A complete guide to core Data Science concepts and tools using Python.
- 👨💼Employee_Management_System(EMS)
- 🎲Basic_Probability_Simulations_Using_Python
- 📈Fundamentals_of_Statistics_in_DataScience
- 🧮NumPy_Assignment
- 🧾Pandas_Assignment
- 📊Matplotlib_Assignment
- 📐Matrix_Vector_basics_DataScience
📘 Explanation:
A beginner-friendly Python project designed to manage employee records using core programming concepts such as control structures, functions, and object-oriented programming. It supports essential operations like adding, viewing, and searching employees through a menu-driven interface. Employee data is stored in a dictionary with unique employee IDs, demonstrating basic CRUD operations and structured code organization.
📎 Google Drive Link: https://drive.google.com/file/d/12g_1JOIxVhVCbuvp8eLnxhvcu2EbKa_T/view?usp=sharing
🎯 Explanation:
A beginner-focused Python project to understand and simulate basic probability concepts like coin tosses, dice rolls, conditional probability, Bayes' Theorem, random variables, and the Central Limit Theorem. The project includes both discrete and continuous probability simulations using libraries like random, numpy, and matplotlib. Ideal for students exploring how statistical theories behave in practice!
📎 Google Drive Link: https://drive.google.com/file/d/1oEgL17WTzxlk87LfD2DErOaXiGkpHCKS/view?usp=sharing
📊 Explanation:
A beginner-level project focused on exploring fundamental statistical concepts essential for data science. It includes practical implementations of central tendency, dispersion, hypothesis testing, errors, regression types, and correlation analysis using Python. Libraries like numpy, scipy, matplotlib, seaborn, and sklearn are used to simulate, analyze, and visualize statistical techniques in real-world datasets.
📎 Google Drive Link: https://drive.google.com/file/d/1HxMV486s12I92D9GFNGT6D3F-JU1u6SX/view?usp=sharing
🛠️ Explanation:
A hands-on assignment series to understand the core tools in Python for Data Science. This folder includes three subcategories: NumPy, Pandas, and Matplotlib — each focused on essential operations in data manipulation, analysis, and visualization.
🧮 NumPy_Assignment
Objective: Learn array creation, manipulation, and basic statistical operations using NumPy.
Key Tasks:
- 1D and 2D array operations (sum, mean, std, transpose, slicing).
- Element-wise and matrix operations (addition, multiplication, dot product).
🧾 Pandas_Assignment
Objective: Explore dataframes, group operations, filtering, and saving data using Pandas.
Key Tasks:
- Creating DataFrames from dictionaries.
- Filtering by conditions, calculating aggregates.
- Adding columns, grouping, sorting, and exporting to CSV.
📊 Matplotlib_Assignment
Objective: Practice data visualization through line plots, bar charts, pie charts, and histograms.
Key Tasks:
- Line plot customization.
- Bar chart for student scores.
- Pie chart with region-wise revenue.
- Histogram for random data distribution.
📎 Google Drive Link: https://drive.google.com/file/d/1btQSUTjfesr_Zpdu2H0VolqNbBZmB03T/view?usp=sharing
📐 Objective : Explore foundational matrix and vector operations using NumPy, and apply them to practical data science problems like solving linear equations, checking invertibility, calculating eigenvalues, and performing PCA.
Topics Covered
Matrix-vector multiplication, trace, eigenvalues/eigenvectors
Determinant, singularity check, inverse of a matrix
Solving systems of linear equations
Submatrix extraction, Frobenius norm, rank
PCA (Principal Component Analysis) with covariance matrix
📎 Google Drive Link: https://drive.google.com/file/d/1oBd5pSX78se9JPSr0qUzXOA6dkYy-xEF/view?usp=sharing