Pickl AI

MAY 31, 2023

After that, move towards unsupervised learning methods like clustering and dimensionality reduction. Machine Learning: Data Science aspirants need to have a good and concise understanding on Machine Learning algorithms including both supervised and unsupervised learning. Also Read: How to become a Data Scientist after 10th?

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Pickl AI

JULY 20, 2023

Here are some project ideas suitable for students interested in big data analytics with Python: 1. Kaggle datasets) and use Python’s Pandas library to perform data cleaning, data wrangling, and exploratory data analysis (EDA). Analyzing Large Datasets: Choose a large dataset from public sources (e.g.,

Analytics 40

Analytics Analytics Big Data Big Data 40

Pickl AI

JULY 10, 2023

The programming language can handle Big Data and perform effective data analysis and statistical modelling. Hence, you can use R for classification, clustering, statistical tests and linear and non-linear modelling. How is R Used in Data Science? Accordingly, Caret represents regression as well as classification training.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52