Python for Data Science
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Python for Data Science Syllabus — Unit-wise Content
The official GTU curriculum for Python for Data Science (subject code 3144601) as prescribed for CS-DS · Semester 4 divides the course into 8 units, covering 70 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 (3144601)Unit 1 Basics of Python
- Variables and Datatypes in Python
- Operators: Arithmetic
- identity and membership
- Strings: Slicing
- String operations
- List: List comprehension
- List operations
- Operations of Tuple
- Set and Dictionary
Unit 2 Decision Making and Looping
- if statement
- if...else statement
- elif statement
- while loop
- for loop
- Nested Loop
- break statement
- continue statement
- pass statement
- Use for and while loops along with useful built-in functions to iterate over and manipulate different data types
Unit 3 Functions and Modules
- Creating User Defined Function
- Parameterized function
- Default argument
- Keyword argument
- Variable length argument
- Returning value from function
- global keyword
- lambda function, Map, Filter and Reduce in Functional programming
- Module and Package: import statement
- dir() function
Unit 4 Exception Handling and File Management
- Try, except
- raise keyword
- Handling multiple exceptions
- Creating user defined exception
- File Management: File
- writing and reading file
- File handling functions Subject Code : 3144601 Subject Name : Python for Data Science
Unit 5 Python Libraries for Data Science
- NumPy: NumPy Array
- Aggregations functions
- Computation on Arrays
- Masks, and Boolean Logic
- Fancy Indexing
- Sorting Arrays
- NumPy's Structured Arrays Data Manipulation with Pandas: Pandas Objects
- Data Indexing and Selection
- Operating on Data in Pandas
- Handling Missing Data
- Hierarchical Indexing
- Combining Datasets: Concat
- Merge and Join
- Aggregation and Grouping Visualization with Matplotlib: 2D and 3D plots
- Geographic Data with Basemap
- Visualization with Seaborn
Unit 6 Data Wrangling
- Validating Data
- Manipulating Categorical Variables
- Dealing with Missing Data
- Slicing and Dicing: Filtering and Selecting Data
- Concatenating and Transforming
- Aggregating Data at Any Level
Unit 7 Exploring Data Analysis
- EDA approach
- Defining Descriptive Statistics for Numeric Data
- Counting for Categorical Data Creating Applied Visualization for EDA, Understanding Correlation
- Modifying Data Distributions Reducing Dimensionality : Understanding SVD, Factor Analysis and PCA, Applications of dimensionality reduction
Unit 8 Outliers Detection in Data
- Understanding an outlier
- Examining a Simple Univariate Method
- Developing a Multivariate Approach Learning from Data: Linear Regression
- Logistic Regression
- Naïve Bayes
- Learning Lazily with Nearest Neighbors
- cross validation
- selection and optimization
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