The document serves as a guide for freshers preparing for big data interviews, highlighting essential skills such as programming languages (R or Python with SQL), statistics, machine learning concepts, data wrangling, data visualisation, software engineering principles, and understanding product management metrics. It emphasizes the importance of practical knowledge and familiarity with tools and techniques fundamental to data science. Additionally, it encourages taking mock interviews to further prepare for real-world scenarios.

1. rockinterview.in

2. rockinterview.in
A FRESHER’S GUIDE TO PREPARING
FOR A BIG DATA INTERVIEW

3. 1. BASIC PROGRAMMING LANGUAGES
YOU SHOULD KNOW:
At least one statistical programming language,
like R or Python (along with Numpy and Pandas Libraries)
And one database querying language like SQL
rockinterview.in

4. 2. STATISTICS:
Statistics is important to crunch data and to pick out the most important
figures out of a huge dataset. This is critical in the decision-making process
and to design experiments.
Here are a few phrases you should definitely be able to explain:
null hypothesis
P-value
maximum likelihood estimators
confidence intervals
rockinterview.in

5. 3. MACHINE LEARNING:
Familiarise yourself with how data science is used in practical
manners.
You should be able to explain K-nearest neighbours, random
forests, and ensemble methods.
These techniques are typically implemented in R or Python.
rockinterview.in

6. 4. DATA WRANGLING:
You should be able to identify corrupt or impure data and
correct them.
This basically means understanding that a negative number
cannot exist in a dataset that describes population, or a grey
and gray are the same colour, etc…
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7. 5. DATA VISUALISATION:
Learn to use data visualisation tools like ggplot, as they help you
present data and findings in a cohesive manner.
This is an important skill set, as it ensures that Product Managers and
other stakeholders understand your work and incorporate it in the
product.
.
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8. 6. SOFTWARE ENGINEERING:
Know the use cases and run time of these data structures:
Queues, Arrays, Lists, Stacks, Trees, etc.
These are often necessary in creating efficient algorithms for
machine learning.
rockinterview.in

9. 6. SOFTWARE ENGINEERING:
Know the use cases and run time of these data structures:
Queues, Arrays, Lists, Stacks, Trees, etc.
These are often necessary in creating efficient algorithms for
machine learning.
rockinterview.in

10. 7. PRODUCT MANAGEMENT:
Data Scientists that understand the product are the ones who will know
what metrics are the most important.
Usability Testing
Wireframing
Retention
Conversion Rates
Traffic Analysis
Know what these terms mean:
Customer Feedback
Internal Logs
A/B Testing
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11. Take a mock interview with us to find out more.
rockInterview.in