Tell about a recent project of yours;; Collaborating in the image matching kaggle competition. Obviously S2DS project too.
What are some areas in this business you are interested in?;; Technical consulting, energy projects. Any area with the room to problem solve, to apply the scientific method to business problems. Areas where data can be turned into decisions. Building technical systems too for operations (feasibility projects).
How do you approach prioritizing tasks in a data science project?;; Current project objectives, complexity, dependencies, and client impact.
How do you handle conflicts or disagreements within a data science team?;; I promote open communication, facilitate discussions, and seek win-win solutions to resolve conflicts constructively.
How do you stay updated with the latest developments and trends in data science?;; I would like to attend events, talk to peers, stay connect to colleagues online. Watch updates on youtube.
How do you ensure the quality and reliability of data used in your data science projects? ;; I implement data validation and cleaning procedures, conduct exploratory data analysis, and would collaborate with domain experts and engineers to verify data accuracy and relevance.
Can you describe a time when you had to make a decision under tight deadlines in a data science project? ;; In S2DS the client wanted further features to be added, so we said no nicely.
What strategies do you employ to ensure alignment between data science initiatives and business objectives? ;; I collaborate closely with stakeholders to understand business goals, prioritize projects based on their strategic importance, and regularly communicate progress and results to ensure alignment and maximize impact.
How do you stay organized and manage deadlines in your data science projects? ;; I utilize project management tools, break down tasks into manageable components, set realistic timelines, and regularly reassess priorities to ensure timely delivery.
Can you discuss a challenging problem you encountered in a data science project and how you resolved it? ;; In alice in wonderland people issue,, I analyzed the root cause, consulted domain experts or literature, experimented with alternative approaches, and iteratively refined solutions until achieving satisfactory results.
What do you think is the most important thing in a team?;; Buy in, communication, also initiative.
What do you think is a no-go in a team?;; Lack of accountability/blaming people, just own your mistakes and learn from them its more productive
Business and situational questions
Tell about a recent project of yours;; Collaborating in the image matching kaggle competition. Obviously S2DS project too.
What are some areas in this business you are interested in?;; Technical consulting, Energy projects. Any area with the room to problem solve, to apply the scientific method to business problems. Areas where data can be turned into decisions. Building technical systems too for operations (feasibility projects).
How do you approach prioritizing tasks in a data science project?;; Current project objectives, complexity, dependencies, and client impact.
How do you handle conflicts or disagreements within a data science team?;; I promote open communication, facilitate discussions, and seek win-win solutions to resolve conflicts constructively.
How do you stay updated with the latest developments and trends in data science?;; I would like to attend events, talk to peers, stay connect to colleagues online. Watch updates on youtube.
How do you ensure the quality and reliability of data used in your data science projects? ;; I implement data validation and cleaning procedures, conduct exploratory data analysis, and would collaborate with domain experts and engineers to verify data accuracy and relevance.
Can you describe a time when you had to make a decision under tight deadlines in a data science project? ;; In S2DS the client wanted further features to be added, so we said no nicely.
What strategies do you employ to ensure alignment between data science initiatives and business objectives? ;; I collaborate closely with stakeholders to understand business goals, prioritize projects based on their strategic importance, and regularly communicate progress and results to ensure alignment and maximize impact.
How do you stay organized and manage deadlines in your data science projects? ;; I utilize project management tools, break down tasks into manageable components, set realistic timelines, and regularly reassess priorities to ensure timely delivery.
Can you discuss a challenging problem you encountered in a data science project and how you resolved it? ;; In alice in wonderland people issue,, I analyzed the root cause, consulted domain experts or literature, experimented with alternative approaches, and iteratively refined solutions until achieving satisfactory results.
Team work questions
What do you think is the most important thing in a team?;; Buy in, communication, also initiative.
What do you think is a no-go in a team?;; Lack of accountability/blaming people, just own your mistakes and learn from them its more productive
General questions
What are some areas of the DS field you are interested in?;; NLP (the techniques machines use to understand complex concepts), time series analysis (very real, forecasting).
Why are you interested in data science?;; Problem solving aspect, with tools that are technically interesting. Work with technical minded people. I enjoy the scientific viewpoint.
How would you interact with the data science community?;; Participate in Datafest, Kaggle projects, and engage with colleagues.
#interview_questions
What is data normalization and why do we need it? ;; Data Normalised Schema is used in Preprocessing for preprocessing as it rescales values to fit within a specific range.
Explain Dimensionality Reduction, where it’s used, and its benefits? ;; Dimensionality Reduction involves reducing the number of feature variables by obtaining a set of principal variables, reducing storage space, speeding up computation, removing redundant features, and enabling data visualization to identify patterns.
How do you handle missing or corrupted data in a dataset? ;; Missing or corrupted data can be handled by dropping affected rows or columns, replacing them with another value, or filling them with a placeholder value using methods like isnull(), dropna(), or fillna() in Pandas.
How would you go about doing an exploratory data analysis (EDA)? ;; EDA involves gaining insights from data before applying predictive models, starting with high-level global insights, dropping unnecessary columns, filling missing values, and creating basic visualizations such as bar plots and scatter plots to understand feature relationships.
How do you know which machine learning model you should use? ;; Model Selection depends on factors such as the nature of the problem, data characteristics, and desired outcomes, often involving trial-and-error.
Explain your phd and its outcomes. ;; Looking for a counter examples, what are FCJ. Built algorithims to compute, then computed them
Q2. What is the difference between Type I vs Type II error? ;; Type I error occurs when the null hypothesis is true, but it is rejected. Type II error occurs when the null hypothesis is false, but it is not rejected.
Q3. What is Linear Regression?;;Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
What do the terms P-value, coefficient, R-Squared value mean? ;;P-value indicates the significance of the coefficient, coefficient represents the strength and direction of the relationship, and R-Squared value measures the proportion of variance explained by the model.
What are the assumptions required for Linear Regression? ;; The assumptions include a linear relationship between dependent and independent variables, normally distributed and independent errors, minimal multicollinearity among explanatory variables.
What is a statistical interaction? ;; Statistical interaction occurs when the effect of one variable on a dependent variable is dependent on the value of another variable.
What is selection bias? ;; Selection bias refers to a systematic error in sampling that results in a sample that is not representative of the population, leading to incorrect conclusions about the population.