Quick Roadmaps to start learning something new before 2025 😄

👉 Java

👉 Python

👉 Javascript

👉 Data Analysis

👉 Data Science

👉 Frontend development

👉 AI/ML

👉 SQL

👉 Web development

👉 Tableau

👉 Cyber Security

👉 Ethical Hacking

Always remember consistency is the key – small efforts today lead to big achievements tomorrow. Start now, embrace the journey, and watch your growth unfold. 💪

In case you need some help, feel free to reach out to me @love_data

ENJOY LEARNING 👍👍

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

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100 Must do Leetcode problems 🚀

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Python for everything 👆