Unit 1 Introduction, Data Types and Operators

10 lecture hours

• Features & History of Python
• Installation and working with Python
• Variables and data types in python
• perform computations and create logical statements using Python’s operators: Arithmetic
• Bitwise operators
• tuple, set, dictionary and string operations
• Python Decision making
• Loops & Functions: Conditional statements using If statement
• if ...else statement
• elif statement and Boolean expressions
• while loop
• for loop
• Nested Loop
• defining custom functions
• Organizing Python codes using functions
• Create and reference variables using the appropriate scope
• Modules - create own and use basic inbuilt modules like random

Unit 2 Python File Handling and exception handling

8 lecture hours

• An introduction to file I/O
• use text files
• use CSV files
• use binary files
• Exception handling in python
• Python MySql basic operations

Unit 3 Data Science and Descriptive Statistics

6 lecture hours

• Understanding data
• its type and data science
• Considering the emergence and fundamentals of data science
• Data science life cycle
• Data analysis (Univariate
• bivariate and Multivariate)
• Data analytics
• Data measurement scale
• Data descriptive statistics (Measures of central tendency
• measure of location
• Shape and symmetry)
• Understanding Python's role in data science

Unit 4 Exploring Python libraries

6 lecture hours

• Exploring statistics of data using stats package from SciPy
• Numerical computing and working with multidimensional array through NumPy
• Data manipulation through Pandas
• Basic functionalities of machine learning using Scikit-learn
• Extracting components from HTML document through beautiful soup

Unit 5 Probabilistic and Inferential Statistics

5 lecture hours

• Random variable
• basics of probability
• Probability mass function
• Probability density function
• Cumulative distribution function
• Discrete probability distribution (Binomial and Poisson)
• Continuous probability distributions (Normal and Exponential)
• Sampling and its various techniques
• point estimation and interval estimation
• Standard normal distribution
• Central limit theorem
• Hypothesis testing through one sample test

Unit 6 Data preparation

• Data loading
• Data cleaning
• dealing with missing data
• removing duplicates
• Slicing and Dicing
• Filtering, and selecting data
• Concatenating and transforming
• adding new cases and variables
• removing data
• Sorting and shuffling
• Aggregating data
• Handling outliers
• Data wrangling
• Data Normalization 5 Program Name: Bachelor of Engineering Level: UG Subject Code: BE05000231 Subject Name: Python for Data Science

Unit 7 Data Visualization

5 lecture hours

• Visualizing data through figure
• subplot and its properties
• plots in matplotlib
• basics of advanced visualizations with Seaborn