Accenture Data Science Analyst Interview Questions and Answers

To analyze the problem of poor performance of certain pizza outlets after the pandemic, start by identifying potential factors and gathering data.

• Identify potential factors such as changes in consumer behavior, supply chain disruptions, or local regulations

• Gather data on sales, customer feedback, employee turnover, and operational costs

• Analyze the data to identify patterns and correlations

• Develop hypotheses and test them through further analysis or experiments

• Recommend soluti...read more

PII stands for Personally Identifiable Information. It refers to any data that can be used to identify an individual.

• Examples of PII include name, address, phone number, email address, social security number, driver's license number, passport number, and date of birth.

• PII can also include biometric data such as fingerprints or facial recognition data.

• It is important to protect PII to prevent identity theft and other forms of fraud.

Normalization and standardization are techniques used to transform data into a common scale.

• Normalization scales the data between 0 and 1, making it easier to compare different features.

• Standardization transforms the data to have a mean of 0 and standard deviation of 1, making it easier to compare different samples.

• Normalization is useful when the scale of the features varies widely, while standardization is useful when the data has outliers or follows a normal distribution.

• E...read more

Variance and standard deviation are measures of spread or dispersion of a dataset.

• Variance is the average of the squared differences from the mean.

• Standard deviation is the square root of variance.

• They are used to understand the distribution of data and to compare different datasets.

• Higher variance or standard deviation indicates more spread or variability in the data.

• Lower variance or standard deviation indicates less spread or variability in the data.

Precision and recall are two metrics used to evaluate the performance of a classification model.

• Precision measures the proportion of true positives among all positive predictions.

• Recall measures the proportion of true positives among all actual positives.

• Both metrics are important in different scenarios, depending on the cost of false positives and false negatives.

• For example, in a medical diagnosis scenario, recall may be more important to avoid missing a potentially life-th...read more

Proficient in Python with experience in data analysis and visualization.

• Experience in using Python libraries such as Pandas, NumPy, and Matplotlib.

• Ability to write efficient and optimized code for data manipulation and analysis.

• Familiarity with machine learning algorithms and their implementation in Python.

• Experience in web scraping and data extraction using Python.

• Proficient in using Jupyter Notebook for data analysis and visualization.

Proficient in Python, SQL, and PowerBI for data analysis and visualization.

• Extensive experience using Python for data manipulation and analysis

• Strong SQL skills for querying databases and extracting relevant information

• Proficient in creating interactive dashboards and reports using PowerBI

• Ability to integrate Python scripts with PowerBI for advanced analytics

• Experience in data visualization techniques to communicate insights effectively