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)
Module 1
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 1 - Notes
Module 1 Data Analytics | CST 322 HANDWRITTEN Notes
Module 1 Data Analytics | CST 322 PPT 1 Notes
Module 1 Data Analytics | CST 322 PPT 2 Notes
Module 2
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 2 - Notes
Module 2 Data Analytics | CST 322 PPT Notes
Module 3
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 3 - Notes
Module 3 Data Analytics | CST 322 PPT Notes
Module 4
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 4 - Notes
Module 4 Data Analytics | CST 322 PPT Notes
Module 5
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
KTU S6 CSE Related Links
KTU S6 CSE Syllabus | Click Here |
KTU S6 CSE Study Notes | Click Here |
KTU S6 CSE Reference Textbook | Click Here |
KTU S6 CSE Previous Year Solved Questions | Click Here |
KTU S6 CSE Study Materials | Click Here |
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CST 306 Algorithm Analysis And Design | Click Here |
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CSL 342 Automated Verification (Elective) | Click Here |
CSL 352 Introduction To IA32 Architecture (Elective) | Click Here |
CSL 362 Programming In Python (Elective) | Click Here |
CSL 372 Data And Computer Communication (Elective) | Click Here |
CSL 332 Networking Lab | Click Here |
CSD 334 Mini project | Click Here |