JNTUA B.TECH R 20 2-3 Syllabus For Advanced python programming for data science PDF 2022
February 10, 2022 2022-02-10 17:03JNTUA B.TECH R 20 2-3 Syllabus For Advanced python programming for data science PDF 2022
JNTUA B.TECH R 20 2-3 Syllabus For Advanced python programming for data science PDF 2022
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You will be able to find information about Advanced python programming for 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 Advanced python programming for data science after reading this blog. Advanced python programming for data science has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Advanced python programming for 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 Advanced python programming for data science are mentioned below in detail. If you are having a hard time understanding Advanced python programming for 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.
Advanced python programming for data science Unit One
The Role of Python in Data Science
Introduction-Creating the Data Science Pipeline, Understanding Python’s Role in Data Science, Learning to Use Python Fast, Setting Up Python for Data Science, Reviewing Basic Python
Advanced python programming for data science Unit Two
Conditioning and Working with Real Data
Uploading, Streaming, and Sampling Data, Accessing Data in Structured Flat‐File Form, Sending Data in Unstructured File Form, Managing Data from Relational Databases, Interacting with Data from NoSQL Databases, Accessing Data from the Web,NumPy and pandas, Validating Your Data, Manipulating Categorical Variables, Dealing with Dates in Your Data, Slicing and Dicing: Filtering and Selecting Data, Aggregating Data at Any Level.
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Advanced python programming for data science Unit Three
Shaping and Performing Action on Data
Working with HTML Pages, Working with Raw Text, Using the Bag of Words Model and Beyond, Working with Graph Data, Contextualizing Problems and Data, Considering the Art of Feature Creation, Performing Operations on Arrays
Advanced python programming for data science Unit Four
MatPlotLib and Visualization of Data
Starting with a Graph, Setting the Axis, Ticks, Grids, Defining the Line Appearance, Using Labels, Annotations, and Legends, Choosing the Right Graph, Creating Advanced Scatterplots, Plotting Time Series, Plotting Geographical Data, Visualizing Graphs
Advanced python programming for data science Unit Five
Wrangling Data
Playing with Scikit‐learn, Performing the Hashing Trick, Considering Timing and Performance, Running in Parallel, Counting for Categorical Data, Understanding Correlation, Modifying Data Distributions, Reducing Dimensionality, Clustering, Detecting Outliers in Data
Advanced python programming for data science Course Objectives
The main objective of this course is to help students learn, understand, and practice dataanalytics using python, which include the study of modern computingbig data technologies and scaling up machine learning techniques focusing on industryapplications. Mainly the course objectives are conceptualization and summarization of data
Advanced python programming for data science Course Outcomes
After completion of the course, students will be able to
•Write relatively advanced, well structured, computer programs in Python
•Gain familiarity with principles and techniques for optimizing the performance of numeric applications
•Understand parallel computing and how parallel applications can be written in Python
•Experiment with developing GPU accelerated Python applications
•Learn the fundamentals of the most widely used Python packages; including NumPy, Pandas and Matplotlib
•Apply programming concepts in Data Analysis and Data Visualization projects
Advanced python programming for data science Text Books
1.Python for Data Science for Dummies, 2ed, Luca Massaron John Paul Mueller, by ISBN: 978‐1‐118‐84418‐2
Advanced python programming for data science Reference Books
1.Introduction to Parallel Computing, Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar, Pearson; 2 edition (January 26, 2003), ISBN 978-0201648652
2.Big Data: Principles and best practices of scalable realtime data systems, 1st Edition, Nathan Marz, James Warren, ISBN 978-1617290343
Scoring Marks in Advanced python programming for data science
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Information about JNTUA B.Tech R 20 Advanced python programming for data science 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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