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:
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
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:
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.Also Read: The Impact Of Data Science On Business And Society And Why You Should Consider Taking A Course
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:
Here are the steps we use to solve a data analysis project:
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:
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:
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.
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.2. Is a data science interview hard?
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.3. Is coding required for a data science interview?
Yes, you must have a decent knowledge of programming languages, such as Python, R, SQL, etc., to crack a data science interview.4. Is data science easy for non-IT students?
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.Share this on