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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.
Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, DecisionTrees, Regression Analysis Problem-solving capability Big Data: (..)
It provides functions for descriptive statistics, hypothesistesting, 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.
Concepts such as probability distributions, hypothesistesting , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. DecisionTrees These trees split data into branches based on feature values, providing clear decision rules.
Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decisiontrees, and support vector machines. 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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