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Data Science Interview Challenge
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Data Science Interview Challenge

Data Science Interview Challenge

1. Python
IntroductionChallenge 1: List ComprehensionChallenge 2: String ManipulationChallenge 3: Dictionary ManipulationChallenge 4: Nested LoopsChallenge 5: ClassesTheoretical Questions
2. NumPy
Challenge 1: Array CreationChallenge 2: Array ManipulationChallenge 3: Statistical InsightsChallenge 4: Handling Missing ValuesChallenge 5: Subarray SortingTheoretical Questions
3. Pandas
Challenge 1: DataFrame CreationChallenge 2: Data GroupingChallenge 3: Indexing and MultiIndexingChallenge 4: Altering DataFrameChallenge 5: Iterating Over DataTheoretical Questions
4. Matplotlib
Challenge 1: Fundamentals of PlottingChallenge 2: Other Graph TypesChallenge 3: SubplotsChallenge 4: Customizing PlotsChallenge 5: Adding Text to PlotsTheoretical Questions
5. Seaborn
Challenge 1: Visualizing DistributionsChallenge 2: Exploring Categorical DataChallenge 3: Relational PlotsChallenge 4: Regression PlotsChallenge 5: Matrix PlotsTheoretical Questions
6. Statistics
Challenge 1: Probabilities and DistributionsChallenge 2: Bayes' TheoremChallenge 3: Hypothesis TestingChallenge 4: Confidence IntervalsChallenge 5: CorrelationTheoretical Questions
7. Scikit-learn
Challenge 1: Data ScalingChallenge 2: Basic Model CreationChallenge 3: PipelinesChallenge 4: Cross-validationChallenge 5: Hyperparameter TuningTheoretical Questions

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Challenge 2: Data Grouping

Pandas, known for its comprehensive data analysis tools, offers a versatile grouping mechanism called the groupby method. This method is pivotal for aggregating data based on certain criteria, a process similar to the SQL GROUP BY statement. The benefits of using groupby are manifold:

  • Granularity Control: You can aggregate data at different levels of granularity, from high level (e.g., grouping by country) to fine-grained (e.g., grouping by individual timestamps).
  • Simplicity: The groupby syntax is concise and expressive, making it easy to chain operations and achieve complex aggregations.
  • Extensibility: With groupby, you can apply custom aggregation functions, not just the built-in ones, giving you the power to compute custom metrics for groups.

When diving into data exploration, the grouping capabilities of Pandas can reveal insightful patterns and trends by segmenting data into meaningful categories.

Uppgift

Swipe to start coding

Demonstrate data grouping in Pandas with the following tasks:

  1. Group data by a single column A.
  2. Sum all data grouped for column A using the built-in function.
  3. Apply multiple aggregation functions simultaneously. Get sum aggregation for B column and mean for C column.
  4. Group by multiple columns (A and B).

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 2
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book
Challenge 2: Data Grouping

Pandas, known for its comprehensive data analysis tools, offers a versatile grouping mechanism called the groupby method. This method is pivotal for aggregating data based on certain criteria, a process similar to the SQL GROUP BY statement. The benefits of using groupby are manifold:

  • Granularity Control: You can aggregate data at different levels of granularity, from high level (e.g., grouping by country) to fine-grained (e.g., grouping by individual timestamps).
  • Simplicity: The groupby syntax is concise and expressive, making it easy to chain operations and achieve complex aggregations.
  • Extensibility: With groupby, you can apply custom aggregation functions, not just the built-in ones, giving you the power to compute custom metrics for groups.

When diving into data exploration, the grouping capabilities of Pandas can reveal insightful patterns and trends by segmenting data into meaningful categories.

Uppgift

Swipe to start coding

Demonstrate data grouping in Pandas with the following tasks:

  1. Group data by a single column A.
  2. Sum all data grouped for column A using the built-in function.
  3. Apply multiple aggregation functions simultaneously. Get sum aggregation for B column and mean for C column.
  4. Group by multiple columns (A and B).

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 2
Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
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