Syllabus Introduction to Data Science - (BC05001021) Total Credits L + T + (PR/2) Assessment Pattern and Marks Total Marks C Theory Tutorial / Practical ESE (E) PA / CA (M) PA (I) ESE (V) 5 70 30 20 30 150 Unit No. Content 1. Introduction to Data Science Introduction to Data Science, importance and scope of Data Science, Data Science versus Data Analytics and Business Intelligence, applications of Data Science, Data Science lifecycle. Types of data : structured, semi-structured and unstructured data, data sources and data collection methods, role of data scientist, challenges in data handling, ethical issues and data privacy. (Chapter - 1) 2. Data Collection and Data Preprocessing Data acquisition techniques, working with CSV, Excel and JSON data formats, data cleaning concepts, handling missing values and inconsistent data, noise and outlier detection, data integration and transformation, data reduction techniques, normalization and scaling, introduction to NumPy, introduction to Pandas, Series and DataFrame, data loading, selection, filtering and basic data manipulation. (Chapter - 2) 3. Data Analysis and Statistical Techniques Introduction to statistics for data science, descriptive statistics, data summarization techniques, measures of central tendency : mean, median and mode, measures of dispersion : range, variance and standard deviation, correlation and covariance, basic probability concepts, aggregation and grouping operations, interpretation of statistical results. (Chapter - 3) 4. Data Visualization Techniques Importance of data visualization, principles of visual representation, selection of appropriate visualization methods, graphical techniques including line chart, bar chart, histogram, pie chart, scatter plot and box plot, introduction to Matplotlib and Seaborn libraries, plot customization, comparison and presentation of analytical results using visualization. (Chapter - 4) 5. Exploratory Data Analysis using Python Introduction to exploratory data analysis, dataset inspection and understanding structure, data filtering and sorting, data transformation using Pandas operations, identification of trends and patterns, combining statistical analysis with visualization, case study-based data analysis, interpretation of results and preparation of analytical summaries. (Chapter - 5)
