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.

Data Science 98

Data Science Data Wrangling Machine Learning Machine Learning 98

learn data science

DECEMBER 30, 2020

The main things are Performance, Prediction, Summary View’s Correlation Mode, Text Data Wrangling UI, and Summarize Table. Performance But the performance to me is probably the most important feature for any data analysis tools. Switching between Data Frames. Moving between the Data Wrangling Steps.

Data Wrangling 52

Data Wrangling Hypothesis Testing Analytics Analytics 52

Pickl AI

MARCH 20, 2024

Essential technical skills Understanding of statistics and probability A strong foundation in statistics and probability theory forms the bedrock of Data Science. R, with its robust statistical capabilities, remains a popular choice for statistical analysis and data visualization.

Data Science 52

Data Science Data Scientist Data Wrangling Machine Learning 52

Pickl AI

FEBRUARY 12, 2024

Here are some key areas often assessed: Programming Proficiency Candidates are often tested on their proficiency in languages such as Python, R, and SQL, with a focus on data manipulation, analysis, and visualization. Explain the concept of feature engineering in Maachine Learning.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Dataconomy

MAY 16, 2023

Skills and qualifications required for the role Data scientists require a diverse set of skills and qualifications to excel in their role. Programming skills: Data scientists should be proficient in programming languages such as Python, R, or SQL to manipulate and analyze data, automate processes, and develop statistical models.

Data Scientist 156

Data Scientist ML ML Machine Learning 156

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. Most common R Libraries for Data Science In Data Science, you can find several R Libraries and perform different tasks.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

AUGUST 21, 2023

Software engineering concepts facilitate efficient data manipulation, enabling you to design algorithms, create visualizations, and build machine learning models. Step 2: Acquiring Statistical Proficiency A Data Scientist’s toolkit is incomplete without a solid understanding of statistics.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52