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Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.
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.
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. These models may include regression, classification, clustering, and more.
Hence, you can use R for classification, clustering, statistical tests and linear and non-linear modelling. It provides functions for descriptive statistics, hypothesistesting, regression analysis, time series analysis, survival analysis, and more. How is R Used in Data Science?
Concepts such as probability distributions, hypothesistesting , 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.
Knowledge of supervised and unsupervised learning and techniques like clustering, classification, and regression is essential. This knowledge allows the design of experiments, hypothesistesting, and the derivation of conclusions from data. This skill allows the creation of predictive models and insights from data.
Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesistesting, confidence intervals). Scikit-learn covers various classification , regression , clustering , and dimensionality reduction algorithms. These concepts help you analyse and interpret data effectively.
After that, move towards unsupervised learning methods like clustering and dimensionality reduction. Accordingly, you need to make sense of the data that you derive from the various sources for which knowledge in probability, hypothesistesting, regression analysis is important.
Statistical analysis and hypothesistesting Statistical methods provide powerful tools for understanding data. Hypothesistesting, correlation, and regression analysis, and distribution analysis are some of the essential statistical tools that data scientists use.
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