Big Data and Data Science, A Project on Data Analytics - A Little History on Methodologies for Data Analytics, KDD Process, CRISP-DM Methodology; Data Analytics- Types, Tools and Applications
Descriptive Statistics - Scale Types, Descriptive Univariate Analysis, Descriptive Bivariate Analysis; Descriptive Multivariate Analysis - Multivariate Frequencies, Multivariate Data Visualization, Multivariate Statistics, Info graphics and Word Clouds;
Data Quality - Missing Values, Redundant Data, Inconsistent Data, Noisy Data, Outliers. Random Forest, Decision Tree, Normal and Binomial distributions, Time Series Analysis, Linear and Multiple Regression, Logistic Regression, Survival Analysis.
Prescriptive Analytics: Creating data for analytics through designed experiments, Creating data for analytics through active learning, Creating data for analytics through reinforcement learning
Overview of R programming, Environment setup with R Studio, R Commands, Variables and Data Types, Control Structures, Array, Matrix, Vectors, Factors, Functions, R packages. Reading and getting data into R (External Data): Using CSV files, XML files, Excel files.
Reference Book:
1. Dean J, ―Big Data, Data Mining and Machine learning, Wiley publications, 2014. 2. Provost F and Fawcett T, ―Data Science for Business, O‘Reilly Media Inc, 2013. 3. Janert PK, ―Data Analysis with Open Source Tools, O‘Reilly Media Inc, 2011. 4. Weiss SM, Indurkhya N and Zhang T, ―Fundamentals of Predictive Text Mining, Springer-Verlag London Limited, 2010. 5.Marz N and Warren J,- Big Data, Manning Publications,2015 6. Runkler T A, - Data Analytics: Models and Algorithms for Intelligent data analysis,Springer, 2012 7. Jared P Lander, R for everyone: advanced analytics and graphics, Pearson Education, 2013 8. Dunlop, Dorothy D., and Ajit C. Tamhane. Statistics and data analysis: from elementary to intermediate. Prentice Hall, 2000. 9. G Casella and R.L. Berger, Statistical Inference, Thomson Learning 2002.
Text Book:
1.João Moreira, Andre Carvalho, Tomás Horvath – “A General Introduction to Data Analytics” – Wiley -2018 2.An Introduction to R, Notes on R: A Programming Environment for Data Analysis and Graphics. W. N. Venables, D.M. Smith and the R Development Core Team. Version 3.0.1 (2013-05-16). URL: https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
