Wipro Data Analyst Interview Questions and Answers

Artificial intelligence functions by simulating human intelligence through algorithms and data processing.

• AI uses algorithms to analyze data patterns, such as in recommendation systems like Netflix.

• Machine learning, a subset of AI, allows systems to learn from data, like spam filters improving over time.

• Deep learning, another subset, uses neural networks to process complex data, such as image recognition in self-d...read more

AI enhances data analysis through automation, predictive modeling, and pattern recognition, improving insights and decision-making.

• Automated data cleaning: AI can identify and correct errors in datasets, saving time and improving accuracy.

• Predictive analytics: Machine learning algorithms can forecast trends, such as sales predictions based on historical data.

• Natural language processing: AI can analyze unstructured...read more

Data analysis is the process of inspecting, cleansing, transforming, and modeling data to discover useful information and support decision-making.

• Involves collecting data from various sources, such as surveys or databases.

• Data cleaning is crucial; for example, removing duplicates or correcting errors.

• Statistical analysis helps identify trends; e.g., calculating average sales over a period.

• Visualization tools like ...read more

The three golden rules of accounting guide the recording of financial transactions in a systematic manner.

• 1. Debit the receiver, credit the giver: This rule applies to personal accounts. For example, if you receive cash from a customer, you debit the cash account and credit the customer account.

• 2. Debit what comes in, credit what goes out: This rule is for real accounts. For instance, when you purchase equipment, ...read more

Golden rules are fundamental principles guiding data analysis for accuracy and efficiency.

• 1. Understand the data: Always know the source and context of your data. Example: Knowing if data is from surveys or sensors.

• 2. Clean the data: Remove duplicates and handle missing values. Example: Using imputation for missing data points.

• 3. Visualize the data: Use graphs to identify trends and patterns. Example: Creating a s...read more

Developing a spam detection system involves data collection, feature extraction, and model training to classify messages.

• 1. Data Collection: Gather a dataset of labeled messages (spam and non-spam). Example: Use datasets like the Enron email dataset.

• 2. Preprocessing: Clean the data by removing special characters, stop words, and normalizing text. Example: Convert all text to lowercase.

• 3. Feature Extraction: Conver...read more

I am proficient in Python, which I use extensively for data analysis, visualization, and automation tasks.

• Python's libraries like Pandas and NumPy are essential for data manipulation and analysis.

• I utilize Matplotlib and Seaborn for creating insightful visualizations.

• For statistical analysis, I often use SciPy and StatsModels.

• I have experience with SQL for querying databases and extracting relevant data.

OLTP stands for Online Transaction Processing, used for real-time transaction processing. OLAP stands for Online Analytical Processing, used for complex queries and data analysis.

• OLTP is used for day-to-day transaction processing in real-time

• OLAP is used for complex queries and data analysis for decision-making

• OLTP databases are normalized for efficient transaction processing

• OLAP databases are denormalized for fas...read more