Syllabus

Module 1:
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.

Module 2:
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).

Module 3:
Time Series Data Analysis: Introduction to Time Series, Univariate Time Series Forecasting using Statistical, Deep Learning and Hybrid Models: Autoregressive Moving Average (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.
Text Mining and Analytics: Introduction, Data Cleaning, Text Mining Techniques: Stemming, Stop-Word Removal, Tokenization, Lemmatization, Uni-gram, Bi-gram, n-gram, tf-idf, Word2Vec, Bag of Words Case Study.

Module 4:
Descriptive Modelling: k-means, Hierarchical, DBScan clustering.
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 extract valuable information for use in strategic decision making, product development, trend analysis, and forecasting.

Apply quantitative modeling and data analysis techniques to the solution of real world business problems, communicate findings, and effectively present results using data visualization techniques.

Employ cutting edge tools and technologies to analyze Big Data.

1. Develop in depth understanding of the key technologies in data science and business analytics: data mining, machine learning, visualization techniques, predictive modeling, and statistics.
2. Practice problem analysis and decision-making.
3. Gain practical, hands-on experience with statistics programming languages and big data tools through coursework and applied research experiences.
4. Students will apply data science concepts and methods to solve problems in real-world contexts and will 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