Data Science has gained popularity in the last few years as almost all scalable decisions today rely on data. Tons of data are being generated every day with billions of devices. But this data becomes useful only when there is someone to analyze it. Data science is the field concerned with data collection, sorting, storing, and analysis, and growing in demand from across the industry.

If you are an aspirant, you should know the common data science interview questions you may face during your entry. You can start preparing for a data science interview after completing your graduation and getting a data science certification. If you prepare yourself well, you can get your dream job in data science. Here are some most important questions to prepare for a data science interview:

If you are appearing for a data science interview for the first time, you are likely to face the following questions:

Data Science is a field that combines scientific processes, coding algorithms, machine learning techniques, and several tools to gather meaningful insights from given data sets. The data science lifecycle consists of various steps, including:

- Business understanding
- Data mining
- Data cleaning
- Data exploration
- Feature engineering
- Predictive modeling
- Data visualization

Data Science is an umbrella term that deals with explorations and innovations. On the other hand, data analytics is s more specific field that uses existing resources. In other words, data science focuses on answering questions for futuristic problems, whereas data analytics is all about solving present problems using existing historical context.

Data Science |
Data Analytics |
---|---|

The most used programming language is Python. |
Programming knowledge in both Python and R is required. |

You must have in-depth programming knowledge. |
Only basic programming knowledge is enough. |

Machine Learning algorithms are used to drive meaningful insights. |
Data analytics doesn’t use ML algorithms. |

Data Science skills include computer science, machine learning, software development, big data software tools, algorithm development, etc. |
Data Analytics skills include data management systems, data analysis software, data visualization tools, business intelligence tools, etc. |

The common skills in data science and data analytics include Basic statistical analysis, data mining, problem-solving, programming languages, data storytelling, etc.

Recommendation engines are systems based on ML algorithms that use data science techniques to recommend relevant products & services to consumers. The primary goal for many businesses is to understand their customer’s behaviors.

Recommendation engines aim to analyze customers’ behavior and recommend products relevant to their interests. Most leading e-commerce businesses such as Amazon, YouTube, Netflix, and Flipkart, use such recommendation systems.

Linear regression predicts the value of a dependent variable (Y) based on the value of an independent variable (X). The value of variable Y is predicted using the value of variable X. Here, variable X is called the predictor variable, and Y is the criterion variable.

Logistic Regression is the technique used to find binary outcomes from a combination of predictor variables. The number of outcomes is limited in this regression model, like Yes or No, 0 or 1, etc.

When there are extremely large datasets, data analyses cannot be performed on the entire data. In such cases, some data samples are selected that can represent the whole data. There are two types of sampling techniques:

**Probability Sampling Techniques:**Simple random sampling, clustered sampling, and stratified sampling.**Non-Probability Sampling Techniques:**Convenience sampling, quota sampling, and snowball sampling.

A bias in data science is a type of mistake in the data science model when an algorithm fails to capture the important patterns and trends in data. It happens when the data is too complex to comprehend for the algorithm. Due to this complexity, the data science model is constructed based on assumptions, making it less accurate.

Data Science and Machine Learning are two different but closely related fields. Data Science works with enormous amounts of data to extract useful information. Data Science uses Machine Learning algorithms to turn complex data into easy-to-understand formats. ML methods are also used to automate the building of analytical models to study big data.

If you are already working in the data science field and preparing for an interview to get promoted to higher positions, here are some important questions and answers you must learn:

RMSE (Root Mean Square Error) in a linear regression model is used to test the performance of an ML model. It evaluates the data spread around the line of best fit to measure the deviation from the actual value. It is calculated by taking the square root of MSE (Mean Square Error). A model with zero RMSE value indicates the perfect fit.

**Overfitting:** Overfitting or force-fitting in a model occurs when the model cannot analyze new data but gives accurate predictions for the training data. In other words, these models correspond to a particular data set. Overfitting is most likely to occur in decision trees.

**Underfitting:** Underfitting in a model occurs when the model performs poorly, even on the training data. These models are unable to find the relationship between the input variables. This generally happens due to low variance and high bias. Underfitting is more likely to occur in linear regression models.

Neural networks are computing systems that combine various nodes that work like human brain neurons. These neural networks identify the trends and patterns in data to use this knowledge for future data predictions.

