sc JNTU-K B.TECH R19 4-1 Syllabus For Data science PDF 2022 – Cynohub

# JNTU-K B.TECH R19 4-1 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

Introduction, The Ascendance of Data, Motivating Hypothetical: Data Sciencester, Finding Key Connectors, The Zen of Python, Getting Python, Virtual Environments, Whitespace Formatting,

Modules, Functions, Strings, Exceptions, Lists, Tuples, Dictionaries defaultdict, Counters, Sets,

Control Flow, Truthiness, Sorting, List Comprehensions, Automated Testing and assert, Object-Oriented Programming, Iterables and Generators, Randomness, Regular Expressions, Functional Programming, zip and Argument Unpacking, args and kwargs, Type Annotations, How to Write Type Annotations.

### Data science Unit Two

#### Visualizing Data

Visualizing Data: matplotlib, Bar Charts, Line Charts, Scatterplots. Linear Algebra: Vectors, Matrices, Statistics: Describing a Single Set of Data, Correlation, Simpson’s Paradox, Some Other Correlational Caveats, Correlation and Causation.

### Data science Unit Three

#### Getting Data

Getting Data: stdin and stdout, Reading Files, Scraping the Web, Using APIs,

Working with Data: Exploring Your DataUsing NamedTuples, Dataclasses, Cleaning and Munging, Manipulating Data, Rescaling, Dimensionality Reduction.

Probability: Dependence and Independence, Conditional Probability, Bayes’s Theorem, Random

Variables, Continuous Distributions, The Normal Distribution, The Central Limit Theorem

### Data science Unit Four

#### Machine Learning

Machine Learning: Modeling, Overfitting and Underfitting, Correctness, The Bias-Variance

Tradeoff, Feature Extraction and Selection, k-Nearest Neighbors, Naive Bayes, Simple Linear Regression, Multiple Regression, Digression, Logistic Regression

### Data science Unit Five

#### Clustering

Clustering: The Idea, The Model, Choosing k, Bottom-Up Hierarchical Clustering.

Recommender Systems: Manual Curation, Recommending What’s Popular, User-Based

Collaborative Filtering, Item-Based Collaborative Filtering, Matrix Factorization

Data Ethics, Building Bad Data Products, Trading Off Accuracy and Fairness, Collaboration,

Interpretability, Recommendations, Biased Data, Data Protection IPython, Mathematics, NumPy, pandas, scikit-learn, Visualization, R

### Data science Course Objectives

From the course the student will learn

 Provide you with the knowledge and expertise to become a proficient data scientist

 Demonstrate an understanding of statistics and machine learning concepts that are vital for data science

 Learn to statistically analyze a dataset

 Explain the significance of exploratory data analysis (EDA) in data science

 Critically evaluate data visualizations based on their design and use for communicating stories from data

### Data science Course Outcomes

At the end of the course, student will be able to

 Describe what Data Science is and the skill sets needed to be a data scientist

 Illustrate in basic terms what Statistical Inference means. Identify probability distributions

commonly used as foundations for statistical modelling, Fit a model to data

 Use R to carry out basic statistical modeling and analysis

 Apply basic tools (plots, graphs, summary statistics) to carry out EDA

 Describe the Data Science Process and how its components interact

 Use APIs and other tools to scrap the Web and collect data

 Apply EDA and the Data Science process in a case study

### Data science Text Books

1) Joel Grus, “Data Science From Scratch”, OReilly. 2) Allen B.Downey, “Think Stats”, OReilly.

### Data science Reference Books

1) Doing Data Science: Straight Talk From The Frontline, 1st Edition, Cathy O’Neil and Rachel Schutt, O’Reilly, 2013

2) Mining of Massive Datasets, 2nd Edition, Jure Leskovek, Anand Rajaraman and Jeffrey Ullman, v2.1, Cambridge University Press, 2014

3) “The Art of Data Science”, 1st Edition, Roger D. Peng and Elizabeth matsui, Lean Publications, 2015

4) “Algorithms for Data Science”, 1st Edition, Steele, Brian, Chandler, John, Reddy,

Swarna, springers Publications, 2016