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Statistical Concepts A strong understanding of statistical concepts, including probability, hypothesistesting, regression analysis, and experimental design, is paramount in Data Science roles. Supervised learning algorithms learn from labelled data, where each input is associated with a corresponding output label.
Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and supportvectormachines.
Data Cleaning and Transformation Techniques for preprocessing data to ensure quality and consistency, including handling missing values, outliers, and data type conversions. Students should learn about datawrangling and the importance of data quality.
D Data Mining : The process of discovering patterns, insights, and knowledge from large datasets using various techniques such as classification, clustering, and association rule learning. DataWrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis.
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