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How to do data analytics in notebooks

By Nick Barth

Updated on March 6, 2024

Data analytics is a powerful skill set that allows you to extract insights and knowledge from your data. Modern tools like Jupyter notebooks and Deepnote have become invaluable assets for Data Scientists and Analysts to perform these analyses efficiently and collaboratively. In this guide, we'll cover the essential steps in data analytics within notebooks, focusing on key areas like data visualization, statistical analysis, and machine learning.

  1. Setting up your notebook environment

Before diving into analytics, it's important to set up your notebook environment. Here are some initial steps:

  • Choosing the right notebook platform: Jupyter is widely used for Python-based data analytics. However, platforms like Deepnote offer real-time collaboration features.
  • Installation: If using Jupyter, ensure you have it installed on your system or use a cloud-based service like Google Colab.
  • Dependencies: Install necessary Python libraries such as pandas for data manipulation, matplotlib/seaborn for visualization, scikit-learn for machine learning, and others that you might need for your analysis.

2. Importing and preparing data

Good analysis starts with well-prepared data. Here are the main tasks you'll need to perform:

  • Data import: Load your datasets into the notebook using pandas or another suitable library.
  • Data cleaning: Perform necessary steps to clean your data, including handling missing values, outliers, and errors.
  • Data transformation: Structure your data correctly for analysis by normalizing, scaling, or encoding it appropriately.

3. Data visualization

Data visualization is critical for understanding the underlying patterns and trends in the data.

  • Exploratory data analysis (EDA): Use histograms, box plots, and scatter plots to understand distributions and relationships.
  • Advanced visualization: Create more complex visualizations like heatmaps, pair plots, or time-series graphs as needed.
  • Interactive visualization: Libraries like plotly or bokeh allow for interactive plots that can be very insightful.

4. Statistical analysis

Statistical analysis can provide a rigorous foundation for your insights.

  • Descriptive statistics: Summarize your data using means, medians, modes, ranges, and standard deviations.
  • Inferential statistics: Perform hypothesis testing or regression analysis to understand relationships or make predictions.

5. Machine learning

Use machine learning to predict outcomes or uncover patterns.

  • Model selection: Choose an appropriate model for your problem, such as linear regression for continuous data or decision trees for classification.
  • Training: Split your data into training and test sets, and train your model using the training data.
  • Evaluation: Measure your model's performance using appropriate metrics such as accuracy, precision, recall, F1 score, or mean squared error.

6. Collaborating and sharing insights

  • Collaboration: Use tools like Deepnote to collaborate with other data scientists and share your analysis in real time.
  • Reporting: Summarize your findings, visualizations, and models in a report or dashboard format to communicate your insights to stakeholders.


As the data comes to life in our analysis, let's document key insights...

Remember, every dataset and project is unique, so these steps might differ slightly based on your needs. Ensure you document your process clearly in your notebook for reproducibility and communication purposes. Happy Analyzing!

Nick Barth

Product Engineer

Nick has been interested in data science ever since he recorded all his poops in spreadsheet, and found that on average, he pooped 1.41 times per day. When he isn't coding, or writing content, he spends his time enjoying various leisurely pursuits.

Follow Nick on LinkedIn and GitHub

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