Python is a general purpose, dynamic, high-level, and interpreted programming language. It supports Object Oriented programming approach to develop applications. It is simple and easy to learn and provides lots of high-level data structures.
Topic Name | No. of Videos | No. of Questions |
---|---|---|
Core Python | 6 | 100 |
Introduction to Languages | 1 | 100 |
Introduction to Python | 5 | 100 |
Python Software’s | 2 | 100 |
Python Language Fundamentals | 5 | 100 |
Different Modes of Python | 3 | 100 |
Python Variables | 5 | 100 |
Operators | 2 | 100 |
Input & Output Operators | 6 | 100 |
Control Statements | 3 | 100 |
Data Structures or Collections | 4 | 100 |
List Collection | 7 | 100 |
Tuple Collection | 8 | 100 |
Set Collection | 2 | 100 |
Dictionary Collection | 6 | 100 |
Functions | 5 | 100 |
Topic Name | No. of Videos | No. of Questions |
---|---|---|
Python Modules | 6 | 100 |
Packages | 1 | 100 |
OOPs | 5 | 100 |
Exception Handling & Types of Errors | 2 | 100 |
Regular expressions | 5 | 100 |
File & Directory handling | 3 | 100 |
Python Logging | 5 | 100 |
Date & Time module | 2 | 100 |
OS module | 6 | 100 |
Multi-threading & Multi Processing | 3 | 100 |
Garbage collection | 4 | 100 |
Python Data Base Communications(PDBC) | 7 | 100 |
Python – Network Programming | 8 | 100 |
Tkinter & Turtle | 2 | 100 |
Data analytics modules | 6 | 100 |
DJANGO | 5 | 100 |
Topic Name | No. of Videos | No. of Questions |
---|---|---|
Pandas – Introduction | 6 | 100 |
Pandas – Environment Setup | 1 | 100 |
Pandas – Introduction to Data Structures | 5 | 100 |
Pandas — Series | 2 | 100 |
Pandas – DataFrame | 5 | 100 |
Pandas – Panel | 3 | 100 |
Pandas – Basic Functionality | 5 | 100 |
Pandas – Descriptive Statistics | 2 | 100 |
Pandas – Function Application | 6 | 100 |
Pandas – Reindexing | 3 | 100 |
Pandas – Iteration | 4 | 100 |
Pandas – Sorting | 7 | 100 |
Pandas – Working with Text Data | 8 | 100 |
Pandas – Options and Customization | 2 | 100 |
Pandas – Indexing and Selecting Data | 6 | 100 |
Pandas – Statistical Functions | 5 | 100 |
Pandas – Window Functions | 5 | 100 |
Pandas – Aggregations | 5 | 100 |
Pandas – Missing Data | 5 | 100 |
Pandas – GroupBy | 5 | 100 |
Pandas – Merging/Joining | 5 | 100 |
Pandas – Concatenation | 5 | 100 |
Pandas – Date Functionality | 5 | 100 |
Pandas – Timedelta | 5 | 100 |
Pandas – Categorical Data | 5 | 100 |
Pandas – Visualization | 5 | 100 |
Pandas – IO Tools | 5 | 100 |
Pandas – Sparse Data | 5 | 100 |
Pandas – Caveats & Gotchas | 5 | 100 |
Pandas – Comparison with SQL | 5 | 100 |
Topic Name | No. of Videos | No. of Questions |
---|---|---|
NUMPY − INTRODUCTION | 6 | 100 |
NUMPY − ENVIRONMENT | 1 | 100 |
NUMPY − NDARRAY OBJECT | 5 | 100 |
NUMPY − DATA TYPES | 2 | 100 |
NUMPY − ARRAY ATTRIBUTES | 5 | 100 |
NUMPY − ARRAY CREATION ROUTINES | 3 | 100 |
NUMPY − ARRAY FROM EXISTING DATA | 5 | 100 |
NUMPY − ARRAY FROM NUMERICAL RANGES | 2 | 100 |
NUMPY − INDEXING & SLICING | 6 | 100 |
NUMPY − ADVANCED INDEXING | 3 | 100 |
NUMPY − BROADCASTING | 4 | 100 |
NUMPY − ITERATING OVER ARRAY | 7 | 100 |
NUMPY – ARRAY MANIPULATION | 8 | 100 |
NUMPY – BINARY OPERATORS | 2 | 100 |
NUMPY − STRING FUNCTIONS | 6 | 100 |
NUMPY − MATHEMATICAL FUNCTIONS | 5 | 100 |
NUMPY − ARITHMETIC OPERATIONS | 5 | 100 |
NUMPY − STATISTICAL FUNCTIONS | 5 | 100 |
NUMPY − SORT, SEARCH & COUNTING FUNCTIONS | 5 | 100 |
NUMPY − BYTE SWAPPING | 5 | 100 |
NUMPY − COPIES & VIEWS | 5 | 100 |
NUMPY − MATRIX LIBRARY | 5 | 100 |
NUMPY − LINEAR ALGEBRA | 5 | 100 |
NUMPY − MATPLOTLIB | 5 | 100 |
NUMPY – HISTOGRAM USING MATPLOTLIB | 5 | 100 |
NUMPY − I/O WITH NUMPY | 5 | 100 |
Problem Solving
Rs. 399
English & Verbal
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Presentation & Interview
Rs. 399
Coding with Java
Rs. 180
TISS NET
Rs. 1599
Campus Hiring Skills
Rs. 999
SBI PO
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IBPS Clerk - Rs. 50
Rs. 0
0
Mock TCS Digital - Rs. 50
Rs. 0
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CAT - Rs. 50
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