{"id":58357,"date":"2023-09-02T17:11:12","date_gmt":"2023-09-02T17:11:12","guid":{"rendered":"https:\/\/www.inspirenignite.com\/anna-university\/ocs353-data-science-fundamentals-syllabus-for-sfe-2021-regulation-open-elective-i\/"},"modified":"2023-09-02T17:11:12","modified_gmt":"2023-09-02T17:11:12","slug":"ocs353-data-science-fundamentals-syllabus-for-sfe-2021-regulation-open-elective-i","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/anna-university\/ocs353-data-science-fundamentals-syllabus-for-sfe-2021-regulation-open-elective-i\/","title":{"rendered":"OCS353: Data Science Fundamentals syllabus for SFE 2021 regulation (Open Elective-I)"},"content":{"rendered":"

Data Science Fundamentals detailed syllabus for Safety & Fire Engineering (SFE) for 2021 regulation curriculum has been taken from the Anna Universities<\/a> official website and presented for the SFE students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below. <\/p>\n

For Safety & Fire Engineering 6th Sem scheme and its subjects, do visit SFE 6th Sem 2021 regulation scheme<\/a>. For Open Elective-I scheme and its subjects refer to SFE Open Elective-I syllabus scheme<\/a>. The detailed syllabus of data science fundamentals is as follows. <\/p>\n

Course Objectives:<\/h4>\n

Download the iStudy App for all syllabus and other updates.<\/a>
\"Get<\/a><\/h4>\n

Unit I<\/h4>\n

INTRODUCTION
\nData Science: Benefits and uses – facets of data – Data Science Process: Overview – Defining research goals – Retrieving data – data preparation – Exploratory Data analysis – build the model -presenting findings and building applications – Data Mining – Data Warehousing – Basic statistical descriptions of Data\n<\/p>\n

Unit II<\/h4>\n

DATA MANIPULATION
\nPython Shell – Jupyter Notebook – IPython Magic Commands – NumPy Arrays-Universal Functions – Aggregations – Computation on Arrays – Fancy Indexing – Sorting arrays – Structured data – Data manipulation with Pandas – Data Indexing and Selection – Handling missing data – Hierarchical indexing – Combining datasets – Aggregation and Grouping – String operations – Working with time series – High performance\n<\/p>\n

Unit III<\/h4>\n

Download the iStudy App for all syllabus and other updates.<\/a>
\"Get<\/a><\/h4>\n

Unit IV<\/h4>\n

DATA VISUALIZATION
\nImporting Matplotlib – Simple line plots – Simple scatter plots – visualizing errors – density and contour plots – Histograms – legends – colors – subplots – text and annotation – customization -three dimensional plotting – Geographic Data with Basemap – Visualization with Seaborn\n<\/p>\n

Unit V<\/h4>\n

HANDLING LARGE DATA
\nProblems – techniques for handling large volumes of data – programming tips for dealing with large data sets- Case studies: Predicting malicious URLs, Building a recommender system – Tools and techniques needed – Research question – Data preparation – Model building – Presentation and automation.\n<\/p>\n

Practical Exercises<\/h4>\n
    \n
  1. Download, install and explore the features of Python for data analytics.<\/li>\n
  2. Working with Numpy arrays<\/li>\n
  3. Working with Pandas data frames<\/li>\n
  4. Basic plots using Matplotlib<\/li>\n
  5. Statistical and Probability measures<\/li>\n

    a) Frequency distributions
    \nb) Mean, Mode, Standard Deviation
    \nc) Variability
    \nd) Normal curves
    \ne) Correlation and scatter plots
    \nf) Correlation coefficient
    \ng) Regression<\/p>\n

  6. Use the standard benchmark data set for performing the following:<\/li>\n

    a) Univariate Analysis: Frequency, Mean, Median, Mode, Variance, Standard Deviation, Skewness and Kurtosis.
    \nb) Bivariate Analysis: Linear and logistic regression modelling.<\/p>\n

  7. Apply supervised learning algorithms and unsupervised learning algorithms on any data set.<\/li>\n
  8. Apply and explore various plotting functions on any data set.<\/li>\n<\/ol>\n

    Note<\/i>
    \nExample data sets like: UCI, Iris, Pima Indians Diabetes etc.\n<\/p>\n

    Course Outcomes:<\/h4>\n

    Download the iStudy App for all syllabus and other updates.<\/a>
    \"Get<\/a><\/h4>\n

    Text Books:<\/h4>\n
      \n
    1. David Cielen, Arno D. B. Meysman, and Mohamed Ali, \u201cIntroducing Data Science\u201d, Manning Publications, 2016.<\/li>\n
    2. Jake VanderPlas, \u201cPython Data Science Handbook\u201d, O\u2019Reilly, 2016.<\/li>\n<\/ol>\n

      Reference Books:<\/h4>\n
        \n
      1. Robert S. Witte and John S. Witte, \u201cStatistics\u201d, Eleventh Edition, Wiley Publications, 2017.<\/li>\n
      2. Allen B. Downey, \u201cThink Stats: Exploratory Data Analysis in Python\u201d, Green Tea Press,2014.<\/li>\n<\/li>\n<\/ol>\n

        For detailed syllabus of all the other subjects of Safety & Fire Engineering 6th Sem, visit SFE 6th Sem subject syllabuses for 2021 regulation<\/a>. <\/p>\n

        For all Safety & Fire Engineering results, visit Anna University SFE all semester results<\/a> direct link. <\/p>\n","protected":false},"excerpt":{"rendered":"

        Data Science Fundamentals detailed syllabus for Safety & Fire Engineering (SFE) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the SFE students. […]<\/p>\n","protected":false},"author":2297,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[153],"tags":[],"class_list":["post-58357","post","type-post","status-publish","format-standard","hentry","category-sfe"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/58357","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/users\/2297"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/comments?post=58357"}],"version-history":[{"count":0,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/58357\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/media?parent=58357"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/categories?post=58357"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/tags?post=58357"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}