Data journalist portfolio
Turning data into clear analytical research, interactive reports and visual stories.
Analytics and storytelling
Periodic table of AI startups – 14 company categories
Classification of 305 AI startups that raised funding between February 2025 and February 2026 by funding, count, annual growth, momentum trend, and ecosystem.
|Web scraping| • |Python| • |Tableau|
|Web scraping| • |Python| • |Tableau|
Funding and outcomes analysis of 100,000 startups
A study of model data for predicting IPO, acquisition, or shutdown outcomes. The approach and data structure used are identifying the true drivers of startup success and failure.
|Python| • |SQL| • |Tableau|
|Python| • |SQL| • |Tableau|
25-year risk-return analysis of investment portfolios
Risk and return are directly related: the higher an asset's potential profit, the higher the probability of financial loss. Safe instruments deliver minimal returns.
|OSINT| • |Python| • |Tableau|
|OSINT| • |Python| • |Tableau|
Who bought U.S. oil & products in 2025
A detailed analysis of U.S. Energy Information Administration data on U.S. exports of crude oil and petroleum products, broken down by country and region.
|Claude| • |Python| • |Tableau|
|Claude| • |Python| • |Tableau|
Housing bubble risk in major cities around the world
A 'bubble' is a substantial overvaluation of an asset, which can only be confirmed in hindsight. For asset managers and retail investors, such rankings serve as a warning sign.
|OSINT| • |CSV| • |Tableau|
|OSINT| • |CSV| • |Tableau|
Investment portfolio #1 - best US companies
An opportunity to buy stocks of IT, biotech, technology, and other fast-growing US companies. Limited risks with a high probability of achieving return targets.
|Google Sheets| • |JavaScript| • |Everviz|
|Google Sheets| • |JavaScript| • |Everviz|
Data visualization
Project Details & Insights
Goal:
- Visualize the results of the Barron's Spring 2026 Big Money Poll of U.S. professional investors to analyze their market sentiment and investment preferences
- Analyze the sentiment of portfolio managers and investment strategists toward U.S. and global equity markets
- Show the distribution of investment preferences across sectors, asset classes, and international markets
- Present investor forecasts for key economic metrics: inflation, Fed rate, Treasury yields, and GDP growth
- Collecting and preprocessing survey data from 105 professional asset managers
- Developing visualizations in Tableau
- Creating informational annotations, interactive highlights, and tooltips
- Publishing and presenting the dashboard ⧉
- Interactive Tableau dashboard with four thematic tabs: Markets, Assets, Equity, Economy
- Navigation system for switching between a global market overview and detailed analysis of individual asset classes and sectors
- Recorded a rise in optimism among professional investors: the share of bulls increased from 47% to 54%
- Identified the most attractive sectors: Energy (24%), IT (17%), and Industrials (15%)
- Determined asset class leaders: the strongest bullish sentiment was recorded for Commodities (75%) and non-U.S. equities (66%)
- The tool enables investors and analysts to quickly assess shifts in market sentiment and adjust investment strategies accordingly
Project Details & Insights
Goal:
- Visualize global shifts in U.S. crude oil and petroleum exports in 2025, highlighting key changes in trade partners and regional market shares
- Analyze import volume dynamics across the world’s major economies
- Clearly illustrate the redistribution of energy flows (e.g., the rise of India and the Netherlands against China’s declining share)
- Provide a tool for Year-over-Year (2024 vs. 2025) comparison by country and region
- Data preprocessing and energy shipment analysis using Python
- Generating special dataset with Claude
- Developing spatial visualizations using Map Layers in Tableau
- Configuring dynamic comparison elements to track market trends and growth rates
- Designing the UI in a Visual Capitalist-style infographic format for maximum readability
- Publishing and presenting the dashboard ⧉
- An interactive Tableau dashboard combining geographic mapping with detailed growth/decline charts
- A navigation system allowing users to toggle between global overviews and deep dives into specific markets (Netherlands, Mexico, China, etc.)
- Identified the Netherlands (Port of Rotterdam) as Europe's primary energy hub in 2025 (419M barrels)
- Captured a sharp 25% decrease in China’s reliance on U.S. oil, contrasted by a 35% surge in Indian imports.
