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

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 Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 28, 2024

Without linear algebra, understanding the mechanics of Deep Learning and optimisation would be nearly impossible. Concepts such as probability distributions, hypothesis testing , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

MAY 29, 2024

This knowledge allows the design of experiments, hypothesis testing, and the derivation of conclusions from data. The curriculum includes subjects like linear algebra, calculus, probability, and statistics, essential for understanding Machine Learning and Deep Learning Models.

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 Data Scientist Hypothesis Testing Natural Language Processing 52

Data Science Dojo

AUGUST 30, 2023

Statistical analysis and hypothesis testing Statistical methods provide powerful tools for understanding data. Hypothesis testing, correlation, and regression analysis, and distribution analysis are some of the essential statistical tools that data scientists use.

Data Science 195

Data Science Hypothesis Testing Machine Learning Machine Learning 195