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All You Need to Know about Transitioning your Career to Data Science from Computer Science

Pickl AI

With technological developments occurring rapidly within the world, Computer Science and Data Science are increasingly becoming the most demanding career choices. Moreover, with the oozing opportunities in Data Science job roles, transitioning your career from Computer Science to Data Science can be quite interesting.

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11 Ways to do Machine Learning Better at ODSC West 2023

ODSC - Open Data Science

Many companies are now utilizing data science and machine learning , but there’s still a lot of room for improvement in terms of ROI. Completing Knowledge Discovery fast at High Quality with AI Alex Liu, Ph.D. It continues with the selection of a clustering algorithm and the fine-tuning of a model to create clusters.

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Data Science Career FAQs Answered: Educational Background

Mlearning.ai

Blind 75 LeetCode Questions - LeetCode Discuss Data Manipulation and Analysis Proficiency in working with data is crucial. This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA).

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Their primary responsibilities include: Data Collection and Preparation Data Scientists start by gathering relevant data from various sources, including databases, APIs, and online platforms. They clean and preprocess the data to remove inconsistencies and ensure its quality.

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Five machine learning types to know

IBM Journey to AI blog

That’s why diversifying enterprise AI and ML usage can prove invaluable to maintaining a competitive edge. ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions.

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Understanding Data Science and Data Analysis Life Cycle

Pickl AI

Understanding Data Science Data Science involves analysing and interpreting complex data sets to uncover valuable insights that can inform decision-making and solve real-world problems. You will collect and clean data from multiple sources, ensuring it is suitable for analysis.

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Meet the winners of the Unsupervised Wisdom Challenge!

DrivenData Labs

Solvers submitted a wide range of methodologies to this end, including using open-source and third party LLMs (GPT, LLaMA), clustering (DBSCAN, K-Means), dimensionality reduction (PCA), topic modeling (LDA, BERT), sentence transformers, semantic search, named entity recognition, and more. and DistilBERT. What motivated you to participate? :