One of the simplest neural networks, Perceptron, contains a single neuron performing two functions – estimating the weighted sum of two or more input variables and generating one output. The output can activate or deactivate a device, for example, turn a television on or off.

Some neural networks are more complicated and consist of three layers:

**Input Layer:**The input layer in a neural network receives the input.**Hidden Layer:**Neural network consists of multiple hidden layers between the input and output layers. The initial layers are low-level patterns, and other layers combine the outputs from previous layers to identify new patterns.**Output Layer:**This layer shows the prediction as output.

Here are the steps we use to solve a data analysis project:

- Firstly, you need to understand the requirements of the business you are working for.
- The second step is to explore the data carefully. If you find any data points missing, get your requirements fulfilled by the organization.
- The next step is to perform data cleaning and preparation. Here, you must find out missing values and transform the data to prepare it for modeling.
- Use your model against the data, build visualization, and analyze it carefully to drive meaningful insights.
- Track the results and performance of the model over time.
- Make sure to perform cross-validation of the model.

Data cleaning forms the bulk of the data science lifecycle. It identifies errors, duplicates, and irrelevant data from a raw data set and fixes them. Data cleaning is the process of cleaning data from multiple sources to transform it into a format workable for data scientists.

As the quantity of data increases, it becomes more time-consuming to clean this data. Data Cleaning might take up 80% of the total time to analyze a data set. This is why it is a critical part of data science.

You first need to identify the variables with missing values. If you can figure out a pattern, you can move further to drive meaningful information out of it. On the other hand, if no patterns are identified, you can either replace the missing values with mean or ignore them. If more than 80% of values are missing, you can omit the variable instead of substituting the missing values.

There are several techniques to correct an imbalanced data set. It can be done by resampling, using the right evaluation metrics, or other methods. The following are some best approaches to balancing data:

**Use the Correct Evaluation Metrics:**Using the right evaluation metrics that give valuable information is very important to turn imbalanced data into balanced ones. You can apply evaluation metrics, such as specificity, sensitivity, F1 score, MCC, and AUC.**Training Set Resampling:**You can also balance data by working with different data sets by resampling the training data. You can use under-sampling when the data quantity is sufficient and over-sampling when the data quantity is not sufficient.**Perform K-Fold Cross-Validation:**You should apply cross-validation before over-sampling. If you use K-fold cross-validation after over-sampling, the overfitted model derives a result.

Statistical analysis is classified into univariate, bivariate, and multivariate analysis based on the number of variables to be processed. Here is how the three are different from each other:

**Univariate Analysis:**In this, you deal with only one variable at a time, for example, a sales pie chart based on a particular region.**Bivariate Analysis:**In this, you study two variables simultaneously, for example, a graph of sales and spending volume by a business.**Multivariate Analysis:**In this, you deal with more than two variables at a time. For example, signs recorded by a newborn baby, such as blood pressure, temperature, respiratory rate, heart rate, etc.

Before you start applying for data science jobs and giving interviews, you must have a strong foundation in the subject. A good data science course will prepare you for the most entry-level questions and develop practical skills. Application-based questions are common for every data science interview.

There are lots of data science course options available online and offline today. You must check the curriculum, duration, learning style, fees, and resources before enrolling in the course.

**IBM Professional Certificate in Data Science:**It Covers Business Analytics in R, Machine Learning in Python, and Data Analysis in SQL. You get a certification from IBM.**Certified Data Science Specialist:**This course also covers Business Analytics, data analysis, and machine learning.**PG Program in Data Science:**After completion, you get certified by UPES. The course is ranked among the top 5 courses in India.**Executive Program in Data Science for Managers:**For this course, you should have at least three years of experience in data science after graduation. It is an advanced-level course designed for experienced professionals.

To clear a data science interview, you don’t only need theoretical knowledge, but you should also be aware of how to apply this knowledge in real life. So, you can work on real-world data science projects before you go for an interview.

The difficulty level of data science interview questions depends on the complexity and experience required for the job you have applied for. Data science interview questions for beginners usually bank on the basic understanding of data science concepts. So, if you have completed your data science certification you should not have a problem clearing the interview.

Yes, you must have a decent knowledge of programming languages, such as Python, R, SQL, etc., to crack a data science interview.

The field of data science is open to everyone with an interest in learning mathematics, statistics, programming, etc. Even non-IT students can become data scientists by developing the required skills.

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