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