Course Detail

Data Science Course

Data Science Course - PR Softwere


Course Detail


Course Description

  1. Statistics and Probability
    1. Introduction and Motivation for Data Science

    2. Basic Statistical Concepts

    3. Central Tendencies

    4. Measures of variability

    5. Probability Basics – Rules and Types

    6. Conditional Probability and Bayes’ Theorem

    7. Probability Distributions

      • Bernoulli

      • Geometric

      • Binomial

      • Poisson

      • Exponential

    8. Normal Distribution – Continuity Correction

    9. Sampling Distribution of Means - Central Limit Theorem

    10. Confidence Levels and Intervals

    11. Hypothesis Testing

      • t-distribution

      • z-distribution

      • Chi-squared distribution

      • F distribution

      • Annova

      • Errors

  2. Introduction to Python for Data Science
  3. Statistical Modelling
    1. Linear Regression

      • Simple Linear Regression

      • Covariance and Correlation

      • Coefficient of Determination

      • Residual Analysis

      • Interpreting Residuals

      • Testing the model

      • R-Squared and Significance – Caveats

      • Leverage

    2. Multiple Linear Regression

      • Interpreting Regression Coefficients

      • Assumptions of Multiple Linear Regression

      • Adjusted R-Squared

      • Handling Simple Non-linearity

      • Feature selection & Model building

      • Multicollinearity

      • Evaluating the accuracy of forecasts

    3. Logistic Regression & Naïve Bayes Classification

      • Introduction

      • Probability vs Likelihood

      • Maximum Likelihood Estimation

      • Odds Ratio

      • Psedo R2

      • Kappa Metric

      • ROC Curves and AUC

      • Gains and Lifts Charts

      • Naïve Bayes Algorithm

      • Classification problems with multiple classes

      • Naïve Bayes Assumptions

    4. PCA & Regularization

      • Dimensionality Reduction

      • Principal Component Analysis

      • Methodology

      • Evaluating Model Accuracy

      • Error Measures

      • Bias-Variance Trade-off

      • Overfitting vs Underfitting

      • Regularization

      • Ridge Regression

      • Lasso Regression

      • Elastic-net

    5. Time-series Forecasting

      • Time-series data

      • Forecasting

      • Regression on Time

      • Seasonality

      • Auto-regressive methods

      • Components of a time-series

      • Autocorelation (ACF )and Partial-Autocorelation (PACF)

      • Stationary and non-stationary data

      • SMA, WMA & Exponential smoothing

      • Holt-Winters Model

      • Residual Plots

      • AR, MA and ARIMA models

      • Model Selection

  4. Methods and Algorithms in ML
    1. Unsupervised Models – Clustering

      • Understanding Distance

      • Distance between Categorical Attributes

      • Value Distance Measure

      • Kernel Tricks

      • Clustering Applications

      • Hierarchial (Agglomerative) clustering

      • K-means and K-medoids

      • Stopping or Convergence Criteria

      • Stability Check of Clusters

    2. Association Rules

      • Frequent Pattern Analysis

      • Market-basket Analysis

      • Apriori Algorithm

    3. Reccomendation Systems

      • Collaborative Filtering o User-based o Item-based

      • Cold-Start Problems

    4. Decision Trees

      • Classification Inroduction

      • Hunt’s Algorithm

      • Tree Induction

      • Determining the best split

      • Measures of Node impurity o Gini Index o Information Gain o Gain Ratio

      • Handling Numeric Attributes

      • Stopping Criteria for Tree Induction

    5. KNN

      • Process

      • Observation

      • Issues with KNN and Instance based Learning

      • Engineering KNN

    6. Support Vector Machines (SVM)

      • Maximum Margin Classifiers

      • Basic Formulation for Linear SVMs

      • Dealing with Noisy Data

      • Non-linear decision boundaries

      • Practical advice on SVMs

    7. Ensembling Techniques

      • Ensemble Learning - Motivation

      • Majority Voting

      • Bagging Classifiers

      • Random Forest

      • Boosting & Stacking o ADA boost o Gradient Boosting Machines o Gradient Descent

    8. Architecting ML Solutions

    9. Summary ML

  5. Structured Data Processing & Visualization
    1. Data Vizualization

      • Art and Science of Story telling

      • Histograms

      • Box-plots

      • Bar chart

      • Pi chart

      • Scatter plots

  6. Text Mining
    • Motivation – Examples
    • Word Representations
    • Tokenization
    • Stemming
    • Lemmatization
    • TF-IDF
    • SVD
    • Word2Vec
    • Word Embeddings
    • One Hot Vector
    • Relevancy – Search
    • Ranking
  7. Neural Networks
    1. Artificial Neural Networks

      • Limitations of Algorithmic Solutions

      • Networks in the brain

      • Perceptrons

      • Loss Functions

        • L2, L1, and Huber

        • Binary – Margin, exponential, Log loss and Hinge loss

        • Multi – Hinge, Softmax

      • Regularization Functions

      • Gradient Descent and local minima

      • Learning Rate

      • Multi-Layer Perceptron

      • Back-propagation Algorithms

      • Limitations of MLP – Vanishing gradients, and Overfitting

      • Nurturing a deep neural network

      • Activating Functions – Sigmoid, tanh, ReLU, Leaky ReLU, ELU

      • Auto-encoding

      • Wieght Initializing

      • Batch Normalization

      • Dropouts

    2. Convoluted Neural Networks

      • Understanding images

      • Traditional approaches

      • Convolutions – 1D & 2D

      • Interpreting Convolutions as NNs

      • How convolutions solves issues

      • Hyper-parameters – Depth, Size, Stride & padding

      • Pooling

      • CNN Architectures - LeNet-5, AlexNet, ZFNet, VGGNet, Inception, ResNet

      • Pre and Post Processing

      • Transfer Learning

    3. Recurrent Neural Networks

      • Motivation

      • Sequential Data

      • Architecture

      • Types

      • Back Propagation Through Time

      • Applications

      • LSTM

      • GRU

      • Practical Tips

Institute Overview

Hyderabad, Telangana, India

If you are experiencing heart and mind combat… ‘I know’ ‘Do I know enough’, PR Software is the apt destination. A group of professionals wanting to bring a change in technical education system teaching in a corporate st... Read More

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