In data science, computer science and statistics converge. As data scientists, we use statistical principles to write code such that we can effectively explore the problem at hand.
Source: HOB
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Everyone wants to develop "skills in Machine Learning and AI" but few are willing to put in the hard yards to develop the foundational understanding of the relevant Math and CS
Source: HOB5691
There are a lot of computer science graduates and programmers applying for programming, coding, and software development roles at startups like Uber and Netflix; big organizations like Amazon, Microsoft, and Google; and service-based companies like Infosys or Luxsoft, but many of them have no idea of what kind of programming interview questions to expect when you're applying for a job with these companies.
Source: HOB213471
Algorithms and Data Structure are language agnostic and any programmer worth their salt should be able to convert them to their language of choice. Unfortunately, I have come across several programmers who are REALLY good on programming language e.g. Java, knows minor details of API and language intricacies but has very poor knowledge of algorithms.
Source: HOB49554
Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.
Source: HOB4773
Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.
Source: HOB1476
It's 2018 now and from last few years, phone interviews also known as the telephonic round is the single most popular way to screen candidates in a programming language job interview.
Source: HOB12246
These days organizations look for the ways where they can prepare the data very quickly and appropriately for solving the challenges of data and enabling machine learning. The data should be cleaned and accurate it should be checked before the data is brought to the model of machine learning or any other project of analytics.
Source: HOB42849
Skills of data analytics have become the leading factor in terms of the advanced development and for the career perspective, the demand of the data analyst is increasing day by day. There are several online courses which you would prefer if you want to build your career as a data analyst as it will help you to learn the fundamentals of data science, the key tools of data science and the study of programming languages in the analysis of big data.
Source: HOB6900
Technical interviews for programming language jobs can be stressful. Here are eight skills to hone that could help you ace the interview.
Source: HOB137145
Despite scoring decent grades in both my CS101 Algorithm class and Data Structures class in university, I shudder at the thought of going through a Programming Language Interview that focuses on algorithms.
Source: HOB17451
There are many people who are interested in machine learning these days. One thing is very clear that machine learning has arrived.
Source: HOB1575
To write a good code it requires skills on Algorithms and Data structures, failing of which coding seems the most difficult and frustrating task by any programmer.
Source: HOB1701
The 7 most important terms in Data Science that every Data Scientist must know while making a career in Data Science are discussed in the present article.
Source: HOB1164
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