# KTU Data Analytics Notes CSL 322 | 2019 Scheme

KTU  Data Analytics CSL 322 is an S6 CSE Elective DA 2019 scheme course. This course will assist the learner in comprehending the fundamental ideas of data analytics. This course covers data analytics mathematics, predictive and descriptive data analytics, Big data and its applications, big data management strategies, and data analysis and visualization with the R programming tool.

A data analyst is a person who analyses data and reports findings using technical skills. On a typical day, a data analyst would retrieve data from a company database using SQL skills, analyze the data using programming skills, and then convey their findings to a larger audience using communication skills... The Notes for Data Analytics are easily available on our website (www.keralanotes.com).

 Board KTU Scheme 2019 New Scheme Year Third Year Semester S6 Subject CSL 322 |  DATA ANALYTICS Credit 3 Category KTU S6 Computer Science

## KTU S6  Data Analytics | CSL 322 | Notes (2019 Scheme)

Are you looking for study materials for CSL 322 Data Analytics? This course illustrates the mathematical concepts for data analytics, explains the basic concepts of data analytics, Illustrate various predictive and descriptive analytics algorithms, Describes the key concepts and applications of Big Data Analytics, Demonstrates the usage of the Map-Reduce paradigm for Big Data Analytics, uses R programming tool to perform data analysis and visualization.

### Module 1 - Syllabus

Mathematics for Data Analytics

Descriptive statistics - Measures of central tendency and dispersion, Association of two variables - Discrete variables, Ordinal and Continuous variable, Probability calculus - probability distributions, Inductive statistics - Point estimation, interval estimation, Hypothesis Testing - Basic definitions, test

### Module 2 - Syllabus

Introduction to Data Analytics

Introduction to Data Analysis - Analytics, Analytics Process Model, Analytical Model Requirements. Data Analytics Life Cycle overview. Basics of data collection, sampling, preprocessing and dimensionality reduction

### Module 3 - Syllabus

Predictive and Descriptive Analytics

Supervised Learning - Classification, Naive Bayes, KNN, Linear Regression. Unsupervised Learning - Clustering, Hierarchical algorithms – Agglomerative algorithm, Partitional algorithms - K- Means. Association Rule Mining - Apriori algorithm

### Module 4 - Syllabus

Big Data Analytics

Big Data Overview – State of the practice in analytics, Example Applications - Credit Risk Modeling, Business Process Analytics.Big Data Analytics using Map-Reduce and Apache Hadoop, Developing and Executing a Hadoop MapReduce Program.

### Module 5 - Syllabus

R programming for Data Analysis

Overview of modern data analytic tools. Data Analysis Using R - Introduction to R - R Graphical User Interfaces, Data Import and Export, Attribute and Data Types, Descriptive Statistics, Exploratory Data Analysis - Visualization Before Analysis, Dirty Data, Visualizing a Single Variable, Examining Multiple Variables, Data Exploration Versus Presentation, Statistical Methods for Evaluation

### Module 5 - Notes

#### Module 5 Data Analytics | CST 322 PPT Notes

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