Learn Python for Data science by quiz

The "Python Test for Data Science" course is designed to equip students with essential programming skills in Python specifically tailored for data science applications. This comprehensive course covers fundamental concepts, such as data types, conditional statements, exception handling, functions, modules, object-oriented programming (OOP), and key libraries including Matplotlib, NumPy, and Pandas. By the end of this course, students will have a solid foundation in Python programming, enabling them to effectively manipulate and analyze data for various data science tasks.

Course Outline:Introduction to Python ProgrammingOverview of Python and its applications in data scienceSetting up the development environment (Python installation and IDEs)Python Data TypesNumeric data types (integers, floats, complex numbers)Sequences (strings, lists, tuples)Mapping types (dictionaries)Sets and booleansConditional Statementsif, else, and elif statementsComparison operators and logical operatorsNested conditionalsException HandlingUnderstanding exceptions and error handlingHandling exceptions using try and except blocksRaising and catching custom exceptionsFunctionsDefining and calling functionsFunction parameters and return valuesScope and variable visibilityLambda functions and built-in functionsModulesImporting and using modules in PythonExploring commonly used modules for data scienceCreating and organizing your own modulesObject-Oriented Programming (OOP)Introduction to OOP concepts (classes, objects, attributes, methods)Defining and using classes in PythonInheritance and polymorphismEncapsulation and abstractionData Visualization with MatplotlibIntroduction to Matplotlib for creating visualizationsPlotting basic graphs (line plots, scatter plots, bar plots)Customizing plots (labels, titles, legends)Creating subplots and adding annotationsNumerical Computing with NumPyIntroduction to NumPy and its multidimensional array object (ndarray)Performing mathematical operations on arraysArray slicing and indexingWorking with random numbers and basic statisticsData Manipulation and Analysis with PandasIntroduction to Pandas and its core data structures (Series, DataFrame)Loading and cleaning dataManipulating and transforming dataPerforming data analysis tasks (filtering, grouping, aggregating).