{"id":6841,"date":"2024-04-19T08:20:37","date_gmt":"2024-04-19T08:20:37","guid":{"rendered":"https:\/\/rathinamglobal.edu.in\/blog\/?page_id=6841"},"modified":"2025-02-14T07:31:31","modified_gmt":"2025-02-14T07:31:31","slug":"b-sc-data-science-and-analytics","status":"publish","type":"page","link":"https:\/\/rathinamglobal.edu.in\/blog\/b-sc-data-science-and-analytics\/","title":{"rendered":"B.Sc Data Science and Analytics"},"content":{"rendered":"<div class=\"wpb-content-wrapper\">[vc_row][vc_column][vc_tta_tabs][vc_tta_section title=&#8221;OVERVIEW OF THE PROGRAMME&#8221; tab_id=&#8221;1713451901646-ccee0db7-1b6a&#8221;][vc_column_text]The Programme in Data Science and Analytics is meticulously designed to empower participants with the expertise and skills required to analyze and interpret complex data. It combines rigorous academic theory with practical, real-world applications, covering a comprehensive curriculum that includes statistics, machine learning, data mining, and big data technologies. Through hands-on projects and case studies, students gain invaluable experience, preparing them for a successful career in the burgeoning field of data science. The programme aims to produce graduates who are not only proficient in the technical aspects of data analysis but also possess the ability to make data-driven decisions and communicate their findings effectively to stakeholders. This multidisciplinary approach ensures that graduates are well-equipped to meet the demands of an increasingly data-driven world.<\/p>\n<p>ABOUT THE PROGRAMME:<\/p>\n<p>Data Science and Analytics is delivered in both lecture-based and hands-on lab learning environments where students can develop and apply their skills to complex, real-world datasets and data science and analytics problems.Data Science with Analytics&#8221; is a program that typically combines the fields of data science and analytics to equip individuals with the skills and knowledge needed to extract insights from data. Here&#8217;s a breakdown of what this program might entail:Data science involves using various techniques, algorithms, and systems to extract insights and knowledge from structured and unstructured data. This often includes skills in programming languages like Python or R, statistical analysis, machine learning, data visualization, and data manipulation. Analytics involves the process of analyzing data to uncover patterns, trends, and insights that can be used to make data-driven decisions. This may involve techniques such as descriptive analytics (summarizing data), diagnostic analytics (identifying causes of events), predictive analytics (forecasting future trends), and prescriptive analytics (suggesting actions based on data analysis).<strong>Skills Development<\/strong>: program in data science with analytics would likely focus on developing practical skills in areas such as data collection, data cleaning, exploratory data analysis, statistical modeling, machine learning algorithms, data visualization, and interpretation of results.<strong>Tools and Technologies<\/strong>: in such a program would likely gain proficiency in tools and technologies commonly used in data science and analytics, such as Python libraries, R programming language, SQL databases, data visualization tools, , and machine learning frameworks.In<strong>Real-world Applications<\/strong>: The program may include case studies, projects, and real-world applications to provide students with hands-on experience in applying data science and analytics techniques to solve practical problems in various domains such as finance, healthcare, marketing, and others.<strong> Ethics and Privacy<\/strong> Given the sensitive nature of data and the potential impact of data-driven decisions, an emphasis on ethics, privacy, and responsible data handling practices may also be included in the curriculum.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=&#8221;PEO, PO and PSO&#8221; tab_id=&#8221;1680238749793-1b117b08-0823&#8243;][vc_column_text]<strong>Program Educational Objectives (PEO):<\/strong><\/p>\n<p><strong>The B.Sc. Computer Science with Data Analytics <\/strong>program describe accomplishments that graduates are expected to attain within five to seven years after graduation.<\/p>\n<ul>\n<li><strong>PEO1: <\/strong>Develop in depth understanding of the key technologies in data science and business analytics: data mining, machine learning, visualization techniques, predictive modeling, and statistics<\/li>\n<li><strong>PEO2: <\/strong>Apply principles of Data Science to the analysis of business problem<\/li>\n<li><strong>PEO3: <\/strong>Demonstrate knowledge of statistical data analysis techniques utilized in business decision making<\/li>\n<\/ul>\n[\/vc_column_text][vc_column_text]<strong>Program Outcomes (PO):<\/strong><\/p>\n<p><strong>On successful completion oft he B.Sc. Computer Science with Data Analytics<\/strong><\/p>\n<ul>\n<li><strong>PO1: <\/strong>Exhibit good domain knowledge <span style=\"font-size: 16px;\">and completes the assigned responsibilities <\/span>Effectively and efficiently in par with the expected quality standards.<\/li>\n<li><strong>PO2: <\/strong>Apply <strong style=\"font-size: 16px;\">analytical and critical thinking <\/strong><span style=\"font-size: 16px;\">to identify, formulate, analyze, and solve <\/span>complex problems inorder to reach authenticated conclusions<\/li>\n<li><strong>PO3:\u00a0<\/strong> <strong style=\"font-size: 16px;\">Design and develop research based solutions <\/strong><span style=\"font-size: 16px;\">for complex problems with specified needs through a ppropriate consideration for the public health, safety, cultural, societal,<\/span><br \/>\nAnd environmental concerns.