What is Python?
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.
Python Syllabus
| 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 |
Advanced Python Syllabus
| 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 |
PANDAS Syllabus
| 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 |
NUMPY Syllabus
| 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 |
