Dataconomy

MAY 16, 2023

Mathematical and statistical knowledge: A solid foundation in mathematical concepts, linear algebra, calculus, and statistics is necessary to understand the underlying principles of machine learning algorithms. Data visualization and communication: Data scientists need to effectively communicate their findings and insights to stakeholders.

Data Scientist 156

Data Scientist ML ML Machine Learning 156

Pickl AI

NOVEMBER 28, 2024

For example, in neural networks, data is represented as matrices, and operations like matrix multiplication transform inputs through layers, adjusting weights during training. Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

JULY 25, 2023

Skills and Tools of Data Scientists To excel in the field of Data Science, professionals need a diverse skill set, including: Programming Languages: Python, R, SQL, etc. Statistical Analysis: Hypothesis testing, probability, regression analysis, etc. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

AUGUST 9, 2024

Data Cleaning and Transformation Techniques for preprocessing data to ensure quality and consistency, including handling missing values, outliers, and data type conversions. Students should learn about data wrangling and the importance of data quality.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

ML 52

ML ML Machine Learning Machine Learning 52

Pickl AI

APRIL 26, 2024

I break down the problem into smaller manageable tasks, define clear objectives, gather relevant data, apply appropriate analytical techniques, and iteratively refine the solution based on feedback and insights. Describe a situation where you had to think creatively to solve a data-related challenge.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52