Data Science Interview Challenge

Data Science Interview Challenge

0%

1. Python

Let's take a look at what we'll be working with in this course. The first section will acquaint you with Python, a flexible and advanced programming language known for its clear syntax and readability.

Challenge 1: List Comprehension

Challenge 2: String Manipulation

Challenge 3: Dictionary Manipulation

Challenge 4: Nested Loops

Challenge 5: Classes

Theoretical Questions

2. NumPy

NumPy is a fundamental library in Python that facilitates efficient numerical computations with powerful n-dimensional arrays and mathematical functions.

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

Pandas provides intuitive and versatile data structures for efficient data manipulation and analysis, streamlining the initial stages of the data science pipeline.

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

Matplotlib is a comprehensive Python library for creating static, animated, and interactive visualizations in Python.

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

Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for creating informative and attractive statistical graphics.

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

Statistics provides data scientists with foundational techniques and tools to extract meaningful insights from data, allowing them to make informed decisions and predictions based on empirical evidence.

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

Scikit-learn is an open-source Python library that provides simple and efficient tools for data analysis and modeling, particularly for machine learning. Data scientists use it extensively for its comprehensive collection of algorithms and processing techniques, enabling them to quickly develop and deploy predictive models.

Challenge 1: Data Scaling

Challenge 2: Basic Model Creation

Challenge 3: Pipelines

Challenge 4: Cross-validation

Challenge 5: Hyperparameter Tuning

Theoretical Questions

Course Content

Data Science Interview Challenge

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

Theoretical Questions

1. In hypothesis testing, what is the null hypothesis, and what is the alternative hypothesis?

2. If the p-value in a hypothesis test is `0.03`, and the significance level (alpha) is set at `0.05`, do you reject or fail to reject the null hypothesis?

3. Explain what a 95% confidence interval means in the context of estimating a population mean.

4. If two variables have a correlation coefficient of `-0.85`, what does this tell you about their relationship?

5. Causation implies that:

6. In a scatter plot with points forming a straight line sloping upward from left to right, what type of correlation is likely present?

7. What statistical test is commonly used to determine if a correlation coefficient is significantly different from zero?

8. If you want to assess whether a set of data follows a normal distribution, which statistical test or plot is most appropriate?

9. In which real-life situation can the Chi-squared test be used?

10. Which of the following best describes the Central Limit Theorem (CLT)?

11. Which statement best captures the essence of the Law of Large Numbers?

In hypothesis testing, what is the null hypothesis, and what is the alternative hypothesis?

Select the correct answer

Null: The hypothesis we hope to support; Alternative: The opposite of the null hypothesis.

Null: The hypothesis we hope to reject; Alternative: The opposite of the null hypothesis.

Null: The hypothesis we hope to support; Alternative: A random guess.

If the p-value in a hypothesis test is 0.03, and the significance level (alpha) is set at 0.05, do you reject or fail to reject the null hypothesis?

Select the correct answer

Reject the null hypothesis.

Fail to reject the null hypothesis.

Explain what a 95% confidence interval means in the context of estimating a population mean.

Select the correct answer

The interval contains 95% of the data points in the sample.

The interval is guaranteed to contain the population mean.

We are 95% confident that the random chosen data falls within the interval.

There is a 95% chance that this interval would cover the real mean of the entire population.

If two variables have a correlation coefficient of -0.85, what does this tell you about their relationship?

Select the correct answer

They have a strong positive relationship.

There is no relationship between them.

They have a strong negative relationship.

The correlation coefficient is invalid.

Causation implies that:

Select the correct answer

Correlation exists between two variables.

One variable is the direct cause of changes in another variable.

Statistical significance has been established.

The p-value is less than 0.05.

In a scatter plot with points forming a straight line sloping upward from left to right, what type of correlation is likely present?

Select the correct answer

Positive correlation

Negative correlation

No сorrelation

Unclear

What statistical test is commonly used to determine if a correlation coefficient is significantly different from zero?

Select the correct answer

Chi-squared test

ANOVA

T-test

None of the above

If you want to assess whether a set of data follows a normal distribution, which statistical test or plot is most appropriate?

Select the correct answer

T-test

Shapiro-Wilk test

Scatter plot

Chi-squared test

In which real-life situation can the Chi-squared test be used?

Select the correct answer

Comparing the average income of two different cities.

Determining the effect of a new medication on blood pressure levels in a clinical trial.

Examining the correlation between the number of hours spent studying and exam scores in a class of students.

Analyzing the relationship between smoking status and the development of health problems in a group of individuals.

Which of the following best describes the Central Limit Theorem (CLT)?

Select the correct answer

The mean of a large sample drawn from any distribution will always equal the mean of the population.

The sum of random variables, regardless of distribution, will always be normally distributed.

The distribution of the sample mean of independent variables approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution.

A large sample from a population will always be normally distributed.

Which statement best captures the essence of the Law of Large Numbers?

Select the correct answer

The average of the results obtained from a large number of experiments should be close to the expected value and will tend to become closer as more trials are performed.

As the sample size increases, the sample distribution becomes more skewed.

The larger the sample size, the closer the sample variance is to the population variance.

Everything was clear?

Section 6. Chapter 6

We're sorry to hear that something went wrong. What happened?

some-alt