Course Code Course Name L T P C BCA-6004 Data Science and Machine Learning 3 1 0 4 Course Outcomes: At the end of the course, the student will be able to: UNIT-I Introduction to Data Science: Evolution of Data Science, Data Science Roles, Stages in a Data Science Project, Applications of Data Science in various fields, Data Security Issues. UNIT-II Data Collection and Data Pre-Processing: Data Collection Strategies, Data Pre-Processing Overview, Data Cleaning, Data Integration and Transformation, Data Reduction. UNIT-III Exploratory Data Analytics: Descriptive Statistics - Mean Standard Deviation, Skewness and Kurtosis – Box Plots – Pivot Table – Correlation Statistics – ANOVA. UNIT-IV Introduction: Idea of Machines learning from data, Classification of problem – Regression and Classification, Supervised and Unsupervised learning. UNIT-V Neural Networks: History, Artificial and biological neural networks, Artificial intelligence and neural networks, Biological neurons, Models of single neurons, Different neural network models. Referential Books: 1. Cathy O’Neil and Rachel Schutt , “Doing Data Science”, O'Reilly, 2015. 2. David Dietrich, Barry Heller, Beibei Yang, “Data Science and Big data Analytics”, EMC 2013 3. Machine Learning, Tom M. Mitchell 4. Introduction to Machine learning, Nils J.Nilsson
- Teacher: Mamta Tiwari