Course Content

Data Science Interview Challenge

1. Python

Introduction Challenge 1: List Comprehension Challenge 2: String Manipulation Challenge 3: Dictionary Manipulation Challenge 4: Nested Loops Challenge 5: Classes Theoretical Questions

2. NumPy

Challenge 1: Array Creation Challenge 2: Array Manipulation Challenge 3: Statistical Insights Challenge 4: Handling Missing Values Challenge 5: Subarray Sorting Theoretical Questions

3. Pandas

Challenge 1: DataFrame Creation Challenge 2: Data Grouping Challenge 3: Indexing and MultiIndexing Challenge 4: Altering DataFrame Challenge 5: Iterating Over Data Theoretical Questions

4. Matplotlib

Challenge 1: Fundamentals of Plotting Challenge 2: Other Graph Types Challenge 3: Subplots Challenge 4: Customizing Plots Challenge 5: Adding Text to Plots Theoretical Questions

5. Seaborn

Challenge 1: Visualizing Distributions Challenge 2: Exploring Categorical Data Challenge 3: Relational Plots Challenge 4: Regression Plots Challenge 5: Matrix Plots Theoretical Questions

6. Statistics

Challenge 1: Probabilities and Distributions Challenge 2: Bayes' Theorem Challenge 3: Hypothesis Testing Challenge 4: Confidence Intervals Challenge 5: Correlation Theoretical Questions

7. Scikit-learn

Challenge 1: Data Scaling Challenge 2: Basic Model Creation Challenge 3: Pipelines Challenge 4: Cross-validation Challenge 5: Hyperparameter Tuning Theoretical Questions

Challenge 3: Statistical Insights

Gleaning insights from your data often involves statistical analysis. NumPy shines here with its:

Rich Set of Functions: From means and medians to variances and percentiles, NumPy has it covered.
Performance: NumPy's statistical functions are optimized, making them notably fast on large datasets.
Reliability: The library's tried and tested functions ensure consistent and accurate results.

Harnessing NumPy's statistical tools can empower you to extract meaningful patterns and insights from your data, which is a pivotal step in many data science projects.

Task

Numpy provides functions to obtain insights about data.

Compute the mean of the data.
Get the standard deviation of the data.
Identify the value in the array that is nearest to the mean value of the array.

Task

Numpy provides functions to obtain insights about data.

Compute the mean of the data.
Get the standard deviation of the data.
Identify the value in the array that is nearest to the mean value of the array.

Everything was clear?

Section 2. Chapter 3

Challenge 3: Statistical Insights

Gleaning insights from your data often involves statistical analysis. NumPy shines here with its:

Rich Set of Functions: From means and medians to variances and percentiles, NumPy has it covered.
Performance: NumPy's statistical functions are optimized, making them notably fast on large datasets.
Reliability: The library's tried and tested functions ensure consistent and accurate results.

Harnessing NumPy's statistical tools can empower you to extract meaningful patterns and insights from your data, which is a pivotal step in many data science projects.

Task

Numpy provides functions to obtain insights about data.

Compute the mean of the data.
Get the standard deviation of the data.
Identify the value in the array that is nearest to the mean value of the array.

Task

Numpy provides functions to obtain insights about data.

Compute the mean of the data.
Get the standard deviation of the data.
Identify the value in the array that is nearest to the mean value of the array.

Everything was clear?

Section 2. Chapter 3

Challenge 3: Statistical Insights

Gleaning insights from your data often involves statistical analysis. NumPy shines here with its:

Rich Set of Functions: From means and medians to variances and percentiles, NumPy has it covered.
Performance: NumPy's statistical functions are optimized, making them notably fast on large datasets.
Reliability: The library's tried and tested functions ensure consistent and accurate results.

Harnessing NumPy's statistical tools can empower you to extract meaningful patterns and insights from your data, which is a pivotal step in many data science projects.

Task

Numpy provides functions to obtain insights about data.

Compute the mean of the data.
Get the standard deviation of the data.
Identify the value in the array that is nearest to the mean value of the array.

Task

Numpy provides functions to obtain insights about data.

Compute the mean of the data.
Get the standard deviation of the data.
Identify the value in the array that is nearest to the mean value of the array.

Everything was clear?

Gleaning insights from your data often involves statistical analysis. NumPy shines here with its:

Rich Set of Functions: From means and medians to variances and percentiles, NumPy has it covered.
Performance: NumPy's statistical functions are optimized, making them notably fast on large datasets.
Reliability: The library's tried and tested functions ensure consistent and accurate results.

Harnessing NumPy's statistical tools can empower you to extract meaningful patterns and insights from your data, which is a pivotal step in many data science projects.

Task

Numpy provides functions to obtain insights about data.

Compute the mean of the data.
Get the standard deviation of the data.
Identify the value in the array that is nearest to the mean value of the array.

Section 2. Chapter 3

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