TCS NPT Python Developer Exam Details
The TCS NPT Python Developer course provides comprehensive training in Python programming and its application in real-world software development. This course covers core Python concepts, data analysis, web development, and application development using advanced Python libraries and frameworks. It focuses on hands-on experience and practical skills that are essential for becoming a proficient Python Developer.
What You Will Learn With TCS NPT Python Developer Course
The Python Developer course is structured to equip you with a variety of skills necessary for full-fledged Python development:
-
Core Python Development
- Importance: Essential for mastering Python programming and building robust applications.
- Learning Focus: Fundamentals of Python, object-oriented programming (OOPs) concepts, error handling, and functions.
- Skills Covered: Writing efficient Python code, understanding data types, expressions, operators, and object-oriented design principles.
-
Data Analysis and Munging
- Importance: Data analysis is crucial for extracting meaningful insights from raw data.
- Learning Focus: Using Python libraries like NumPy and Pandas for data manipulation and cleaning.
- Skills Covered: Working with datasets, handling missing data, performing data transformations, and aggregating data.
-
NumPy for N-Dimensional Arrays
- Importance: NumPy is key for handling large datasets and performing complex numerical operations.
- Learning Focus: Mastering NumPy arrays, performing mathematical operations, and handling N-dimensional data.
- Skills Covered: Array creation, slicing, indexing, matrix operations, and basic statistics with NumPy.
-
Exploratory Data Analysis (EDA)
- Importance: EDA helps in uncovering underlying patterns in the data before building models.
- Learning Focus: Techniques for performing EDA using Pandas and visualization tools like Matplotlib.
- Skills Covered: Data visualization (scatter plots, histograms, box plots), correlation analysis, identifying outliers, and summarizing data distributions.
-
Visualization with Matplotlib
- Importance: Visualizing data helps in interpreting patterns and trends effectively.
- Learning Focus: Plotting data using Matplotlib and customizing visualizations to present insights clearly.
- Skills Covered: Creating different plots like line, bar, and pie charts, managing subplots, and customizing plot aesthetics.
-
Web Development with Python
- Importance: Building interactive web applications using Python-based frameworks.
- Learning Focus: Introduction to Flask and Django for web application development.
- Skills Covered: Creating routes, handling requests and responses, building dynamic web pages, and integrating databases with web apps.
TCS NPT Python Developer Exam Structure
The Python Developer Exam consists of two parts:
Part A: Test of Knowledge
| Total Questions |
Marks per MCQ |
Total Marks |
Duration (Minutes) |
| 50 |
1 |
50 |
60 |
Syllabus for TCS NPT Python Developer Exam
Syllabus for Part A: Test of Knowledge
| Sr. No. |
Module |
Descriptor |
Topics Covered |
| 1 |
Statistics Basics |
Foundations of Statistics, data summarization, and statistical inference. |
Descriptive statistics (mean, median, mode, variance, standard deviation), hypothesis testing, probability, and distributions. |
| 2 |
Core Python |
Core Python programming with an emphasis on object-oriented concepts and data handling. |
Installing Python, data types, OOPs, loops, functions, error handling, file operations, and recursion. |
| 3 |
NumPy |
Working with NumPy for N-dimensional array manipulations and computations. |
Array creation, slicing, binary operators, reshaping, and universal functions (np.sum, np.add). |
| 4 |
Data Munging |
Manipulating datasets using Pandas for cleaning and preparation. |
Loading datasets, handling missing values, aggregations, concatenation, and data transformations. |
| 5 |
EDA |
Exploratory Data Analysis (EDA) techniques for uncovering data insights. |
Data visualization, outlier detection, correlation, and univariate/bivariate analysis. |
| 6 |
Matplotlib |
Visualization techniques using Matplotlib for effective data representation. |
Scatter plots, histograms, bar charts, box plots, pie charts, and heatmaps. |
Part B: Test of Application
| Total Questions (Case Studies) |
Total Marks |
Duration (Minutes) |
| 9 |
40 |
40 |
| Case No. |
No. of Questions |
Total Marks |
Module Coverage |
Weightage |
| 1 |
3 |
12 |
Basics of Statistics, NumPy, and Data Munging using Pandas |
30% |
| 2 |
3 |
12 |
Exploratory Data Analysis using Pandas, NumPy, and Matplotlib |
30% |
| 3 |
3 |
12 |
Core Python programming and application of object-oriented principles |
40% |
Syllabus Part B: Test of Application
| Sr. No. |
Module |
Coverage |
Skills Assessed |
| 1 |
Statistics Basics |
Application of statistics concepts using Python libraries like NumPy and Pandas. |
Statistical inference, sampling, and descriptive statistics applied to data science problems. |
| 2 |
NumPy and Pandas |
Data manipulation and computation with NumPy and Pandas for real-world datasets. |
Array operations, dataset handling, and cleaning. |
| 3 |
EDA and Visualization |
Conducting EDA and data visualization using Matplotlib. |
Univariate and bivariate analysis, visualizations, and outlier detection. |
| 4 |
Core Python |
Applying Core Python programming concepts to solve real-life problems using OOPs principles. |
Logic building, recursion, file handling, and error handling in Python programs. |
Skills Assessed
The Python Developer course evaluates participants on the following key skills:
-
Core Python Programming
- Ability to write clean, efficient Python code using basic and advanced programming concepts, including functions, classes, and error handling.
-
Data Analysis and Munging
- Expertise in cleaning, transforming, and analyzing data using libraries like Pandas and NumPy.
-
Exploratory Data Analysis (EDA)
- Ability to perform in-depth data analysis using visualization and statistical methods to extract meaningful insights.
-
Visualization Techniques
- Proficiency in creating various types of plots, including bar charts, scatter plots, histograms, and pie charts using Matplotlib.
-
Object-Oriented Programming (OOP)
- Strong understanding and application of object-oriented design principles in real-life software development problems.
-
Web Development
- Skills in developing dynamic web applications using Python frameworks like Flask and Django, including handling requests, responses, and database integration.
By completing the TCS NPT Python Developer course, participants will gain the technical expertise needed to become proficient Python developers capable of solving complex problems and delivering high-quality applications.
3 months
11 modules
