Assignment 1

1. Basic Programs using Python

2.  Python Data Structures

3. Introduction to Data Manipulation Libraries

4. Data Visualization with Matplotlib

5. Data Analysis Project:

Assignment 2: Practical Assignments on Data Acquisition and Cleaning

Objective:

1. To develop skills in data acquisition from various sources, web scraping, API integration, and

2. data cleaning and preprocessing.

      Programs:

2.1 Data Importing from Various Sources

1. Importing Data from CSV:

• Download a CSV file from the internet (e.g., a public dataset from Kaggle).

• Use Python to import the CSV file into a DataFrame.

• Display the first few rows of the DataFrame.

   2. Importing Data from JSON:

• Download a JSON file from the internet.

• Use Python to import the JSON file into a DataFrame.

• Display the first few rows of the DataFrame.

                        3. Importing Data from SQL Databases:

• Set up a local SQLite database and create a table with some sample data.

• Use Python to import data from the SQL table into a DataFrame.

• Display the first few rows of the DataFrame.

              2.2 Data Scraping Techniques

1. Web Scraping with BeautifulSoup:

• Identify a website with tabular data (e.g., a Wikipedia table).

• Use BeautifulSoup to scrape the data and store it in a DataFrame.

• Display the first few rows of the DataFrame.

2. API Integration:

• Identify a public API (e.g., OpenWeatherMap, CoinGecko).

• Use Python to fetch data from the API and store it in a DataFrame.

• Display the first few rows of the DataFrame.

Example1: 

Example 2:

Example 3:

              2.3 Data Cleaning and Preprocessing

1. Handling Missing Values:

• Create a DataFrame with some missing values.

• Use Python to handle missing values by dropping rows,

• filling with a specific value, and filling with the mean of the column.

• Display the DataFrame after each operation.

  • • • Example 1:

      • Example 2:

      • Example 3:

2. Handling Outliers:

• Create a DataFrame with some outliers.

• Use Python to identify outliers using the Z-score method.

• Remove the outliers and display the cleaned DataFrame.

Assignment 3: Practical Assignments on Data Visualization and Exploratory  Data Analysis (EDA)

Objective:

1. To develop skills in data visualization using libraries like Matplotlib and Seaborn and to perform Exploratory Data Analysis (EDA) using summary statistics, data distribution analysis, and correlation analysis.

Assignment 4: Practical Assignments on Statistical / Algorithmic Data Modeling

Objective:

• To develop skills in statistical data modeling, hypothesis testing, classification and regression algorithms, model evaluation techniques, and hands-on exercises with the scikit-learn library.

4.1: Hypothesis Testing and Probability Distributions

1. Hypothesis Testing:

o Perform a hypothesis test to determine if the mean of a sample differs significantly from a known population mean

2. Probability Distributions:

o Visualize the probability distribution of a dataset using histograms and probability density functions (PDFs).

4.2: Basics of Classification and Regression Algorithms

1. Classification Algorithm (Logistic Regression):

o Implement a logistic regression model using scikit-learn to classify the Iris dataset.

2. Regression Algorithm (Linear Regression):

o Implement a linear regression model using scikit-learn to predict house prices.

4.3: Model Evaluation Techniques

1. Performance Metrics for Classification:

o Evaluate a classification model using confusion matrix, precision, recall, and F1-score.

2. Performance Metrics for Regression:

o Evaluate a regression model using mean squared error, mean absolute error, and R-squared.

4.4: Hands-on Exercises with scikit-learn Library

1. Implement a Decision Tree Classifier:

o Train and evaluate a Decision Tree Classifier on the Iris dataset.

2. Implement a Random Forest Regressor:

o Train and evaluate a Random Forest Regressor on the Boston Housing dataset.

Assignment no. 5. Mini-project 

The objective of this mini project is to provide hands-on experience with the full data science workflow, encompassing data collection, preprocessing, exploratory data analysis (EDA), modeling, evaluation, and deployment. By the end of this project, participants will have developed a comprehensive understanding of how to apply data science techniques to solve a real-world problem and present their findings effectively.