Tag: data scientist
Top Programming Languages for 2025: Your Guide to High-Paying Tech Careers
Are you wondering which programming languages will dominate the tech industry in 2025? Despite concerns about AI replacing developers, programming skills are more valuable than ever. In fact, AI is becoming a powerful ally for programmers, enhancing their capabilities rather than replacing them. Let’s explore the top 10 programming languages that promise lucrative careers and…
Read MoreTop 10 Remote Job Portals for Data Science in 2025
In today’s digital age, data science professionals are increasingly seeking flexible remote work opportunities. This comprehensive guide explores the best job portals where data scientists can find quality remote positions in 2025. 1. LinkedIn Jobs The Professional Network’s Power LinkedIn remains the powerhouse for data science recruitment, offering: 2. DataScienceCareers.io Specialized for Data Professionals This…
Read More10 Common Data Science Interview Questions and How to Answer Them?
Data science has become a very competitive field and it is important to prepare for data science interviews if you are looking for your dream job. As part of the interview process, you can expect to be asked a number of questions to assess your knowledge, skills and experience in the field. In this blog…
Read MoreTop 5 Natural Language Processing Libraries for Data Scientist
In this blog post we are going to talk about Natural Language Processing (NLP) which is one of the branches of machine learning which focuses on teaching machines to understand human language. it has multiple applications, from chatbots to sentiment analysis, and is an important skill in the data scientist’s toolbox. let’s look at five…
Read More3 Concepts Every Data Scientist Must Know Part – 3
1. What is the significance of sampling? Name some techniques for sampling? For analyzing the data, we cannot proceed with the whole volume at once for large datasets. We need to take some samples from the data which can represent the whole population. While making a sample out of complete data, we should take the…
Read More3 Concepts Every Data Scientist Must Know Part – 2
1. Bagging and Boosting Bagging and Boosting are two different ways used in combining base estimators for ensemble learning (Like random forest combining decision trees). Bagging means aggregating the predictions of several weak learners. We can think of it combining weak learners is used in parallel. The average of the predictions of several weak learners…
Read More3 Concepts Every Data Scientist Must Know Part – 1
Central Limit Theorem We first need to introduce the normal (gaussian) distribution for central limit theorem to make sense. Normal distribution is a probability distribution that look like a bell. X-axis represents the values and y-axis represents the probability of observing these values. The sigma values represent standard deviation normal distribution is used to represent…
Read MoreDifference between Data Scientist and Data Analyst
What are their skills? Data Analyst Data Mining Data Warehousing Math, Statistics Tableau and data visualization SQL Business Intelligence Advanced Excel skills Data Scientist Data Mining Data Warehousing Math, Statistics, Computer Science Tableau and Data Visualization/Storytelling Python, R, JAVA, Scala, SQL, Matlab, Pig Economics Big Data/Hadoop Machine Learning Educational requirements Data Analyst Foundational math, statistics…
Read MoreDifference between Data Scientist and Data Engineer
What do they do? Data Engineers Data Engineers design, build, test, integrate, and optimize data collected from multiple sources. They use Big Data tools and technologies to construct free-flowing data pipelines that facilitate real-time analytics applications on complex data. Data Engineers also write complex queries to improve data accessibility. Data Scientist Data Scientists are more…
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