- Highlighted Canada’s infrastructure-driven dependence on U.S. imports despite its own vast reserves
- Provided a high-level tool for investors and analysts to quickly assess geopolitical risks in the energy sector
Project Details & Insights
Goal:
- To identify the factors that most significantly influence the success or failure of startups
- Detect nonlinear relationships between numerical and categorical metrics that standard Pearson correlation fails to capture
- Provide users with a tool for conducting their own in-depth analysis of the distribution of results
- Calculating the Phik correlation matrix in Python to uncover nonlinear relationships
- Designing a PostgreSQL → Tableau data model
- Configuring a filtering and navigation panel using Dynamic Zone Visibility
- Creating explanatory notes, interactive highlights, and tooltips
- Publishing and presenting the dashboard ⧉
- Interactive dashboard in Tableau, integrated with PostgreSQL
- Navigation system for data exploration and drill-down into any metric
- Quantified venture risks through dependency analysis
- Identified critical metrics for strategy optimization
- Uncovered underperforming projects based on early indicators
- Projected a 20-30% increase in venture portfolio ROI
Project Details & Insights
Goal:
- To visualize the AI startup landscape and identify investment trends across 14 market categories for the period from February 2025 to February 2026
- Present 305 startups in a structured format while preserving 5 key parameters for each category
- Enable users to independently compare categories by funding volume, growth dynamics, and market trend
- Provide quick access to specific companies and market facts through interactive tooltips
- Adapting the Veridion external classification as a data source
- Designing a visual layout in periodic table format
- Placing parameters using Map Layers: investment ranking, market trend, annual growth, funding volume, number of companies
- Configuring tooltips with startup examples and analytical facts for each category
- Publishing and presenting the dashboard ⧉
- Interactive Tableau dashboard with a periodic table layout
- Navigation across five parameters through visual element placement
- Tooltips and bar charts as a second level of detail
- Visualized 14 AI market categories on a single screen
- Identified growth leaders: AI for Developers (+320%), AI Agents (+260%), and Vertical SaaS (+250%)
- Captured the market shift from general-purpose AI to sector-specific solutions
- Delivered a ready-to-use tool for initial screening of AI investment trends
Project Details & Insights
Goal:
- To visualize the risk-return relationship of asset classes and model investment portfolios based on 25 years of historical data (2001–2025)
- Illustrate the behavior of individual assets and diversified portfolios on the risk-return scatter plot
- Enable users to evaluate annual returns for any portfolio or asset over a selected period
- Provide filters by analysis period and asset classes
- Implement a "Quadrant View" mode to categorize portfolios based on target return and risk tolerance
- Collecting and calculating 25 years of historical data on asset returns in USD
- Creating a scatter plot with three types of objects: diamonds (asset classes), lines (portfolios of two assets), and grey dots (portfolios of three assets in 5% increments)
- Developing tooltips showing annual returns for each point and diamond
- Integrating filters by analysis period and asset classes
- Configuring Dynamic Zone Visibility for "Quadrant View" mode: when enabled, additional filters for target return and risk tolerance appear
- Publishing and presenting the dashboard ⧉
- An interactive Tableau dashboard based on a scatter plot with three levels of portfolio detail
- Visual tooltips showing annual returns as a second level of analysis for any point
- "Quadrant View" mode with dynamically appearing filters to assess portfolio relevance based on the investor's specified parameters
- Empirically validated the fundamental risk-return tradeoff using 25 years of real-world data
- Demonstrated the diversification effect, visually showing how two- and three-asset portfolios shift toward an optimized risk-return profile
- Built a dedicated tool for selecting model portfolios tailored to individual investor risk profiles
- Established a foundational framework for client advisory work on asset allocation
Project Details & Insights
Goal:
- To evaluate the effectiveness of ad placement filtering in the Yandex Advertising Network
- Compare the trends in key metrics before and after ad placement filtering
- Visualize the distribution of metrics by device type and ad placement quality
- Set up monitoring of filtering effectiveness with weekly updates
- Preparing and cleaning advertising campaign data from Yandex.Direct
- Designing a dashboard by device type (desktop, mobile, tablet) and ad placement type
- Configuring interactive highlights
- Customizing filters by ad placement, detail, and time period
- Publishing and presenting the dashboard ⧉
- An interactive dashboard in Tableau for monitoring the effectiveness of ad placement filtering by device and platform
- A system of highlights by device type as a tool for quick comparison of segments
- Cross-filters for independent analysis of combinations of platforms and periods
- Validated the necessity of placement filtering, as irrelevant platforms lower ROAS
- Created a dedicated tool for regular ad placement audits
- Established a methodology requiring blocklist moderation at least twice a year
- Recommended allocating 10-15% of the budget to continuous mobile placement testing, given their high ROAS potential
Contacts
Alexander Slobodskoi
Data journalist
Hello!
I turn complex datasets into in-depth analytical research, interactive special projects, and useful info-products.
My focus is on creating data-driven content for media, fintech companies, and consultancies: finding hidden trends in numbers, testing hypotheses, and packaging dry statistics into engaging visual stories. I also translate complex technical language and convoluted reports into a format that's clear for business audiences and the general public.
Key competencies:
- Sources and OSINT: searching for information in open and commercial sources, government registries, agency databases, and auditing methodology to eliminate errors.
- Data collection and analysis: using Python and SQL for automated data collection (web scraping, APIs), cleaning "dirty" datasets, and conducting statistical research.
- Interactive visualization: designing clear charts, interactive maps, and analytical dashboards.
- Data storytelling and distribution: the full cycle of content creation - from formulating a hypothesis and finding a news hook to creating, promoting, and monetizing analytical content.
Currently looking for new opportunities to apply my experience in media projects, corporate research, and content marketing.