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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.

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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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