ODSC - Open Data Science

DECEMBER 7, 2023

ODSC West Training Sessions and Workshops Statistics for Data Science and Measurement Brian Caffo, PhD | Professor | Johns Hopkins Bloomberg School of Public Health Babak Moghadas | Post-Doctoral Fellow Statistics and statistical inference form the core of making sense of data.

Data Science 98

Data Science Data Wrangling Machine Learning Machine Learning 98

Dataconomy

MAY 16, 2023

With the explosion of big data and advancements in computing power, organizations can now collect, store, and analyze massive amounts of data to gain valuable insights. Machine learning, a subset of artificial intelligence , enables systems to learn and improve from data without being explicitly programmed.

Data Scientist 156

Data Scientist ML ML Machine Learning 156

Pickl AI

DECEMBER 4, 2024

Understanding its core components is essential for aspiring data scientists and professionals looking to leverage data effectively. Statistics and Mathematics At its core, Data Science relies heavily on statistical methods and mathematical principles. Ensuring data quality is vital for producing reliable results.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

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

MAY 31, 2023

Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. 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.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Becoming Human

MAY 15, 2023

In Inferential Statistics, you can learn P-Value , T-Value , Hypothesis Testing , and A/B Testing , which will help you to understand your data in the form of mathematics. For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

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