Coding and Aptitude Round before interview

Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.

Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.

Resources for Prep:

For algorithms and data structures prep,Leetcode and Hackerrank are good resources.

For aptitude prep, you can refer to IndiaBixand Practice Aptitude.

With respect to data science challenges, practice well on GLabs and Kaggle.

Brilliant is an excellent resource for tricky math and statistics questions.

For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.

Things to Note:

Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!

In case, you are finished with the test before time, recheck your answers and then submit.

Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.

9 beginner-friendly coding project ideas to build confidence:

📅 Digital Clock — show real-time hours, minutes, seconds
🎲 Dice Roller — generate random numbers with UI
📋 Quiz App — multiple choice questions with score tracking
🔢 Number Guessing Game — apply loops and conditionals
💬 Message Encoder/Decoder — basic string manipulation
🖼️ Image Slider — work with DOM and transitions
🔐 Password Generator — use randomization and user input
📈 Temperature Converter — switch between Celsius and Fahrenheit
✏️ Notes App — add, delete, and save notes with local storage

#coding #projects

Resume key words for data scientist role explained in points:

1. Data Analysis:
   - Proficient in extracting, cleaning, and analyzing data to derive insights.
   - Skilled in using statistical methods and machine learning algorithms for data analysis.
   - Experience with tools such as Python, R, or SQL for data manipulation and analysis.

2. Machine Learning:
   - Strong understanding of machine learning techniques such as regression, classification, clustering, and neural networks.
- Experience in model development, evaluation, and deployment.
   - Familiarity with libraries like TensorFlow, scikit-learn, or PyTorch for implementing machine learning models.

3. Data Visualization:
   - Ability to present complex data in a clear and understandable manner through visualizations.
   - Proficiency in tools like Matplotlib, Seaborn, or Tableau for creating insightful graphs and charts.
   - Understanding of best practices in data visualization for effective communication of findings.

4. Big Data:
   - Experience working with large datasets using technologies like Hadoop, Spark, or Apache Flink.
   - Knowledge of distributed computing principles and tools for processing and analyzing big data.
   - Ability to optimize algorithms and processes for scalability and performance.

5. Problem-Solving:
   - Strong analytical and problem-solving skills to tackle complex data-related challenges.
   - Ability to formulate hypotheses, design experiments, and iterate on solutions.
   - Aptitude for identifying opportunities for leveraging data to drive business outcomes and decision-making.


Resume key words for a data analyst role

1. SQL (Structured Query Language):
   - SQL is a programming language used for managing and querying relational databases.
   - Data analysts often use SQL to extract, manipulate, and analyze data stored in databases, making it a fundamental skill for the role.

2. Python/R:
   - Python and R are popular programming languages used for data analysis and statistical computing.
   - Proficiency in Python or R allows data analysts to perform various tasks such as data cleaning, modeling, visualization, and machine learning.

3. Data Visualization:
   - Data visualization involves presenting data in graphical or visual formats to communicate insights effectively.
   - Data analysts use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to create visualizations that help stakeholders understand complex data patterns and trends.

4. Statistical Analysis:
   - Statistical analysis involves applying statistical methods to analyze and interpret data.
   - Data analysts use statistical techniques to uncover relationships, trends, and patterns in data, providing valuable insights for decision-making.

5. Data-driven Decision Making:
   - Data-driven decision making is the process of making decisions based on data analysis and evidence rather than intuition or gut feelings.
   - Data analysts play a crucial role in helping organizations make informed decisions by analyzing data and providing actionable insights that drive business strategies and operations.

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1. How many report formats are available in Excel?

There are three report formats available in Excel; they are:
1. Compact Form
2. Outline Form
3. Tabular Form

2. What are sets in Tableau?

Sets are custom fields that define a subset of data based on some conditions. A set can be based on a computed condition, for example, a set may contain customers with sales over a certain threshold. Computed sets update as your data changes. Alternatively, a set can be based on specific data point in your view.

3. What is the difference between DROP and TRUNCATE commands?

DROP command removes a table and it cannot be rolled back from the database whereas TRUNCATE command removes all the rows from the table.

4. What is slicing in Python?

Ans: Slicing is used to access parts of sequences like lists, tuples, and strings. The syntax of slicing is-[start:end:step]. The step can be omitted as well. When we write [start:end] this returns all the elements of the sequence from the start (inclusive) till the end-1 element. If the start or end element is negative i, it means the ith element from the end.

5. What is the map() and filter() function in Python?

The map() function is a higher-order function. This function accepts another function and a sequence of ‘iterables’ as parameters and provides output after applying the function to each iterable in the sequence. The filter() function is used to generate an output list of values that return true when the function is called.

Top three most required tech stack for the following roles:

1. Data Analyst: SQL, Excel, Tableau/Power BI
2. Data Scientist: Python, R, SQL
3. Quantitative Analyst: Python, R, MATLAB
4. Business Analyst: SQL, Business Requirements Gathering, Agile Methodologies, Power BI/Tableau
5. Data Engineer: Python/Scala, SQL, Cloud, Apache Spark
6. Machine Learning Engineer: Python, TensorFlow/PyTorch, Docker/Kubernetes.

Coding and Aptitude Round before interview

Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.

Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.

Resources for Prep:

For algorithms and data structures prep,Leetcode and Hackerrank are good resources.

For aptitude prep, you can refer to IndiaBixand Practice Aptitude.

With respect to data science challenges, practice well on GLabs and Kaggle.

Brilliant is an excellent resource for tricky math and statistics questions.

For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.

Things to Note:

Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!

In case, you are finished with the test before time, recheck your answers and then submit.

Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.

9 tips to improve your problem-solving skills in coding:

Understand the problem before coding

Break problems into smaller parts

Practice daily on platforms like LeetCode or HackerRank

Learn common data structures and algorithms

Draw diagrams to visualize logic

Dry run your code with sample inputs

Focus on optimizing time and space complexity

Review solutions after solving a problem

Don’t fear hard problems — struggle builds skill

React with ❤️ for more coding tips

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