<\/li>\n<li><strong>PO4:\u00a0 <\/strong>Establish the ability to <strong style=\"font-size: 16px;\">Listen, read, proficiently communicate and articulate <\/strong><strong>Complexide as <\/strong>with respect to the needs and abilities of diverse audiences<\/li>\n<li><strong>PO5:\u00a0<\/strong> <strong style=\"font-size: 16px;\">Deliver innovative ideas to instigate new business ventures <\/strong><span style=\"font-size: 16px;\">and possess the qualities <\/span>of a good entrepreneur<\/li>\n<li><strong><strong>PO6: <\/strong><\/strong>Acquire the qualities of a <strong>good leader and engage in efficient decision making.<\/strong><\/li>\n<li><strong>PO7: <\/strong>Graduates will be able to undertake any responsibility as an <strong style=\"font-size: 16px;\">individual \/ member of <\/strong><strong>multidisciplinary teams and have an understanding of team leadership<\/strong><\/li>\n<li><strong>PO8: <\/strong>Functionas <strong>s<\/strong><strong style=\"font-size: 16px;\">ocially responsible individual <\/strong><span style=\"font-size: 16px;\">with ethical values and accountable to ethically validate any actions or decisions before proceeding and actively contribute to <\/span>the societal concerns.<\/li>\n<li><strong>PO9:\u00a0 <\/strong>Identify and <strong style=\"font-size: 16px;\">address own educational need <\/strong>si<span style=\"font-size: 16px;\">nachanging world in ways sufficient to <\/span>maintain the competence and to allow them to contribute to the advancement of knowledge<\/li>\n<li><strong>PO10:\u00a0 <\/strong><strong style=\"font-size: 16px;\">Demonstrate knowledge and understanding of management principles <\/strong>a<span style=\"font-size: 16px;\">nd apply <\/span>these to one own work tomanage projects and in multi disciplinary environment<\/li>\n<\/ul>\n[\/vc_column_text][vc_column_text]<strong>Program Specific Outcomes (PSO):<\/strong><\/p>\n<p><strong>After the successful completion of B.Sc. Computer Science with Data Analytics <\/strong>program the students are expected to<\/p>\n<ul>\n<li><strong>PSO1: <\/strong>Impart education with domain knowledge effectively and efficiently in par with the expected quality standards for Data analyst professional<\/li>\n<li><strong>PSO2: <\/strong>Ability to apply the mathematical, technical and critical thinking skills in the discipline of Data analytics to find solutions for complex problems.<\/li>\n<li><strong>PSO3: <\/strong>Ability to engage in life-long learning and adopt fast changing technology to prepare for professional development.<\/li>\n<li><strong>PSO4: <\/strong>Expose the students to key technologies in data science and business analytics:data mining, machine learning, visualization techniques, predictive modeling, and statistics.<\/li>\n<li><strong>PSO5:\u00a0 <\/strong>Inculcate effective communication skills combined with professional &amp; ethical attitude.<\/li>\n<\/ul>\n[\/vc_column_text][\/vc_tta_section][vc_tta_section title=&#8221;Career Opportunities&#8221; tab_id=&#8221;1680238749805-bc5dbf88-e50c&#8221;][vc_column_text]<strong>Data Scientist<\/strong>: Data scientists are at the heart of extracting actionable insights from complex datasets. They use a combination of programming, statistical skills, and machine learning to analyze data and predict trends.<\/p>\n<p><strong>Data Analyst<\/strong>: Data analysts focus on processing and performing statistical analysis on existing datasets. Their work often involves creating visualizations, dashboards, and reports to help businesses make informed decisions.<\/p>\n<p><strong>Machine Learning Engineer<\/strong>: These professionals specialize in creating algorithms and predictive models to make predictions or automate decision-making based on data. They work closely with data scientists to implement and optimize machine learning projects.<\/p>\n<p><strong>Data Engineer<\/strong>: Data engineers build and maintain the architecture (like databases and large-scale processing systems) that allows for the efficient analysis and processing of large data sets. They ensure that data flows smoothly from source to database to analytics.<\/p>\n<p><strong>Business Intelligence Analyst<\/strong>: BI Analysts use data analytics and visualization tools to develop insights into the business performance and market trends. They help in strategic planning by providing data-based recommendations to the management.<\/p>\n<p><strong>Quantitative Analyst (Quant)<\/strong>: In the finance sector, quants use data analytics to model and predict financial markets, helping companies in risk management, investment management, and trading strategies.<\/p>\n<p><strong>Data Analytics Consultant<\/strong>: These consultants work across industries, advising businesses on how to use data analytics to improve processes, increase efficiency, and boost profits. They often work on a project basis and may serve multiple clients.<\/p>\n<p><strong>Big Data Engineer\/Architect<\/strong>: Big Data Engineers or Architects handle the management and organization of big data environments. Their work involves designing, building, and maintaining scalable and secure big data ecosystems.<\/p>\n<p><strong>AI Specialist<\/strong>: Specialists in artificial intelligence develop AI models and applications, often working closely with machine learning engineers and data scientists to integrate AI capabilities into various products and services.