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BCA Semester IV - Data Science using Python

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BCA Semester IV - Data Science using Python

Data Science using Python_

updated on icon Updated on Jun, 2025

language icon Language - English

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category icon Development ,Data Science,Python

Lectures -258

Resources -14

Quizzes -6

Duration -37 hours

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Course Description

Unit I

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.

Unit II

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.

Unit III

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.

Unit IV

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.

Unit V

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

BCA Semester IV - Data Science using Python

Curriculum

Check out the detailed breakdown of what’s inside the course

Unit 1- Introduction to data science

47 Lectures
  • play icon Getting Started with Python 01:04:43 01:04:43
  • play icon String Basics 20:10 20:10
  • play icon String Operations 44:18 44:18
  • play icon Conditional Statements in Python 19:06 19:06
  • play icon Loops in Python 14:32 14:32
  • play icon Data Structures Basics 31:13 31:13
  • play icon List 33:58 33:58
  • play icon Tuple 08:01 08:01
  • play icon Dictonary 22:19 22:19
  • play icon Set 15:54 15:54
  • play icon Functions Basics 34:07 34:07
  • play icon Anonymous Function --Lambda Function 11:49 11:49
  • play icon Special Function 14:12 14:12
  • play icon Comprehensions 20:00 20:00
  • play icon In-Built Functions 36:35 36:35
  • play icon OOP --Basics 38:08 38:08
  • play icon OOP --Inheritance, Encapsulation, Polymorphism 21:01 21:01
  • play icon Date Time Module 14:55 14:55
  • play icon RegEx --Built In Functions 13:02 13:02
  • play icon RegEx --Meta Characters 11:30 11:30
  • play icon NumPy vs List 11:38 11:38
  • play icon NumPy --Basics 27:01 27:01
  • play icon NumPy --Operations 14:03 14:03
  • play icon Pandas --Basics 28:32 28:32
  • play icon Pandas --Header & Index 11:18 11:18
  • play icon Pandas --Columns 23:32 23:32
  • play icon Pandas --loc & iloc 11:24 11:24
  • play icon Pandas --GroupBy, Sorting, Counts 15:13 15:13
  • play icon Pandas --Merge & Concatenate 11:31 11:31
  • play icon Pandas --Datetime 08:10 08:10
  • play icon Pandas --Advanced (Seperators, Rename, String Functions) 10:06 10:06
  • play icon Matplotlib --Basics 22:25 22:25
  • play icon 16. Matplotlib --Types of Graphs 10:10 10:10
  • play icon Missing Values 49:30 49:30
  • play icon Outliers --Basics 22:49 22:49
  • play icon Outliers --Visualization 11:27 11:27
  • play icon Data Cleaning on Naukri Dataset 24:56 24:56
  • play icon Data Visualization --Basics 35:11 35:11
  • play icon Line and Area Plot 18:43 18:43
  • play icon Scatter, Box and Violin Plot 31:40 31:40
  • play icon Maps 09:29 09:29
  • play icon Descriptive and Inferential Statistics 40:48 40:48
  • play icon Hypothesis Testing 39:37 39:37
  • play icon Pandas Profiling 19:05 19:05
  • play icon DABL and Sweetviz 08:54 08:54
  • play icon Capstone Project 40:22 40:22
  • play icon Resource file

Unit 2- Functions, I/O, Conditionals

42 Lectures
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Unit 3- Strings, Lists, Tuples

18 Lectures
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Unit 4-Introduction to Numpy

45 Lectures
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Unit 5-Data Manipulation with Pandas

106 Lectures
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Instructor Details

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