Data Science Dojo

JUNE 1, 2023

The course covers topics such as linear regression, logistic regression, and decision trees. Machine Learning for Data Science by Carlos Guestrin This is an intermediate-level course that teaches you how to use machine learning for data science tasks.

Machine Learning 195

Machine Learning Machine Learning Natural Language Processing Deep Learning 195

ODSC - Open Data Science

AUGUST 15, 2023

Build Classification and Regression Models with Spark on AWS Suman Debnath | Principal Developer Advocate, Data Engineering | Amazon Web Services This immersive session will cover optimizing PySpark and best practices for Spark MLlib. Finally, you’ll explore how to handle missing values and training and validating your models using PySpark.

Machine Learning 52

Machine Learning Machine Learning Data Science Data Scientist 52

Pickl AI

MARCH 20, 2024

R, with its robust statistical capabilities, remains a popular choice for statistical analysis and data visualization. Data wrangling and preprocessing Data seldom comes in a pristine form; it often requires cleaning, transformation, and preprocessing before analysis.

Data Science 52

Data Science Data Scientist Data Wrangling Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Basic Data Science Terms Familiarity with key concepts also fosters confidence when presenting findings to stakeholders. Below is an alphabetical list of essential Data Science terms that every Data Analyst should know. Data Wrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

FEBRUARY 12, 2024

Technical Proficiency Data Science interviews typically evaluate candidates on a myriad of technical skills spanning programming languages, statistical analysis, Machine Learning algorithms, and data manipulation techniques. Differentiate between supervised and unsupervised learning algorithms.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Pickl AI

MAY 31, 2023

Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines. To obtain practical expertise, run the algorithms on datasets. It includes regression, classification, clustering, decision trees, and more.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Pickl AI

AUGUST 9, 2024

Students should learn about data wrangling and the importance of data quality. Statistical Analysis Introducing statistical methods and techniques for analysing data, including hypothesis testing, regression analysis, and descriptive statistics. Students should learn how to apply machine learning models to Big Data.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52