Learning Outcomes from the Course Project:

• Data cleaning: We learnt how to import, manipulate, and clean data sets for analysis. This involved identifying and handling missing values, outliers, and inconsistencies.

• Data exploration techniques: We gained experience with using statistical summaries and data visualization tools to uncover patterns and relationships within the data.

• Data storytelling: We were able to develop the ability to communicate insights discovered through EDA in a clear and concise way.

Learning Outcomes from Laboratory Sessions:

• Hands-on experience: The Labs provided us a platform to apply theoretical concepts from lectures to real-world data sets. This reinforced our understanding of EDA techniques.

• Software proficiency: Labs typically involved using specific software tools like Python libraries (pandas, NumPy, Matplotlib, Seaborn) for data analysis. We gained practical skills in using these tools.

• Problem-solving: Labs often involved working on specific data-driven problems. This helped us develop our critical thinking and problem-solving skills in a data analysis context.

• Troubleshooting: Labs were a safe space to encounter and troubleshoot common issues that arose during data analysis. This built our confidence and resilience in working with data.

Exploratory Data Analysis (EDA) is a crucial step in understanding and deriving insights from data.

We started by searching and analyzing various datasets from various sources. After which we were able to cleanse the data by handling missing values, outliers, and inconsistencies and transformed and preprocessed the features as needed. We then computed summary statistics (mean, median, etc.) to understand the central tendency and variability of the data. After which we explored individual variables to identify patterns, distributions, and potential outliers. We then investigated relationships between different variables and derived our hypothesis along with the help from correlation matrix. We were able able to visualize our hypothesis with the help of python and derived inferences from them which were helpful us to know the how different factors affect the risk of diabetes. EDA helped us to uncover stories within datasets, fuel innovation and make a meaningful impact.