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;
GETTING INSIGHTS FROM DATA, DATA QUALITY AND PREPROCESSING
Descriptive Statistics - Scale Types, Descriptive Univariate Analysis, Descriptive Bivariate Analysis; Descriptive Multivariate Analysis - Multivariate Frequencies, Multivariate Data Visualization, Multivariate Statistics, Infographics and Word Clouds; Data Quality - Missing Values, Redundant Data, Inconsistent Data, Noisy Data, Outliers.
STATISTICS AND PRESCRIPTIVE ANALYTICS
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.
DATA VISUALIZATION USING R
Reading and getting data into R (External Data): Using CSV files, XML files, Web Data, JSON files, Databases, Excel files. Working with R Charts and Graphs: Histograms,Boxplots,Bar Charts, Line Graphs, Scatterplots, Pie Charts
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
