This repository contains the tasks and projects completed during the Afronex Data Science Internship. The internship is divided into three levels, each focusing on a different aspect of data science.
In this level, we focus on visualizing a small dataset using libraries like Matplotlib or Seaborn. The goal is to create simple visualizations such as bar charts, pie charts, or line plots and explore the dataset visually to identify any patterns or trends.
This level involves working with a small dataset with numerical variables. We calculate basic summary statistics such as mean, median, mode, minimum, and maximum values for each variable using Python libraries like Pandas or NumPy. The interpretation of these statistics helps us understand the central tendency, variability, and distribution of the data.
In the final level, we work with a small dataset that contains some missing values or inconsistencies. The tasks involve basic data cleaning such as handling missing values, removing duplicates, or correcting data types. After cleaning, we validate the process by checking for missing values or inconsistencies, ensuring that the dataset is ready for further analysis or visualization.