Course Overview
- Prerequisite, Target Audience
This Hands-on training program on Data Science drives participants through the basics of Python and Statistics before diving in and exploring Data Science in depth. It takes participants through exploratory as well as Real time scenarios in Data Science and also touches base on introduction to Machine Learning.
Requirements
- This training requires participants to have a basic understanding in Python Programming. A knowledge in Mathematics & Statistics would be helpful to attend the training program.
Curriculum
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Introduction to Data Science
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Basics of Statistics
- Statistics Concepts
- Random variable
- Type of Random variables
- Central Tendencies – Mean, Mode, Median, Probability, Probability Distribution of Random variables, PMF, PDF, CDF
- Type of RV – Nominal, Ordinal, Interval, Ratio; Variance, Standard Deviation
- Normal Distribution, Standard Normal Distribution
- Binomial Distribution
- Poisson Distribution
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Advanced Statistics
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Python Programming for Data Science (Lab)
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Graphics and Data Visualization, Exploratory Data Analysis in Python (Lab)
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Machine Learning Concepts
- Introduction to Machine Learning
- Supervised and Unsupervised ML, Parametric/Non-parametric Machine Learning Algorithms
- Machine Learning Models
- Linear Regression
- Logistic Regression
- Classification & KNN
- Decision trees
- Random Forest
- Clustering – K Means & hierarchical Clustering
- Time Series Analysis
- ARIMA Models
- Support Vector Machine
- Model Validation/Cross-validation techniques, Parameter tuning, Model evaluation metrics, MSE, RMSE, R square, Adjusted R Square
- Confusion Matrix
- Bias and Variance
- Underfitting, over Fitting
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Real World Data Science & Machine Learning Case Studies in Python (Lab)