[\/vc_column_text][\/vc_tta_section][vc_tta_section title=&#8221;Curriculum Structure&#8221; tab_id=&#8221;1680238796758-f51620ce-2270&#8243;][wgl_spacing spacer_size=&#8221;30px&#8221;][\/vc_tta_section][\/vc_tta_tabs][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]\n<table>\n<tbody>\n<tr>\n<td>\n     <strong>S.No.<\/strong>\n    <\/td>\n<td>\n     <strong>Sem<\/strong>\n    <\/td>\n<td>\n     <strong>Part<\/strong>\n    <\/td>\n<td>\n     <strong>Sub Type<\/strong>\n    <\/td>\n<td>\n     <strong>Sub Code<\/strong>\n    <\/td>\n<td>\n     <strong>Subject<\/strong>\n    <\/td>\n<td>\n     <strong>Credit<\/strong>\n    <\/td>\n<td>\n     <strong>Hours<\/strong>\n    <\/td>\n<td>\n     <strong>INT<\/strong>\n    <\/td>\n<td>\n     <strong>EXT<\/strong>\n    <\/td>\n<td>\n     <strong>Total<\/strong>\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      1\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      L1\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Language &#8211; I\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      2\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      L2\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      English &#8211; I\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      3\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; I Theory Programming in C\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      4\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; II Practical Programming in C\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      5\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Allied\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Allied-I&nbsp;<br \/>\n      Mathematics for Computer Science\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      6\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      SEC\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Skill Enhancement Courses &ndash; I     Database Management System \/ Practical &ndash; Database Management system Lab\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      7\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      AEC\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Ability Enhancement Course I     Environmental Studies or     Universal Human Values &amp; Professional Ethics\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      50\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      24\n    <\/td>\n<td>\n      30\n    <\/td>\n<td>\n      350\n    <\/td>\n<td>\n      300\n    <\/td>\n<td>\n      650\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      1\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      L1\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Language &#8211; II\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      2\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      L2\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      English &#8211; II\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      3\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; III Theory Python Programming\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      4\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; IV&nbsp; Practical&nbsp; Python Programming Lab\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      5\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Elective\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Elective&nbsp; &#8211; I      Entreprenuership Development\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      6\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Allied\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Allied-II Discreate Mathematics\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      7\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      AEC\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Ability Enhancement Course II     Design Thinking\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      50\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      8\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      Ext\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Extension Activity &#8211; I (NASA)\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      25\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      25\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      25\n    <\/td>\n<td>\n      30\n    <\/td>\n<td>\n      375\n    <\/td>\n<td>\n      300\n    <\/td>\n<td>\n      675\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      1\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      L1\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Language &#8211; III\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      2\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      L2\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      English &#8211; III\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      3\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; V Theory&nbsp; Programming Concept Using Java&nbsp;\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      4\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; VI&nbsp; Practical Programming Concept Using Java&nbsp; Lab\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      5\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Allied\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Allied-III Quantitative Aptitude\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      