GTU Study
KHUDKIBOOK
DATA SCIENCE
FREE PDF
3710219
Code: 3710219
Credits: 4
Semester 1
IT
Data Science
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GTU Previous Year Papers
Data Science Syllabus — Unit-wise Content
The official GTU curriculum for Data Science (subject code 3710219) as prescribed for IT · Semester 1 divides the course into 6 units, covering 17 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3710219)Unit 1 An introduction to core concepts and technologies
- https://www.edureka.co/blog/what-is-data-science/ https://intellipaat.com/blog/what-is-data-science/ Data types: https://www.youtube.com/watch?v=hZxnzfnt5v8 https://www.youtube.com/watch?v=zHcQPKP6NpM&t=247s https://www.youtube.com/watch?v=zHcQPKP6NpM&t=247s http://www.mymarketresearchmethods.com/types-of-data-nominal-ordinal-interval-ratio/ Tools: https://www.ngdata.com/top-tools-for-data-scientists/ 8 open Source Big Data Tools to use in 2018: https://towardsdatascience.com/8-open-source-big-data-tools-to-use-in-2018-e35cab47ca1d Basic Libraries for Data Science: https://www.upwork.com/hiring/data/15-python-libraries-data-science/
Unit 2 Data collection and management
- Data collection: http://bigdata-madesimple.com/3-effective-methods-of-data-collection-for-market-research/ Data Wrangling with example: https://towardsdatascience.com/intro-to-data-science-part-2-data-wrangling-75835b9129b4 https://medium.fr7eecodecamp.org/discovering-the-secrets-of-baseball-with-data-56f793852de0 Data Analysis with example: https://medium.com/@williamkoehrsen/data-analysis-with-python-19434f5d6324 Data cleaning with example: https://www.kdnuggets.com/2016/03/doing-data-science-kaggle-walkthrough-cleaning-data.html 5 APIS a data scientist must know: https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-apis-application-programming- interfaces-5-apis-a-data-scientist-must-know/ Data storage https://searchstorage.techtarget.com/definition/big-data-storage http://www.enterprisestorageforum.com/storage-management/storage-trends/top-10-trends-for-data-storage- with-big-data-analytics.html https://www.computerweekly.com/tip/Big-data-storage-management-challenges-and-how-to-deal-with- them Multiple data sources: https://www.allerin.com/blog/top-5-sources-of-big-data http://tdan.com/combining-data-from-multiple-sources-join-integrate-blend/19877 https://www.techrepublic.com/blog/big-data-analytics/use-normalization-and-etl-to-get-the-big-data-results- you-want/ https://www.youtube.com/watch?v=f0nMfV1GvOg Books for Units 3 to 6 Book1: Data Mining Concepts and Techniques by Jiawei Han, MichelineKamber and Jian Pei Book2: Statistics and Data Analysis by A
- Abebe (available online in .pdf format
Unit 3 Data Analysis
- For Introduction
- Terminology and Concepts o Chapter 3 of Book1 for Data analysis process o https://www.tutorialspoint.com/excel_data_analysis/data_analysis_overview.htm o https://www.tutorialspoint.com/excel_data_analysis/data_analysis_process.htm Introduction to statistics
- central tendencies and distributions
- distribution properties and arithmetic o Section 2.2 (Basic Statistical Descriptions of Data) of book o http://statistics.wikidot.com/ch3 o https://www.listendata.com/2014/04/descriptive-statistics.html Central Limit Theorem (CLT) o Chap. 6 from Book2 o https://web.stanford.edu/class/archive/cs/cs109/cs109.1178/lectureHandouts/190-central- limit-theorem.pdf o https://towardsdatascience.com/understanding-the-central-limit-theorem-642473c63ad8 o https://www.tutorialspoint.com/statistics/central_limit_theorem.htm Basic Machine Learning Algorithms
- Linear regression, SVM, Naïve Bayes o Machine Learning https://www.geeksforgeeks.org/machine-learning/(What is machine learning
- applications of machine learning
- classification of machine learning methods) o Naïve Bayes Section 8.3 from Book1 https://www.geeksforgeeks.org/naive-bayes-classifiers/ o SVM Section 9.3 from Book1 https://machinelearningmastery.com/support-vector-machines-for-machine- learning/ https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector- machine-example-code/ o Linear Regression http://cs229.stanford.edu/notes/cs229-notes1.pdf https://www.geeksforgeeks.org/linear-regression-python-implementation/ http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/
Unit 4 Data Visualization
- Introduction o Section 2.3 from Book1 Types of data visualization o https://info.datalabsagency.com/blog/data-visualization-news/15-most-common-types-of- data-visualisation
- https://datavizcatalogue.com/ Data for visualization o Data types (already covered in Unit 1) o Data Encoding https://www.oreilly.com/library/view/designing-data- visualizations/9781449314774/ch04.html http://paldhous.github.io/ucb/2016/dataviz/week2.html http://www.faculty.jacobs-university.de/llinsen/teaching/340131/Lecture03.pdf Retinal variables
- mapping variables to encoding
- visual encoding o https://www.targetprocess.com/articles/visual-encoding/ o http://vda.univie.ac.at/Teaching/Vis/13s/LectureNotes/05_visual_encodings.pdf o https://www.cs.sfu.ca/~torsten/Teaching/Cmpt467/LectureNotes/05_visual_mappings.pdf
Unit 5 Module 5
- Applications of Data Science – Applications in healthcare
- education, and agriculture can be covered. https://www.analyticsvidhya.com/blog/2015/09/applications-data-science/ o Healthcare: https://medium.com/activewizards-machine-learning-company/top-7-data-science-use- cases-in-healthcare-cddfa82fd9e3 https://www.datapine.com/blog/big-data-examples-in-healthcare/ http://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtab.20180402.14.pdf o Finance https://www.mastersindatascience.org/industry/finance/ https://www.techemergence.com/predictive-analytics-in-finance/ o E-commerce: https://towardsdatascience.com/5-data-science-project-every-e-commerce-company- should-do-8746c5ab4604 https://www.datascience.com/blog/data-science-for-ecommece-businesses-predictive- modeling https://dataconomy.com/2017/07/6-ways-use-big-data-ecommerce/ o Education: https://www.expresscomputer.in/magazine/data-analytics-in-education-sector-to-see- high-growth/14468/ https://www.analyticsindiamag.com/top-6-ways-make-education-institutions-smarter- data-analytics/ https://www.allerin.com/blog/4-ways-big-data-is-transforming-the-education-sector o Agriculture: https://www.analyticsvidhya.com/blog/2018/05/data-analytics-in-the-indian- agriculture-industry/ https://www.wur.nl/upload_mm/6/0/4/307c3061-35ea-4339-a33b- d21f047d2d38_Wolfert%20et%20al%20Big%20Data%20in%20Smart%20Farming.pd f https://www.sciencedirect.com/science/article/pii/S0308521X16303754 Technologies for visualization o https://tdwi.org/articles/2011/11/09/research-excerpt-data-visualization-technology.aspx Bokeh (Python) o https://bokeh.pydata.org/en/latest/ o https://www.analyticsvidhya.com/blog/2015/08/interactive-data-visualization-library-python- bokeh/
Unit 6 Module 6
- Recent trends in various data collection techniques - https://www.tutorialspoint.com/statistics/data_collection.htm https://avaresearch.com.au/different- types-of-data-collection-methodologies/ Various visualization techniques – already covered in Unit 4 Application development methods used in data science o Python Programming o R Programming **Students must be able to implement concepts learned in data science (concepts learned in previous units) using Python and R programming
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