ODSC - Open Data Science

MARCH 13, 2023

First, there’s a need for preparing the data, aka data engineering basics. Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

Machine Learning 94

Machine Learning Machine Learning Data Wrangling Data Science 94

ODSC - Open Data Science

APRIL 3, 2023

Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

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

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

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