Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Statistics : Fundamental statistical concepts and methods, including hypothesis testing, probability, and descriptive statistics.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JULY 25, 2023

With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. Statistical Analysis: Hypothesis testing, probability, regression analysis, etc.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 28, 2024

Concepts such as probability distributions, hypothesis testing , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. Big Data Tools Integration Big data tools like Apache Spark and Hadoop are vital for managing and processing massive datasets.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

JULY 10, 2023

It provides functions for descriptive statistics, hypothesis testing, regression analysis, time series analysis, survival analysis, and more. Packages like dplyr, data.table, and sparklyr enable efficient data processing on big data platforms such as Apache Hadoop and Apache Spark.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

MAY 29, 2024

This knowledge allows the design of experiments, hypothesis testing, and the derivation of conclusions from data. Big Data Technologies (Hadoop, Spark) Hadoop and Spark are super helpful for managing big data. Probability and Statistics A solid understanding of probability and statistics is essential.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Pickl AI

JUNE 19, 2024

Here are some of the most common backgrounds that prepare you well: Mathematics and Statistics These disciplines provide a rock-solid understanding of data analysis, probability theory, statistical modelling, and hypothesis testing – all essential tools for extracting meaning from data.

Data Science 52

Data Science Hypothesis Testing Data Scientist Natural Language Processing 52