Applied Data Science

AUGUST 2, 2021

They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. They build production-ready systems using best-practice containerisation technologies, ETL tools and APIs.

Data Science 130

Data Science Data Scientist ML ML 130

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Statistical Analysis: Hypothesis testing, probability, regression analysis, etc. Machine Learning: Supervised and unsupervised learning techniques, deep learning, etc. Read more to know.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

AUGUST 9, 2024

Understanding ETL (Extract, Transform, Load) processes is vital for students. 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.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

APRIL 26, 2024

Data Warehousing and ETL Processes What is a data warehouse, and why is it important? Explain the Extract, Transform, Load (ETL) process. The ETL process involves extracting data from source systems, transforming it into a suitable format or structure, and loading it into a data warehouse or target system for analysis and reporting.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52