Data Science Career Guide - Interview Preparation

Discover why data science offers fulfilling, high-demand work across industries by applying math and coding to extract insights, with strong pay and growing job opportunities.

Explore the spectrum of data science roles beyond data scientist, including product analyst, business intelligence engineer, machine learning engineer, and data engineer, and how job postings define required skills.

Delve into programming languages, frameworks, and software tools in data science, and learn to pick one language (often Python) plus SQL and key libraries like pandas and scikit-learn.

Explore core machine learning concepts for data science roles, including supervised and unsupervised learning, key algorithms, model validation, regularization, and basics of natural language processing, with practical resources.

Explore common probability theory interview questions and practice with coins and dice to prepare for data science technical screenings.

Explore the expected number of flips required for a fair coin to yield two consecutive identical results, whether two heads or two tails, in this probability theory interview question.

Explore the probability of obtaining a total of four when rolling two dice by adding the outcomes and calculating the likelihood.

Compute the probability of rolling a sum of four with two dice by counting three favorable outcomes—(1,3), (2,2), (3,1)—out of 36 total outcomes, giving 1/12.

Learn to compute the probability of seeing a car in ten minutes when the chance in thirty minutes is 0.95, assuming a constant probability.

Assume a constant default probability to solve the interview question. Compute P from 1−P^3=0.95, then the ten-minute car probability is 1−P.

Assess the probability that it is raining in Seattle based on three independent friends who may lie or tell the truth, starting from a 25 percent prior.

Use a probability tree to determine the likelihood it is raining in Seattle given three independent reports. Leverage truth and lie probabilities to compute the conditional rain chance.

Explore the difference between a type one error and a type two error in statistics, and practice explaining it clearly by whiteboarding your answer.

Explore type one and type two errors in hypothesis testing—false positives and false negatives—when the null hypothesis is true but rejected, or false yet not rejected, with toothpaste examples.

Apply Bayes' theorem to a medical test: with 1% base rate, 99% sensitivity, and 99% specificity, a positive result indicates a 50% chance of infection.

Explore how to determine a motor guarantee using a normal distribution with mean 10 years and standard deviation 2 years, targeting the 3% failure tail via a z-table.

Learn to tackle open-ended product design and metrics questions in data science interviews by thinking aloud, framing answers as a collaborative conversation, and analyzing features tied to company metrics.

Explore how A/B testing with a new search algorithm can yield higher advertising revenue despite less relevant results, and examine potential causes within product design and metrics.

Explain why a new search algorithm can raise advertising revenue despite less relevant results by increasing searches and potentially more relevant ads served by a separate ads algorithm.

Evaluate product design and metrics by comparing mpg upgrades for Technology A on car X and Technology B on car Y, with a 50/50 country split, to maximize gasoline savings.

Analyze two fuel-efficiency policies in a product design and metrics interview, showing policy B saves more gasoline countrywide by comparing mpg improvements with an average distance D.

Identify what's wrong with a sample sql query, pause to review. Prepare to learn the solution in the next lecture.

Identify the error in the SQL query select ID trial date from payments group by ID, as presented in the data with SQL interview question that highlights grouping and selection issues.

Learn how to fix a SQL group by error by applying an aggregate to non-grouped columns like trial date. Group by date when dealing with time-stamped values to clarify results.

Analyze a flawed sql query that selects user ID and avg(total) as average order total from invoices, using a having clause with count on order ID >= 1.

Write an SQL query to join the employees and managers tables on the managed_by foreign key. Retrieve all employees who are managed by Sandy Kim.

Master SQL joins to solve an interview question: find employees managed by Sandy Kim. Build a join on employees.managed_by and managers.id, then filter where managers.name like 'Sandy Kim'.

Write and practice a query that retrieves all employees with no manager using the employees and managers tables, illustrated by Jane Doe's null manager.

Retrieve all employees who have no manager using sql query that checks for null in the managed by column without join, noting variations across mysql, postgresql, oracle, and sql server.

Explains linear regression and its core assumptions—linearity between y and x and normally distributed residuals—and reviews common types like ordinary least squares and ridge and lasso.

Describe the logistic regression formula and how it enables binary classification within the machine learning interview questions, as part of the data science career guide.

Describe the logistic regression formula and how to use the logistic function to turn linear regression outputs into probabilities for binary classification, with a 0.5 cutoff.

Explore how decision trees choose splits by maximizing information gain using entropy in a top-down approach from the root node, with alternatives like the Gini index.

Explain the difference between random forest and boosting tree algorithms such as gradient boosting, highlighting their approaches and use cases for machine learning interview preparation.

Describe how the support vector machine works in a general sense and illustrate the concept with a diagram to explain decision boundaries.

Explore how support vector machines find a hyperplane that maximizes the margin between classes, using support vectors to define the decision boundary, and how the kernel trick enables nonlinear classification.

Define overfitting in machine learning, discuss its causes, and outline ways to avoid it in practice.

Describe the differences between accuracy, precision, and recall in classification tasks. Understand these common metrics and how their definitions relate to model performance.

Learn to evaluate regression with mean absolute error, mean squared error, and root mean squared error. MAE averages absolute errors, MSE squares errors, RMSE preserves units.

Explore the design of experiments and its statistical foundations, including P value, statistical test, and hypothesis test. Review concepts before attempting the questions and consult the guidebook for resources.

Master design of experiments with a/b testing by selecting metrics like daily active users, setting up control and variant pages, randomizing samples, and evaluating hypotheses using alpha and p-values.