sc JNTUA B.TECH R 19 3-2 Syllabus For Data science PDF 2022 – Cynohub

# JNTUA B.TECH R 19 3-2 Syllabus For Data science PDF 2022

### Get Complete Lecture Notes for Data science on Cynohub APP  You will be able to find information about Data science along with its Course Objectives and Course outcomes and also a list of textbook and reference books in this blog.You will get to learn a lot of new stuff and resolve a lot of questions you may have regarding Data science after reading this blog. Data science has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Data science can be learnt easily as long as you have a well planned study schedule and practice all the previous question papers, which are also available on the CynoHub app.

All of the Topic and subtopics related to Data science are mentioned below in detail. If you are having a hard time understanding Data science or any other Engineering Subject of any semester or year then please watch the video lectures on the official CynoHub app as it has detailed explanations of each and every topic making your engineering experience easy and fun.

### Data science Unit One

Introduction to Data Science, A Crash Course in Python, Visualising Data

### Data science Unit Two

Linear Algebra, Statistics, Probability, Hypothesis and Inference, Gradient Descent.

### Get Complete Lecture Notes for Data science on Cynohub APP  ### Data science Unit Three

Getting Data, Working with Data, Machine Leaning, k-Nearest Neighbors, Naïve Bayes.

### Data science Unit Four

Simple Linear Regression, Multiple Regression, Logistic Regression, Decision Trees, Neural Networks.

### Data science Unit Five

Clustering, Natural Language Processing, Network Analysis, Recommender Systems.Database and SQL, MapReduce

### Data science Course Objectives

This course is designed to:Understand the approaches for handling data related problemsExplore the mathematical concepts required for Data scienceExplain the basic concepts of data science.Elucidate various Machine Learning algorithms.Introduce Natural Language Processing and Recommender Systems

### Data science Course Outcomes

After completion of this course the student would be able toVisualize the data using bar charts, line charts and scatter plots(L4). Analyse Correlation between two data objects (L4). Demonstrate feature selection and dimensionality reduction.(L2)Solve decision making problems using k-NN, Naïve Bayes, SVM and Decision.Trees (L3).Determine Clusters in data using k-means and Hierarchical Clustering methods (L3).Designbasic SQL Operations using NotQuiteABase(L6)Demonstrate the way to use machine learning algorithms using python. (L2)

### Data science Text Books

1.Data Science from Scratch, First Principles with Python -Joel Grus, O’Reilly, First Edition

### Data science Reference Books

1.The Data Science Handbook, Field Cady, WILEY.
2.An Introduction to Data Science, Jeffrey M. Stanton, Jeffrey Stanton, 2012

### Scoring Marks in Data science  