JNTU-K B.TECH R19 4-1 Syllabus For Data analytics with python PDF 2022
February 1, 2022 2022-02-02 17:09JNTU-K B.TECH R19 4-1 Syllabus For Data analytics with python PDF 2022
JNTU-K B.TECH R19 4-1 Syllabus For Data analytics with python PDF 2022
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You will be able to find information about Data analytics with python 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 analytics with python after reading this blog. Data analytics with python has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Data analytics with python 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 analytics with python are mentioned below in detail. If you are having a hard time understanding Data analytics with python 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 analytics with python Unit One
Statistical Thinking in the Age of Big Data
Statistical Thinking in the Age of Big Data. Exploratory Data Analysis, The Data Science Process
Machine Learning Algorithms, Linear Regression, k-Nearest Neighbors (k-NN), k-means, Logistic Regression
Data analytics with python Unit Two
Python Language Basics
Python Language Basics, IPython, and Jupyter Notebooks: The Python Interpreter, IPython Basics, Python Language Basics, Built-in Data Structures, Functions, and Files, NumPy Basics: Arrays and Vectorized Computation, Introduction to pandas Data Structures, Essential
Functionality, Summarizing and Computing Descriptive Statistics
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Data analytics with python Unit Three
Data Loading
Data Loading, Storage, and File Formats: Reading and Writing Data in Text Format
Binary Data Formats, Interacting with Web APIs, Interacting with Databases
Data Cleaning and Preparation: Handling Missing Data, Data Transformation, String Manipulation
Data analytics with python Unit Four
Data Wrangling
Data Wrangling: Join, Combine, and Reshape
Hierarchical Indexing, Combining and Merging Datasets, Reshaping and Pivoting
Plotting and Visualization: A Brief matplotlib API Primer, Plotting with pandas and seaborn Other Python Visualization Tools
Data analytics with python Unit Five
Data Aggregation and Group Operations: GroupBy Mechanics
Data Aggregation and Group Operations: GroupBy Mechanics
Data Aggregation, Apply: General split-apply-combine, Pivot Tables and Cross-Tabulation
Time Series: Date and Time Data Types and Tools, Time Series Basics, Date Ranges, Frequencies, and Shifting, Time Zone Handling, Periods and Period Arithmetic, Resampling and Frequency Conversion, Moving Window Functions.
Data analytics with python Course Objectives
The objective of the course is to
Provide 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
Critically evaluate data visualizations based on their design and use for communicating stories from data
Data analytics with python Course Outcomes
At the end of the course, student will be able to
Describe what Data Analysis is and the skill sets needed to be a data scientist Explain in basic terms what Statistical Inference means.
Identify probability distributions commonly used as foundations for statistical modelling, Fit a model to data
Use Python to carry out basic statistical modeling and analysis
Apply basic tools (plots, graphs, summary statistics) to carry out Data Analysis
Data analytics with python Text Books
1) Doing Data Science: Straight Talk From The Frontline, 1st Edition, Cathy O’Neil and Rachel Schutt, O’Reilly, 2013.
2) McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. ” O’Reilly Media, Inc.”.
Data analytics with python Reference Books
1) Anderson Sweeney Williams (2011). Statistics for Business and Economics. “Cengage Learning”.
2) Douglas C. Montgomery, George C. Runger (2002). Applied Statistics & Probability for Engineering. “John Wiley & Sons, Inc”
3) Jiawei Han and Micheline Kamber (2006). “Data Mining: Concepts and Techniques.”
4) “Algorithms for Data Science”, 1st Edition, Steele, Brian, Chandler, John, Reddy, Swarna, springers Publications, 2016.
Scoring Marks in Data analytics with python
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Information about JNTU-K B.Tech R19 Data analytics with python was provided in detail in this article. To know more about the syllabus of other Engineering Subjects of JNTUH check out the official CynoHub application. Click below to download the CynoHub application.
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