6\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      SEC\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Skill Enhancement Courses &ndash; II Practical \/ Training Fundamentals of Data Science\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      7\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      AEC\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Ability Enhancement Course III     Soft Skill-1\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      50\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      8\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      ITR\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Internship \/ Industrial Training      (Summer vacation at the end of II semester activity)\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      50\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      9\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      Ext\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Extension Activity &#8211; II (NASA)\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      25\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      25\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      27\n    <\/td>\n<td>\n      30\n    <\/td>\n<td>\n      425\n    <\/td>\n<td>\n      300\n    <\/td>\n<td>\n      725\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      1\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      L1\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Language &#8211; IV\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      2\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      L2\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      English &#8211; IV\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      3\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; VII Theory&nbsp; Programming in R Language\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      4\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; VIII Practical &nbsp; Programming in R Lab\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      5\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Allied\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Allied-IV&nbsp; Maths for data Science\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      8\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Elective&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Elective&nbsp; &#8211; II&nbsp; &#8211; Data Mining\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      7\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      AEC\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Ability Enhancement Course IV     Soft Skill-2\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      50\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      8\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      Ext\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Extension Activity &#8211; III (NASA)\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      25\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      25\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      25\n    <\/td>\n<td>\n      30\n    <\/td>\n<td>\n      375\n    <\/td>\n<td>\n      300\n    <\/td>\n<td>\n      675\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      1\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; IX Theory Big Data Technology\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      2\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; X Practical&nbsp; Big Data Technology Lab\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      3\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Elective&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Elective&nbsp; &#8211; III-Natural Language Processing\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      PRJ\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Project\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      0\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      SEC\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Skill Enhancement Courses &ndash; III Practical \/ Training &nbsp; Machine Learning Foundations\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      5\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      ITR\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Internship \/ Industrial Training      (Summer vacation at the end of IV semester activity)\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      50\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      6\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      Ext\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Extension Activity &#8211; IV (NASA)\n    <\/td>\n<td>\n      1\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      25\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      25\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      19\n    <\/td>\n<td>\n      30\n    <\/td>\n<td>\n      275\n    <\/td>\n<td>\n      200\n    <\/td>\n<td>\n      475\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      1\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; XI Theory&nbsp; Cloud Computing\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      2\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Core&nbsp;&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core&nbsp; Course &ndash; XII Practical &nbsp; Cloud Computing Lab\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      