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What types of data presentation projects should you include in your portfolio?

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As a data scientist, you need to showcase your skills and experience in a way that attracts potential employers or clients. One of the best ways to do that is to create a portfolio of data presentation projects that demonstrate your ability to communicate insights and recommendations from data analysis. But what types of data presentation projects should you include in your portfolio? Here are some suggestions based on the purpose, audience, and format of your data presentation.

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1 Exploratory data analysis

Exploratory data analysis (EDA) is the process of examining and summarizing data sets using descriptive statistics and visualizations. EDA helps you understand the characteristics, distribution, and relationships of the data, as well as identify any outliers, errors, or patterns. EDA is often the first step in any data analysis project, and it can provide valuable insights for further research or modeling. Including EDA projects in your portfolio can show your ability to work with different types of data, apply appropriate methods and tools, and generate relevant questions and hypotheses.

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Ajay Tiwari

Educator | Data Scientists | ML Expert | Python Developer
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Interactive Visualizations: Utilize libraries like D3.js or Plotly to create interactive charts, maps, or dashboards. Storytelling through Data: Present insights effectively using narratives in articles or blog posts, explaining complex findings in a compelling manner. Data Analysis Reports: Display proficiency in data analysis by showcasing reports with clear methodologies, visual representations, and actionable conclusions. Machine Learning Projects: Highlight predictive models or classification projects with visualizations illustrating model performance. Real-world Applications: Demonstrate applications in domains like finance, healthcare, or social sciences to exhibit practical data-driven solutions.

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2 Predictive modeling

Predictive modeling is the process of creating and testing models that can predict future outcomes or behaviors based on historical or current data. Predictive modeling can be used for various purposes, such as forecasting, classification, regression, or recommendation. Predictive modeling requires you to use various techniques, such as machine learning, statistics, or optimization, to select, train, and evaluate the best model for your data and problem. Including predictive modeling projects in your portfolio can show your ability to apply data science methods and algorithms, measure and improve model performance, and interpret and explain model results.

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Ajay Tiwari

Educator | Data Scientists | ML Expert | Python Developer
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Predictive modeling involves using historical data to build mathematical models that predict future outcomes. It encompasses steps such as data preprocessing, feature selection, model selection (e.g., regression, decision trees, neural networks), and model evaluation using metrics like accuracy or F1 score. These models are trained on known data to make predictions on new, unseen data. Iterative refinement and validation ensure optimal performance, allowing businesses to forecast trends, make informed decisions, and anticipate future scenarios in fields such as finance, healthcare, and marketing.

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3 Data storytelling

Data storytelling is the process of transforming data into a narrative that conveys a clear and compelling message to a specific audience. Data storytelling can be used for various purposes, such as persuasion, education, or entertainment. Data storytelling requires you to use various elements, such as context, structure, visuals, and language, to craft a story that engages and influences your audience. Including data storytelling projects in your portfolio can show your ability to communicate data insights and recommendations, use effective data visualization techniques, and tailor your story to your audience and goal.

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Ajay Tiwari

Educator | Data Scientists | ML Expert | Python Developer
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Data storytelling is the art of conveying insights from data in a narrative format. It merges data analysis with compelling storytelling to communicate complex findings effectively. It involves structuring data into a coherent narrative, using visualizations, infographics, and clear language to engage and inform the audience. By contextualizing data, presenting key findings, and highlighting actionable insights, data storytelling transforms raw information into a compelling story. This approach bridges the gap between data analysis and decision-making, empowering stakeholders to grasp the significance of data-driven insights and facilitating informed actions across various domains like business, journalism, and academia.

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4 Interactive dashboards

Interactive dashboards are web-based applications that allow users to interact with data and explore different scenarios or options. Interactive dashboards can be used for various purposes, such as monitoring, reporting, or decision making. Interactive dashboards require you to use various technologies, such as HTML, CSS, JavaScript, or frameworks, to create a user interface that displays data in an intuitive and interactive way. Including interactive dashboards in your portfolio can show your ability to design and develop data-driven web applications, use different data sources and formats, and create a user-friendly and responsive experience.

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5 Data products

Data products are software applications that use data as a core component to provide value to users or customers. Data products can be used for various purposes, such as personalization, optimization, or automation. Data products require you to use various skills, such as engineering, design, or business, to create a solution that solves a real-world problem or need. Including data products in your portfolio can show your ability to build and deploy data-driven solutions, use different data science frameworks and tools, and understand and meet user or customer expectations.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What types of data presentation projects should you include in your portfolio?

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6

1 Exploratory data analysis

2 Predictive modeling

3 Data storytelling

4 Interactive dashboards

5 Data products

6 Here’s what else to consider

Data Science

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What types of data presentation projects should you include in your portfolio?

1

2

3

4

5

6

1 Exploratory data analysis

2 Predictive modeling

3 Data storytelling

4 Interactive dashboards

5 Data products

6 Here’s what else to consider

Data Science

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