By DatalytIQs Academy — Analytics that Inform Progress Introduction Between 2010 and 2024, the global automotive industry experienced rapid technological evolution, policy shifts toward sustainability, and changing consumer preferences.To understand how these factors influenced BMW’s sales performance, the DatalytIQs Academy data analytics team explored a dataset of 50,000 entries drawn from global BMW markets across Asia, North America, the Middle East, and South America…

Financing: About $63B to 20 countries, including ~$9B across 13 low-income countries (LICs). Stock of exposure (as of Apr 30, 2025): GRA (general resources) commitments + credit outstanding ≈ SDR 165B. PRGT (concessional window) ≈ SDR 29B. Capacity development: ~$382M for hands-on technical assistance, training, and peer learning (delivered via HQ and 17 regional centers).

The Pulse of a Planetary System Every planet tells time — not with hours, but with orbits.The orbital period (how long a planet takes to complete one revolution around its star) defines its temperature, atmospheric chemistry, and habitability potential. To see how Kepler’s planets compare, we classified them into four period ranges and plotted their counts. What the Distribution Shows…

From Features to Families of Worlds After mapping Kepler’s sky, analyzing distributions, and ranking feature importances, it’s time to ask a deeper question:Do exoplanets naturally fall into clusters — families defined by their physical and orbital properties? To explore this, we applied Principal Component Analysis (PCA) followed by K-Means clustering (k = 3). PCA reduces the dataset into principal components (PC1, PC2), capturing most of the variance, while K-Means groups objects with similar characteristics.

From Linear to Nonlinear Thinking After exploring feature distributions, class differences, and a logistic regression baseline, it was time to test how a more flexible model performs.The Random Forest Classifier, a non-linear ensemble of decision trees, was trained on the same Kepler dataset to identify which features most influence planetary classification. The figure below shows each feature’s…

The Story Before the Algorithm Before any model can predict, data must speak.The histograms below visualize how planetary and stellar features are distributed across the Kepler dataset — the raw foundation from which our logistic regression classifier learns to distinguish stars, candidates, and exoplanets. Understanding the Four Key Features 1. Planet Radius (top-left) The distribution is highly right-skewed, with most values near zero and a few extreme outliers reaching enormous sizes.

Seeing the Universe Through Statistics In our previous post, we mapped the Kepler sky distribution and explored how logistic regression identifies exoplanet types using machine learning.Now, we dive deeper — looking at how key numerical features differ across planetary labels using boxplots. Each plot compares feature distributions across the four classification labels: 0 → Non-planetary objects 1 → Planet candidates…

(Mollweide Projection — RA vs Dec) 1. Scientific Interpretation This map plots Right Ascension (RA) and Declination (Dec) for thousands of observed celestial bodies from the Kepler mission. The Mollweide projection preserves global spatial relationships, ideal for visualizing how exoplanet candidates are distributed across the sky. Key observations: Dense clusters correspond to regions of concentrated telescope focus — especially near the…

Insights from the Global Earthquake–Tsunami Risk Assessment Dataset Overview The depth of an earthquake’s focus is the point within the Earth where seismic energy is released. It plays a crucial role in determining the intensity of ground shaking, surface damage, and tsunami potential. Using the Global Earthquake–Tsunami Risk Assessment Dataset (2001–2022), this analysis examines how earthquakes are distributed across…

Insights from the Global Earthquake–Tsunami Risk Assessment Dataset Overview Between 2001 and 2022, Earth experienced several mega-earthquakes—events so powerful that they reshaped coastlines, triggered global tsunamis, and altered the planet’s rotation slightly.This analysis, conducted using the Global Earthquake–Tsunami Risk Assessment Dataset, identifies and maps the ten most extreme earthquakes (≥ 8.5 M) recorded worldwide. The visualization highlights where and how these seismic giants occur, emphasizing their link to…

Overview Earthquakes are often assumed to occur randomly, independent events following a Poisson process, much like radioactive decay or raindrops falling on a roof.However, real-world seismic activity can behave very differently due to tectonic interactions, aftershock sequences, and periodic stress release. This analysis, conducted using the Global Earthquake–Tsunami Risk Assessment Dataset (2001–2022), tests whether global earthquake inter-event times follow a…

Overview The Inter-Event Time Analysis measures the interval between successive global earthquakes, expressed in months.Using the Global Earthquake–Tsunami Risk Assessment Dataset (2001 – 2022), this visualization explores how frequently large-magnitude seismic events occur worldwide. The analysis reveals how tightly earthquakes are clustered over time — an important metric for understanding global seismic rhythm and for developing…

Seismic Features, Temporal Trends, and Global Distribution Analysis (2001–2022) Overview The Global Earthquake–Tsunami Risk Assessment Dataset is a comprehensive, machine learning–ready dataset that records 782 significant earthquakes worldwide from 2001 to 2022.Each record includes detailed seismic parameters such as magnitude, depth, intensity, and tsunami potential, making it a valuable resource for tsunami prediction, hazard assessment, and…

