BCA Semester IV - Data Science using Python
Data Science using Python_
Updated on Jun, 2025
Language - English
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
Curriculum
Check out the detailed breakdown of what’s inside the course
Unit 1- Introduction to data science
47 Lectures
-
Getting Started with Python
01:04:43
01:04:43
-
String Basics
20:10
20:10
-
String Operations
44:18
44:18
-
Conditional Statements in Python
19:06
19:06
-
Loops in Python
14:32
14:32
-
Data Structures Basics
31:13
31:13
-
List
33:58
33:58
-
Tuple
08:01
08:01
-
Dictonary
22:19
22:19
-
Set
15:54
15:54
-
Functions Basics
34:07
34:07
-
Anonymous Function --Lambda Function
11:49
11:49
-
Special Function
14:12
14:12
-
Comprehensions
20:00
20:00
-
In-Built Functions
36:35
36:35
-
OOP --Basics
38:08
38:08
-
OOP --Inheritance, Encapsulation, Polymorphism
21:01
21:01
-
Date Time Module
14:55
14:55
-
RegEx --Built In Functions
13:02
13:02
-
RegEx --Meta Characters
11:30
11:30
-
NumPy vs List
11:38
11:38
-
NumPy --Basics
27:01
27:01
-
NumPy --Operations
14:03
14:03
-
Pandas --Basics
28:32
28:32
-
Pandas --Header & Index
11:18
11:18
-
Pandas --Columns
23:32
23:32
-
Pandas --loc & iloc
11:24
11:24
-
Pandas --GroupBy, Sorting, Counts
15:13
15:13
-
Pandas --Merge & Concatenate
11:31
11:31
-
Pandas --Datetime
08:10
08:10
-
Pandas --Advanced (Seperators, Rename, String Functions)
10:06
10:06
-
Matplotlib --Basics
22:25
22:25
-
16. Matplotlib --Types of Graphs
10:10
10:10
-
Missing Values
49:30
49:30
-
Outliers --Basics
22:49
22:49
-
Outliers --Visualization
11:27
11:27
-
Data Cleaning on Naukri Dataset
24:56
24:56
-
Data Visualization --Basics
35:11
35:11
-
Line and Area Plot
18:43
18:43
-
Scatter, Box and Violin Plot
31:40
31:40
-
Maps
09:29
09:29
-
Descriptive and Inferential Statistics
40:48
40:48
-
Hypothesis Testing
39:37
39:37
-
Pandas Profiling
19:05
19:05
-
DABL and Sweetviz
08:54
08:54
-
Capstone Project
40:22
40:22
-
Resource file
Unit 2- Functions, I/O, Conditionals
42 Lectures
Unit 3- Strings, Lists, Tuples
18 Lectures
Unit 4-Introduction to Numpy
45 Lectures
Unit 5-Data Manipulation with Pandas
106 Lectures
Instructor Details
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