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 donโt go your way, you might have had a brain fade, it was not your day etc. Donโt worry! Shake if off for there is always a next time and this is not the end of the world.
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 donโt go your way, you might have had a brain fade, it was not your day etc. Donโt worry! Shake if off for there is always a next time and this is not the end of the world.
๐3
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๐1
Python Detailed Roadmap ๐
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โผ Data Types & Variables
โผ Operators & Expressions
โผ Control Flow (if, loops)
๐ 2. Functions & Modules
โผ Defining Functions
โผ Lambda Functions
โผ Importing & Creating Modules
๐ 3. File Handling
โผ Reading & Writing Files
โผ Working with CSV & JSON
๐ 4. Object-Oriented Programming (OOP)
โผ Classes & Objects
โผ Inheritance & Polymorphism
โผ Encapsulation
๐ 5. Exception Handling
โผ Try-Except Blocks
โผ Custom Exceptions
๐ 6. Advanced Python Concepts
โผ List & Dictionary Comprehensions
โผ Generators & Iterators
โผ Decorators
๐ 7. Essential Libraries
โผ NumPy (Arrays & Computations)
โผ Pandas (Data Analysis)
โผ Matplotlib & Seaborn (Visualization)
๐ 8. Web Development & APIs
โผ Web Scraping (BeautifulSoup, Scrapy)
โผ API Integration (Requests)
โผ Flask & Django (Backend Development)
๐ 9. Automation & Scripting
โผ Automating Tasks with Python
โผ Working with Selenium & PyAutoGUI
๐ 10. Data Science & Machine Learning
โผ Data Cleaning & Preprocessing
โผ Scikit-Learn (ML Algorithms)
โผ TensorFlow & PyTorch (Deep Learning)
๐ 11. Projects
โผ Build Real-World Applications
โผ Showcase on GitHub
๐ 12. โ Apply for Jobs
โผ Strengthen Resume & Portfolio
โผ Prepare for Technical Interviews
Like for more โค๏ธ๐ช
๐ 1. Basics
โผ Data Types & Variables
โผ Operators & Expressions
โผ Control Flow (if, loops)
๐ 2. Functions & Modules
โผ Defining Functions
โผ Lambda Functions
โผ Importing & Creating Modules
๐ 3. File Handling
โผ Reading & Writing Files
โผ Working with CSV & JSON
๐ 4. Object-Oriented Programming (OOP)
โผ Classes & Objects
โผ Inheritance & Polymorphism
โผ Encapsulation
๐ 5. Exception Handling
โผ Try-Except Blocks
โผ Custom Exceptions
๐ 6. Advanced Python Concepts
โผ List & Dictionary Comprehensions
โผ Generators & Iterators
โผ Decorators
๐ 7. Essential Libraries
โผ NumPy (Arrays & Computations)
โผ Pandas (Data Analysis)
โผ Matplotlib & Seaborn (Visualization)
๐ 8. Web Development & APIs
โผ Web Scraping (BeautifulSoup, Scrapy)
โผ API Integration (Requests)
โผ Flask & Django (Backend Development)
๐ 9. Automation & Scripting
โผ Automating Tasks with Python
โผ Working with Selenium & PyAutoGUI
๐ 10. Data Science & Machine Learning
โผ Data Cleaning & Preprocessing
โผ Scikit-Learn (ML Algorithms)
โผ TensorFlow & PyTorch (Deep Learning)
๐ 11. Projects
โผ Build Real-World Applications
โผ Showcase on GitHub
๐ 12. โ Apply for Jobs
โผ Strengthen Resume & Portfolio
โผ Prepare for Technical Interviews
Like for more โค๏ธ๐ช
๐8
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Drawing Beautiful Design Using Python
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from turtle import *
import turtle as t
def my_turtle():
# Choices
sides = str(3)
loops = str(450)
pen = 1
for i in range(int(loops)):
forward(i * 2/int(sides) + i)
left(360/int(sides) + .350)
hideturtle()
pensize(pen)
speed(30)
my_turtle()
t.done()
๐2
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TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
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Python Most Important Interview Questions
Question 1: Calculate the average stock price for Company X over the last 6 months.
Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data.
Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year.
Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X.
Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question
6: Compare the average stock price of Companies X and Z in the first quarter of the year.
#Data#
----------------------------------------------
import pandas as pd
data = { 'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'), 'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180), 'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180), 'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) }
df = pd.DataFrame(data)
Question 1: Calculate the average stock price for Company X over the last 6 months.
Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data.
Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year.
Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X.
Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question
6: Compare the average stock price of Companies X and Z in the first quarter of the year.
#Data#
----------------------------------------------
import pandas as pd
data = { 'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'), 'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180), 'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180), 'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) }
df = pd.DataFrame(data)
๐2
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