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Option 1: Running using free online resources (1-click, recommended)

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You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer. This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can do using the "Edit > Delete Cells" menu option. NOTE: Remove this cell containing the instructions before making your submission. Understanding the Gender Divide in Data Science Roles by Aakanksha N SĢ020 Stack Overflow Developer Survey Results WhatsApp Chat Data Analysis by Prajwal Prashanth Check out the Jovian.ml Medium publication for inspiration: Īnalyzing StackOverflow Developer Survey ResultsĪnalyzing your browser history using Pandas & Seaborn by Kartik Godawat.Copy over the explanations from your Jupyter notebook into your blog post, and embed code cells & outputs.Sign up on to write a blog post for your project.A blog post is a great way to present and showcase your work.Browse through projects shared by other participants and give feedback.Upload your notebook to your Jovian.ml profile using mit.Step 6: Make a submission & share your work Share links to resources you found useful during your analysis.Share ideas for future work on the same topic using other relevant datasets.Include interesting insights and graphs from previous sections.Write a summary of what you've learned from the analysis.Step 5: Summarize your inferences & write a conclusion Wherever you're using a library function from Pandas/Numpy/Matplotlib etc.Create new columns, merge multiple dataset and perform grouping/aggregation wherever necessary.Answer the questions either by computing the results using Numpy/Pandas or by plotting graphs using Matplotlib/Seaborn.Ask at least 4 interesting questions about your dataset.Step 4: Ask & answer questions about the data Make a note of interesting insights from the exploratory analysis.Explore relationship between columns using scatter plots, bar charts etc.Explore distributions of numeric columns using histograms etc.Compute the mean, sum, range and other interesting statistics for numeric columns.Step 3: Perform exploratory analysis & visualization Perform any additional steps (parsing dates, creating additional columns, merging multiple dataset etc.).Handle missing, incorrect and invalid data.Explore the number of rows & columns, ranges of values etc.Load the dataset into a data frame using Pandas.You can find a list of recommended datasets here: Step 2: Perform data preparation & cleaning Here's some sample code for downloading the US Elections Dataset: import opendatasets as od

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Download the dataset using the opendatasets Python library.The data should be in CSV format, and should contain at least 3 columns and 150 rows.Find an interesting dataset on this page:.copy-pasted for somewhere else.įollow this step-by-step guide to work on your project. Your work must not be plagiarized i.e.Your submission must include explanations using markdown cells, apart from the code.

Your submission must include at least 4 visualizations (graphs).

You must ask and answer at least 4 questions about the dataset.

Dataset must contain at least 3 columns and 150 rows of data.

Your submission will be evaluated using the following criteria: Focus on documentation and presentation - this Jupyter notebook will also serve as a project report, so make sure to include detailed explanations wherever possible using Markdown cells. Use this starter notebook as an outline for your project. You will pick a real-world dataset of your choice and apply the concepts learned in this course to perform exploratory data analysis. This is the starter notebook for the course project for Data Analysis with Python: Zero to Pandas.

Data Analysis with Python: Zero to Pandas - Course Project Guidelines (remove this cell before submission)