Data Science for Engineering
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Follow on Instagram for UpdatesData Science for Engineering Syllabus — Unit-wise Content
The official GTU curriculum for Data Science for Engineering (subject code ME02067041) as prescribed for AI · Semester 2 divides the course into 5 units, covering 35 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME02067041)Unit 1 Introduction of Data Science
- What is data
- What is data science
- Fundamentals of data science
- Data science life cycle
- Why data science is important
- Applications of data science
- Why Python is necessary for data science
Unit 2 Getting Started with Python
- Jupyter / pycharm / spyder or any other python tool setup and installation
- Basics of Python including data types
- control structures using sample dataset
- objects and functions
- Python sequence data structures including String
- Tuple, Set, and Dictionary
- Introduction to various python libraries for data science
Unit 3 Basic Data Processing
- Introduction to Data Frames in Pandas
- learning to access elements with indexes
- Re-indexing in pandas Series and Data frames
- Data preparation
- Numerical Computing with NumPy
- going from Python list to Numpy arrays
- working with multi-dimensional array
- array operations
- Several scientific numerical routines through Scipy
Unit 4 Exploratory data analysis
- Data preprocessing (data loading
- dealing with missing values and outliers
- data wrangling
- filtering data
- Data Normalization
- Data Formatting
- data cleaning)
- Web scraping with beautiful soup
Unit 5 Data Visualization
- Basic visualizations with Matplotlib
- Advanced visualizations with Seaborne
- Plotting images
- graphs and grids of charts
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