Course Content
Python Libraries
Numpy: Array handling, Indexing & Slicing
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Pandas: DataFrame manipulation, Merging datasets, Handling missing data
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Matplotlib: Line plots, Scatter plots, Customization of plots
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Probability & Applied Statistics
Probability Distributions: Normal, Binomial
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Bayes Theorem: Prior, Likelihood, Posterior
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Hypothesis Testing: T-tests, Chi-square test, ANOVA etc
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Confidence Interval: Z-scores, Standard Error
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Standard Deviation: Data deviation & Variance
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PowerBI
Visual Analytics: Filters, Groups, Sets
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Charts & Graphs: Line chart, Bar chart, Scatter plots
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Advanced Charts: Treemaps, Waterfall charts, Geo maps
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Worksheet: Formatting, Calculated fields, Parameters
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Excel, SQL
Excel Functions: VLOOKUP, IF statements, Text functions
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Pivot Tables: Creating, Sorting, Filtering
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Charts in Excel: Pie, Line, Stacked charts
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SQL Commands: SELECT, INSERT, DELETE, UPDATE
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Joins & GroupBy: Inner Join, Left Join, Aggregate functions
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Fundamentals of Modeling, Machine Learning & Deep Learning
Linear Algebra: Matrix operations, Eigenvalues & Eigenvectors
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Classification: Decision boundaries, Cost function
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Gradient Descent: Learning rates, Convergence
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Optimization: Stochastic Gradient Descent
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Intro to Predictive Modeling
Linear Regression: Residual analysis, R-squared, MSE
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Logistic Regression: Sigmoid function
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Polynomial: Feature scaling, Regularization
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Bias-Variance: Tradeoff, Model complexity, underfitting, overfitting
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Machine Learning
Neural Networks: Activation functions, Backpropagation, Epochs
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Supervised Learning: Decision trees, Naive Bayes
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Unsupervised Learning: K-means
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ML Algorithms: KNN, Random Forest, Gradient Boost
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