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Data Science using Python_
Updated on Sep, 2026
Programming, Data Science, Python
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Introduction to data science – Introduction to data science, Data Science Components, Data Science Process, Data Science Jobs Roles, Tools for Data Science, Difference between Data Science with BI (Business Intelligence), Applications of Data Science, Challenges of Data Science Technology.
Data analysis – Introduction to data analysis, Data Analysis Tools, Types of Data Analysis: Techniques and Methods, Data Analysis Process
Introduction to Python, Python features, Python Interpreter, modes of Python Interpreter, Values and Data types, Variables, Keywords, Identifiers, and Statements.
Expressions, Input & Output, Comments, Lines & Indentation, Quotations, Tuple assignment, Operators, Precedence of operators.
Functions: Definition and use, Types of functions, Flow of execution, Parameters and Arguments, Modules.
Conditionals: Conditional(if), Alternative(if-else), Chained Conditionals(if-elif-else), Nested conditionals; Iteration/Control statements: while, for, break, continue, pass; fruitful function vs void function, Parameters/Arguments, Return values, Variables scope(local, global), Function composition.
Strings: Strings, String slices, Immutability, String functions & Methods, String module; List as an array: Array, Methods of the array.
Lists: List operations, List slices, List methods, List loops, Mutability, aliasing, Cloning list, List parameters; Tuple: Benefit of Tuple, Operations on Tuple, Tuple methods, Tuple assignment, Tuple as return value, Tuple as argument; Dictionaries: Operations on Dictionary, methods in
Dictionary, Difference between List, Tuple and Dictionary; Advanced List processing: List comprehension, Nested List.
Introduction to Numpy – The basics of NumPy array, computation on numpy arrays, aggregations, computations on arrays, comparisons, masks and Boolean logic, fancy indexing, sorting arrays, structured data.
Data Manipulation with Pandas – Introducing pandas objects, data indexing and selection, operating on data in pandas, handling missing data, hierarchical indexing, combining datasets, aggregation and grouping
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