𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄😍

𝗦𝗤𝗟:- https://pdlink.in/3SMHxaZ

𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3FJhizk

𝗝𝗮𝘃𝗮  :- https://pdlink.in/4dWkAMf

𝗗𝗦𝗔 :- https://pdlink.in/3FsDA8j

 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4jLOJ2a

𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 :-  https://pdlink.in/4dFem3o

𝗖𝗼𝗱𝗶𝗻𝗴 :- https://pdlink.in/3F00oMw

Get Your Dream Tech Job In Your Dream Company💫

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.

𝟳 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 & 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀😍

💻 You don’t need to spend a rupee to master Python!🐍

Whether you’re an aspiring Data Analyst, Developer, or Tech Enthusiast, these 7 completely free platforms help you go from zero to confident coder👨‍💻📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4l5XXY2

Enjoy Learning ✅️

Data Analytics Interview Topics in structured way :

🔵Python: Data Structures: Lists, tuples, dictionaries, sets Pandas: Data manipulation (DataFrame operations, merging, reshaping) NumPy: Numeric computing, arrays Visualization: Matplotlib, Seaborn for creating charts

🔵SQL: Basic : SELECT, WHERE, JOIN, GROUP BY, ORDER BY Advanced : Subqueries, nested queries, window functions DBMS: Creating tables, altering schema, indexing Joins: Inner join, outer join, left/right join Data Manipulation: UPDATE, DELETE, INSERT statements Aggregate Functions: SUM, AVG, COUNT, MAX, MIN

🔵Excel: Formulas & Functions: VLOOKUP, HLOOKUP, IF, SUMIF, COUNTIF Data Cleaning: Removing duplicates, handling errors, text-to-columns PivotTables Charts and Graphs What-If Analysis: Scenario Manager, Goal Seek, Solver

🔵Power BI:
Data Modeling: Creating relationships between datasets
Transformation: Cleaning & shaping data using
Power Query Editor Visualization: Creating interactive reports and dashboards
DAX (Data Analysis Expressions): Formulas for calculated columns, measures Publishing and sharing reports, scheduling data refresh

🔵 Statistics Fundamentals: Mean, median, mode Variance, standard deviation Probability distributions Hypothesis testing, p-values, confidence intervals

🔵Data Manipulation and Cleaning: Data preprocessing techniques (handling missing values, outliers), Data normalization and standardization Data transformation Handling categorical data

🔵Data Visualization: Chart types (bar, line, scatter, histogram, boxplot) Data visualization libraries (matplotlib, seaborn, ggplot) Effective data storytelling through visualization

Also showcase these skills using data portfolio if possible

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