Dataconomy

MAY 16, 2023

Machine learning engineer vs data scientist: two distinct roles with overlapping expertise, each essential in unlocking the power of data-driven insights. As businesses strive to stay competitive and make data-driven decisions, the roles of machine learning engineers and data scientists have gained prominence.

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Pickl AI

AUGUST 21, 2023

In a digital era fueled by data-driven decision-making, the role of a Data Scientist has become pivotal. With the 650% jump in the implementation of analytics, the role of Data Scientists is becoming profound. Companies are looking forward to hiring crème de la crème Data Scientists.

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Pickl AI

MARCH 20, 2024

Summary: The role of a Data Scientist has emerged as one of the most coveted and lucrative professions across industries. Combining a blend of technical and non-technical skills, a Data Scientist navigates through vast datasets, extracting valuable insights that drive strategic decisions.

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ODSC - Open Data Science

DECEMBER 7, 2023

Data Wrangling with Python Sheamus McGovern | CEO at ODSC | Software Architect, Data Engineer, and AI Expert Data wrangling is the cornerstone of any data-driven project, and Python stands as one of the most powerful tools in this domain.

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Pickl AI

MAY 31, 2023

Accordingly, you need to make sense of the data that you derive from the various sources for which knowledge in probability, hypothesis testing, regression analysis is important. It is critical for knowing how to work with huge data sets efficiently. Also Read: How to become a Data Scientist after 10th?

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Pickl AI

FEBRUARY 12, 2024

Data Science interviews are pivotal moments in the career trajectory of any aspiring data scientist. Having the knowledge about the data science interview questions will help you crack the interview. However, cracking the interview can be challenging. Explain the concept of feature engineering in Maachine Learning.

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Pickl AI

JULY 10, 2023

R’s data manipulation capabilities make cleaning and preprocessing data easy before further analysis. · Statistical Analysis: R has a rich ecosystem of packages for statistical analysis. Packages like tidyverse, knitr, and shiny offer a cohesive data import, cleaning, analysis, visualization, and reporting ecosystem.

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