INTRODUCTION TO DATA SCIENCE B.Tech. III Year I Sem. JNTUH-R18

• Define data science and differentiate between "Big Data Hype" and reality.

• Explain the concept of datafication and its impact on various perspectives.

• Understand the principles of statistical inference, including populations, samples, and statistical modeling.

• Identify common probability distributions used for statistical models and analyze the concept of overfitting.

• Understand the basics of R programming environment and its functionalities.

Unit II: Data Types and Statistical Description

• Classify different data types based on attributes and measurement scales (nominal, ordinal, numeric, etc.).

• Calculate and interpret measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation) to describe data.

• Generate and interpret graphical representations of data using techniques like histograms, boxplots, and scatterplots.

Unit III: Data Structures in R

• Create and manipulate vectors, matrices, and arrays in R, understanding their functionalities and subsetting techniques.

• Understand the concept of factors and data frames, including their creation, subsetting, extension, and sorting.

• Learn how to create and manipulate lists in R, including accessing and modifying elements, merging lists, and converting them to vectors.

Unit IV: Programming in R

• Utilize conditional statements in R using relational and logical operators for decision-making.

• Implement iterative programming constructs like while loops and for loops to automate repetitive tasks.

• Create and utilize functions in R, understanding concepts like nested functions, function scoping, and recursion.

• Explore and utilize different R packages for data analysis and specific functionalities.

• Apply various mathematical functions provided by R for data manipulation and calculations.

Unit V: Data Reduction and Visualization

• Understand different data reduction techniques like wavelet transforms, principal component analysis (PCA), and attribute subset selection.

• Explore various statistical and machine learning models like regression and log-linear models for data reduction.

• Create and interpret diverse data visualizations using pixel-oriented, geometric projection, icon-based, and hierarchical techniques.

• Utilize R libraries and tools for effective data visualization to communicate insights from complex datasets.