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Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python.
There are beginner-friendly programs focusing on foundational concepts, while more advanced courses delve into specialized areas like machine learning or naturallanguageprocessing. Statistical methods like hypothesistesting and regression analysis are essential tools for drawing meaningful insights from 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. Key Deep Learning models include: Convolutional Neural Networks (CNNs) CNNs are designed to process structured grid data, such as images.
Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, NaturalLanguageProcessing , Statistics and Mathematics. It can be easily ported to multiple platforms. It is critical for knowing how to work with huge data sets efficiently.
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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