Syllabus

Mathematical Foundation: Linear Algebra and Vector Calculus, Probability and Statistics.
Introduction and Motivation to Data Science, Data: Definition, Types and Facets of Data, Data Quality Data Preprocessing: Aggregation, Sampling, Dimensionality reduction, Feature subset selection, Feature creation, Discretization and Binarization, Variable transformation Measures of Similarity and Dissimilarity Data science process.
Data Warehousing: Data Preprocessing, Warehouse Architecture, ETL, OLAP, Data Lakes, Big Data Pipeline.

Descriptive Statistics: Introduction, Data Preparation Exploratory data analysis: Summarizing Data and Plotting, Scatter plot, Pair plot, Histogram, Probability Density Function (PDF), Univariate analysis using PDF, Cumulative distribution function (CDF), Percentiles and Quantiles, Inter-Quartile Range (IQR), MAD (Median Absolute Deviation), Box-plot with whiskers, Violin plots, Univariate, Bivariate, and Multivariate analysis, contour plot Outlier Treatment, Measuring Asymmetry: Skewness and Pearson’s Median Skewness Coefficient, Kernel Density Estimation: Sample and Estimated Mean, Variance and Standard Scores, Covariance, and Pearson’s and Spearman’s Rank Correlation.
Predictive Modeling: Regression, Decision Tree, Support Vector Machine (SVM), Ensemble Models: Bagging, Boosting.

Deep Learning: Introduction to Neural Network, Exploding and Vanishing Gradient Problem, Dropout, Regularization, Weight Initialization, Batch Normalization, Optimizers. Convolutional Neural Network (CNN): Convolution, Padding, Strides, Pooling, Convolution over RGB Images, Transfer Learning, Fusion: Early Fusion (Feature Level Fusion), Late Fusion (Decision Level Fusion) and Intermediate Fusion. Recurrent Neural Network (RNN), Long Short Term Neural Network (LSTM), Gated Recurrent Unit (GRU).

Time Series Data Analysis: Introduction to Time Series, Univariate Time Series Forecasting using Statistical, Deep Learning and Hybrid Models: Autoregressive(AR), Moving Average (MA), ARMA models, Autoregressive Integrated Moving Average (ARIMA) models, Seasonal ARIMA (SARIMA), Exponential Smoothing, LSTM, GRU, Hybrid statistical and deep learning models. Multivariate Time Series Forecasting, Point Forecasting, Interval Forecasting, Spatial-Temporal Forecasting.

Recommender System: Introduction, Content-Based Filtering, Collaborative Filtering, Hybrid Recommenders, Modelling User Preferences, Evaluating Recommenders.
Data Visualization and Condensation: Introduction to Data Visualization, Basic charts and dashboard, Dimensions and Measures, Visual analytics, Dashboard design & principles, Advanced design components/ principles: Enhancing the power of dashboards, Special chart types.

To mine valuable information for strategic decision making under uncertain situations, develop products, analyze trends, and forecast.

Employ quantitative and descriptive modeling of solutions for real-world business problems.

Employ cutting edge tools and technologies to analyze Big Data.

To communicate findings, and effectively present results using data visualization techniques.

Develop in depth understanding of the key technologies in data science and business analytics: data mining, machine learning, visualization techniques, predictive modeling, and statistics.

Practice problem analysis and decision-making.

Gain practical, hands-on experience with statistics, programming languages and big data tools through coursework and applied research experiences.

Apply data science concepts and methods to solve problems in real-world contexts and communicate these solutions effectively.

Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Pearson

Laura Igual and Santi Seguí, Introduction to Data Science, Springer

Davy Cielin, Arno Meysman, Mohamed Ali, Introducing Data Science, Manning

Andreas, Practical Data Science, Apress

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