3\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      Elective&nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Elective &ndash; IV &nbsp; Image Analytics\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      PRJ\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Core Project\n    <\/td>\n<td>\n      8\n    <\/td>\n<td>\n      8\n    <\/td>\n<td>\n      100\n    <\/td>\n<td>\n      100\n    <\/td>\n<td>\n      200\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      5\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      SEC\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Skill Enhancement Courses &ndash; IV Practical \/ Training Algorithms in Data Science\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      24\n    <\/td>\n<td>\n      30\n    <\/td>\n<td>\n      300\n    <\/td>\n<td>\n      300\n    <\/td>\n<td>\n      600\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Total credit\n    <\/td>\n<td>\n      144\n    <\/td>\n<td>\n      180\n    <\/td>\n<td>\n      2100\n    <\/td>\n<td>\n      1700\n    <\/td>\n<td>\n      3800\n    <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<table>\n<tbody>\n<tr>\n<td colspan=\"11\">\n     <strong>Additional Credits<\/strong>\n    <\/td>\n<\/tr>\n<tr>\n<td>\n     <strong>S.No.<\/strong>\n    <\/td>\n<td>\n     <strong>Sem<\/strong>\n    <\/td>\n<td>\n     <strong>Part<\/strong>\n    <\/td>\n<td>\n     <strong>Sub Type<\/strong>\n    <\/td>\n<td>\n     <strong>Course <\/strong><strong>Code<\/strong>\n    <\/td>\n<td>\n     <strong>Course Name<\/strong>\n    <\/td>\n<td>\n     <strong>Credit<\/strong>\n    <\/td>\n<td>\n     <strong>Hours<\/strong>\n    <\/td>\n<td>\n     <strong>INT<\/strong>\n    <\/td>\n<td>\n     <strong>EXT<\/strong>\n    <\/td>\n<td>\n     <strong>Total<\/strong>\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      1\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      VAC\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      VAC &#8211; Microsoft CoE Course \/ NPTEL\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      50\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      3\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      IDC\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      VAC &#8211; Microsoft CoE Course \/ NPTEL\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      50\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      VAC\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      VAC &#8211; Microsoft CoE Course \/ NPTEL\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      50\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      50\n    <\/td>\n<\/tr>\n<tr>\n<td>&nbsp;<\/td>\n<td>&nbsp;<\/td>\n<td>&nbsp;<\/td>\n<td>&nbsp;<\/td>\n<td>&nbsp;<\/td>\n<td>&nbsp;<\/td>\n<td>&nbsp;<\/td>\n<td>&nbsp;<\/td>\n<td>&nbsp;<\/td>\n<td>&nbsp;<\/td>\n<td>&nbsp;<\/td>\n<\/tr>\n<tr>\n<td colspan=\"11\">\n     <strong> Certificate on Minor Discipline<\/strong>\n    <\/td>\n<\/tr>\n<tr>\n<td>\n     <strong>S.No.<\/strong>\n    <\/td>\n<td>\n     <strong>Sem<\/strong>\n    <\/td>\n<td>\n     <strong>Part<\/strong>\n    <\/td>\n<td>\n     <strong>Sub Type<\/strong>\n    <\/td>\n<td>\n     <strong>Course <\/strong><strong>Code<\/strong>\n    <\/td>\n<td>\n     <strong>Course Name<\/strong>\n    <\/td>\n<td>\n     <strong>Credit<\/strong>\n    <\/td>\n<td>\n     <strong>Hours<\/strong>\n    <\/td>\n<td>\n     <strong>INT<\/strong>\n    <\/td>\n<td>\n     <strong>EXT<\/strong>\n    <\/td>\n<td>\n     <strong>Total<\/strong>\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      1\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      MD\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Course &#8211; I\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      100\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      2\n    <\/td>\n<td>\n      3\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      MD\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Course &#8211; II\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      100\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      3\n    <\/td>\n<td>\n      4\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      MD\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Course &#8211; III\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      100\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<tr>\n<td>\n      4\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      6\n    <\/td>\n<td>\n      MD\n    <\/td>\n<td>\n      &nbsp;\n    <\/td>\n<td>\n      Course &#8211; IV\n    <\/td>\n<td>\n      5\n    <\/td>\n<td>\n      2\n    <\/td>\n<td>\n      0\n    <\/td>\n<td>\n      100\n    <\/td>\n<td>\n      100\n    <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n[\/vc_column_text][\/vc_column][\/vc_row]\n\n    <div class=\"xs_social_share_widget xs_share_url after_content \t\tmain_content  wslu-style-1 wslu-share-box-shaped wslu-fill-colored wslu-none wslu-share-horizontal wslu-theme-font-no wslu-main_content\">\n\n\t\t\n        <ul>\n\t\t\t        <\/ul>\n    <\/div> \n<\/div>","protected":false},"excerpt":{"rendered":"<p>[vc_row][vc_column][vc_tta_tabs][vc_tta_section title=&#8221;OVERVIEW OF THE PROGRAMME&#8221; tab_id=&#8221;1713451901646-ccee0db7-1b6a&#8221;][vc_column_text]The Programme in Data Science and Analytics is meticulously designed to empower participants with the expertise and skills required to analyze and interpret complex data. 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