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Data science is the study of extracting value from data. Data scientists help firms interpret and manage large, complex sets of data and convert them into valuable insights to solve critical business problems. They typically have a foundation in computer science, modelling, statistics, analytics and math.
Data science is one of the thriving fields in all kinds of industries across the world in these recent years.
Introduction to Data Science – Introduction to Machine Learning – What is Python – Role ofPython in Machine Learning and Python –Installing Python – Python IDE’s – JupyterNotebook Installation and usage.
What is package – Advantage of usingpackages over core Python – Pandas – Numpy– Sci-kit Learn – Mat-plot library.
Selecting rows / observations – RoundingNumbers – Selecting columns and fields –Merging data – Data aggregation – Datamunging techniques.
Probability Basics – What is meant byprobability, Types, ODDS ratio – StandardDeviation – Data deviation andDistribution , Variance. Bias variantTradeoff – Underfitting, Overfitting.Distance metrics - Euclidean Distance,Manhattan Distance. Outlier analysis -What is an Outlier?, Inter Quartile Range,Box & whisker plot, Upper Whisker, LowerWhisker, Scatter plot, Cook’s Distance, -Missing Value treatment - What is NA?,Central Imputation, KNN imputation,Dummification. Correlation - Pearsoncorrelation, positive & Negativecorrelation.
Supervised Learning - Linear Regression,Linear Equation, Slope, Intercept, R squarevalue, Logistic regression, ODDS ratio,Probability of success, Probability of failureBias Variance Tradeoff, ROC curve, BiasVariance Tradeoff –
Unsupervised Learning - K-Means, K-Means ++, Hierarchical Clustering - SVM -Support Vectors, Hyperplanes, 2-D Case,Linear Hyperplane - SVM Kernal – Linear,Radial, Polynomial
Select query - Sub Query - Regular Expressions Functions andConditions in SQL - Date and Time in SQL - SQL Privileges
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