Data Scientist SDS Book

Time series decomposition breaks down data into components to better understand underlying patterns and support forecasting. The main components are:

Trend: Long-term progression (upward or downward).

Seasonal: Repeating short-term patterns (e.g., monthly or quarterly).

Cyclical (Option A): Medium- to long-term cycles (e.g., business cycles).

Irregular/Residual (Option C): Random, unpredictable variations.

Since trend analysis involves examining cyclical, seasonal, and irregular components, the correct answer is Option E (All of the above).

The Big Data Vision Workshop is an early-phase framework designed to help organizations shape their data-driven transformation journey. It is particularly beneficial when:

Option A: Organizations want to leverage big data but lack clarity on where to start.

Option B: Organizations already have large volumes of data but struggle to derive monetization strategies from it.

Option C: Organizations want to identify use cases where data and analytics can enhance or even redefine their business models.

Since all three statements apply, the correct answer is Option E (All of the above).

Graphical models represent relationships between objects using nodes (entities) and edges (relationships). Examples include:

Road maps (Option A): Nodes = intersections, Edges = roads.

Electrical circuits (Option B): Nodes = components, Edges = connections.

Computer networks (Option C): Nodes = devices, Edges = connections.

Geographical networks (Option D): Nodes = locations, Edges = transport or connectivity.

However:

Flow charts (Option E): These represent process flows, not structural networks of entities and relationships. They are procedural diagrams, not graphical models in the statistical/graph-theory sense.

Thus, the correct answer is Option E (Flow charts).

Apache Spark supports multiple programming languages for developing distributed applications:

Java (Option A): Supported through Spark’s JVM-based APIs.

Scala (Option B): Spark is natively written in Scala, and Scala APIs provide full functionality.

Python (Option C): Supported via PySpark, enabling Python developers to leverage Spark.

Additionally, Spark also supports R and SQL-like queries, making it versatile for data scientists and engineers.

Thus, the correct answer is Option D (All of the above).