data science algorithms such as Linear regression, logistic regression, decision trees, random forest, including time-series analysis. Finally, you will get an idea about the Salary structure, Skills, Jobs, and resume of a data scientist.

In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

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