Data Analyst Interview Questions and Answers

Data Analyst Interview Questions and Answers:-

1. What do you mean by Data Analysis?

Data analysis is a multidisciplinary field of data science, in which data is analyzed using mathematical, statistical, and computer science with domain expertise to discover useful information or patterns from the data. It involves gathering, cleaning, transforming, and organizing data to draw conclusions, forecast, and make informed decisions.

The purpose of data analysis is to turn raw data into actionable knowledge that may be used to guide decisions, solve issues, or reveal hidden trends.

2. What is Data Wrangling?

Data Wrangling is very much related concepts to Data Preprocessing. It’s also known as Data munging. It involves the process of cleaning, transforming, and organizing the raw, messy or unstructured data into a usable format.

The main goal of data wrangling is to improve the quality and structure of the dataset. So, that it can be used for analysis, model building, and other data-driven tasks.

3. What is data cleaning?

Data cleaning is the process of identifying the removing misleading or inaccurate records from the datasets. The primary objective of Data cleaning is to improve the quality of the data so that it can be used for analysis and predictive model-building tasks.

It is the next process after the data collection and loading.

4. What is Time Series analysis?

Time Series analysis is a statistical technique used to analyze and interpret data points collected at specific time intervals. Time series data is the data points recorded sequentially over time. The data points can be numerical, categorical, or both.

The objective of time series analysis is to understand the underlying patterns, trends and behaviours in the data as well as to make forecasts about future values.

5. What is data normalization, and why is it important?

Data normalization is the process of transforming numerical data into standardised range.

The objective of data normalization is scale the different features (variables) of a dataset onto a common scale, which make it easier to compare, analyze, and model the data.

This is particularly important when features have different units, scales, or ranges because if we doesn’t normalize then each feature has different-different impact which can affect the performance of various machine learning algorithms and statistical analyses.

6. What are the main libraries you would use for data analysis in Python?

For data analysis in Python, many great libraries are used due to their versatility, functionality, and ease of use. Some of the most common libraries are as follows:

  • NumPy
  • Pandas
  • SciPy
  • Matplotlib
  • Seaborn
  • Scikit-learn
  • Statsmodels

7. What is One-Hot-Encoding?

One-hot encoding is a technique used for converting categorical data into a format that machine learning algorithms can understand.

Categorical data is data that is categorized into different groups, such as colors, nations, or zip codes. Because machine learning algorithms often require numerical input, categorical data is represented as a sequence of binary values using one-hot encoding.

8. What is a probability distribution?

A probability distribution is a mathematical function that estimates the probability of different possible outcomes or events occurring in a random experiment or process.

It is a mathematical representation of random phenomena in terms of sample space and event probability, which helps us understand the relative possibility of each outcome occurring.

9. What is the central limit theorem?

The Central Limit Theorem (CLT) is a fundamental concept in statistics that states that, under certain conditions, the distribution of sample means approaches a normal distribution as sample size rises, regardless of the the original population distribution.

In other words, even if the population distribution is not normal, when the sample size is high enough, the distribution of sample means will tend to be normal.

10. What are the basic SQL CRUD operations?

SQL CRUD stands for CREATE, READ(SELECT), UPDATE, and DELETE statements in SQL Server. CRUD is nothing but Data Manipulation Language (DML) Statements.

CREATE operation is used to insert new data or create new records in a database table, READ operation is used to retrieve data from one or more tables in a database, UPDATE operation is used to modify existing records in a database table and DELETE is used to remove records from the database table based on specified conditions.

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