Seismic Features, Temporal Trends, and Global Distribution Analysis (2001–2022) Overview The Global Earthquake–Tsunami Risk Assessment Dataset is a comprehensive, machine learning–ready seismic database designed for research in tsunami prediction, earthquake analysis, and hazard assessment.It compiles 782 globally recorded earthquakes (2001–2022), detailing each event’s magnitude, depth, intensity, and tsunami potential. This dataset serves as a vital foundation for predictive analytics, early warning models, and geophysical research at…

Seismic Features, Temporal Trends, and Global Distribution Analysis (2001–2022) Overview The Global Earthquake–Tsunami Risk Assessment Dataset is a comprehensive, machine learning–ready dataset covering 782 significant earthquakes recorded worldwide between 2001 and 2022. It integrates seismic characteristics with tsunami potential indicators — ideal for risk prediction, early warning systems, and geospatial hazard assessment. This research forms part of…

Overview The line graph above illustrates the annual frequency of recorded earthquakes with magnitudes ≥6.5 on the Richter scale over 22 years (2001–2022). Each point represents the total number of events per year, providing insight into temporal variations in global seismic activity. Key Observations Fluctuating Seismic Activity The number of large earthquakes varied annually between 25 and 55 events…

Visual Insight The figure above shows a scatter plot of Magnitude vs Depth of Earthquakes, part of a broader global earthquake–tsunami risk assessment study. Each point represents an earthquake event, plotted by its depth (in km) on the x-axis and magnitude (Richter scale) on the y-axis. From the visualization, we observe that: Most high-magnitude earthquakes (≥7.0) occur at shallower depths (≤100 km)

Background After more than a decade of ultra-low rates, the world’s major economies have entered a new interest-rate regime.Between 2015 and 2021, most advanced economies saw negative real yields—that is, nominal interest rates minus inflation.But by 2023–2025, the cycle turned: inflation cooled while nominal rates stayed high, producing the first sustained period of positive real returns…

Understanding the Primary Balance The primary balance measures the difference between a government’s revenues and non-interest expenditures — expressed as a percentage of GDP.It indicates whether a country’s fiscal policy is expansionary (deficit) or consolidating (surplus). The chart below tracks fiscal positions for selected economies across four periods: 2015–19 (blue): Pre-COVID baseline years 2020–21 (red): Pandemic-related fiscal stimulus 2025 (projected, yellow):

Background Japan, the world’s third-largest economy and a leading automobile exporter, has long relied on stable trade relations with both North America and the rest of the world.Yet, 2025 has brought renewed trade frictions, currency volatility, and shifting demand patterns — particularly in the auto industry. The chart below compares Japanese export prices (standard passenger cars) by destination, showing the impact of…

Context: Tariffs and the Price Channel When governments impose tariffs, one of the key questions is: How much do import prices actually rise?In theory, tariffs make imports costlier, but exchange-rate movements, supplier pricing, and corporate margins often absorb part of the shock. The IMF’s analysis of US import prices excluding fuels compares two major tariff episodes: 2018 episode (blue line): Tariffs during the US–China trade tensions.

Understanding Inflation Pass-Through Inflation doesn’t arise in isolation — it spreads through trade linkages, production costs, and consumer demand chains.The IMF’s “Inflation Pass-Through” chart reveals how tariff changes and supply disruptions affect U.S. personal consumption expenditure (PCE) prices between February and July 2025. Each bar in the figure shows the percentage contribution of: 🟦 Direct effects (immediate impact of import tariffs on final goods prices)

Why Services Matter While goods prices have eased worldwide, services inflation—covering rent, transport, health, hospitality, and education—remains stubbornly high.Services are labor-intensive and slow to respond to monetary policy, making them the last mile of global disinflation. Figure 1.6 (4) — Services Inflation Trends (2019 – 2025) Key Regions: United States (blue) Euro Area (red) Other Advanced Economies (gold)

Understanding Core Goods Inflation Core goods inflation isolates price changes in non-food, non-energy manufactured goods — such as cars, electronics, clothing, and household items. It reflects global supply chains, shipping costs, and industrial demand rather than local food or fuel shocks. IMF data (2019–2025) compares trends across: United States (blue) Euro Area (red) Other Advanced Economies (AEs) (gold) China (green)

What Is Core Inflation? Core inflation measures the underlying trend in prices by excluding volatile food and energy items. It reflects the true persistence of inflation within an economy — often driven by wages, housing, services, and expectations. The IMF’s latest Core Inflation chart tracks five major groups: United States (blue) Euro Area (red) Other Advanced Economies (AEs) (gold)