Your comprehensive resource for building expertise in data analytics.
Select a dataset or project from Data Science Hive Projects or any public dataset of interest. Ensure the dataset is meaningful for a specific domain or business problem, such as sales trends, customer satisfaction, or operational efficiency.
Get familiar with the dataset. Identify trends, outliers, and patterns. Use Excel to calculate descriptive statistics (mean, median, standard deviation). Create basic visualizations (e.g., bar charts, histograms, scatterplots) to summarize key findings.
Clean and prepare the dataset for analysis. Handle missing values, duplicates, and inconsistent formatting. Use Python (Pandas, NumPy) or R (tidyverse) for tasks such as filtering, merging datasets, and creating derived variables.
Define a business question based on the EDA insights and answer it using inferential statistics. Choose an appropriate statistical test (e.g., t-test, chi-square, ANOVA) and explain why it’s suitable. Perform the test in Python or R and interpret the results to draw meaningful conclusions. Write a brief explanation of the question, method, and findings.
Create a professional, interactive dashboard summarizing your analysis and key findings. Include visualizations of key metrics and insights from the analysis. Highlight the results of the inferential statistics and business question in a compelling way.
Showcase your project in a professional format. Include a concise project summary, key steps taken in data cleaning, EDA, and statistical analysis, and dashboard screenshots or links. Host your portfolio on a personal website (e.g., GitHub Pages, Notion, or Data Science Hive) or a professional platform (e.g., Tableau Public).
Once completed, share your project with the community on Data Science Hive Discord. Engage with peers to receive feedback, discuss challenges, and showcase your portfolio!