Here are the 50 JavaScript interview questions for 2024

1. What is JavaScript?
2. What are the data types in JavaScript?
3. What is the difference between null and undefined?
4. Explain the concept of hoisting in JavaScript.
5. What is a closure in JavaScript?
6. What is the difference between “==” and “===” operators in JavaScript?
7. Explain the concept of prototypal inheritance in JavaScript.
8. What are the different ways to define a function in JavaScript?
9. How does event delegation work in JavaScript?
10. What is the purpose of the “this” keyword in JavaScript?
11. What are the different ways to create objects in JavaScript?
12. Explain the concept of callback functions in JavaScript.
13. What is event bubbling and event capturing in JavaScript?
14. What is the purpose of the “bind” method in JavaScript?
15. Explain the concept of AJAX in JavaScript.
16. What is the “typeof” operator used for?
17. How does JavaScript handle errors and exceptions?
18. Explain the concept of event-driven programming in JavaScript.
19. What is the purpose of the “async” and “await” keywords in JavaScript?
20. What is the difference between a deep copy and a shallow copy in JavaScript?
21. How does JavaScript handle memory management?
22. Explain the concept of event loop in JavaScript.
23. What is the purpose of the “map” method in JavaScript?
24. What is a promise in JavaScript?
25. How do you handle errors in promises?
26. Explain the concept of currying in JavaScript.
27. What is the purpose of the “reduce” method in JavaScript?
28. What is the difference between “null” and “undefined” in JavaScript?
29. What are the different types of loops in JavaScript?
30. What is the difference between “let,” “const,” and “var” in JavaScript?
31. Explain the concept of event propagation in JavaScript.
32. What are the different ways to manipulate the DOM in JavaScript?
33. What is the purpose of the “localStorage” and “sessionStorage” objects?
34. How do you handle asynchronous operations in JavaScript?
35. What is the purpose of the “forEach” method in JavaScript?
36. What are the differences between “let” and “var” in JavaScript?
37. Explain the concept of memoization in JavaScript.
38. What is the purpose of the “splice” method in JavaScript arrays?
39. What is a generator function in JavaScript?
40. How does JavaScript handle variable scoping?
41. What is the purpose of the “split” method in JavaScript?
42. What is the difference between a deep clone and a shallow clone of an object?
43. Explain the concept of the event delegation pattern.
44. What are the differences between JavaScript’s “null” and “undefined”?
45. What is the purpose of the “arguments” object in JavaScript?
46. What are the different ways to define methods in JavaScript objects?
47. Explain the concept of memoization and its benefits.
48. What is the difference between “slice” and “splice” in JavaScript arrays?
49. What is the purpose of the “apply” and “call” methods in JavaScript?
50. Explain the concept of the event loop in JavaScript and how it handles asynchronous operations.

5 Sites to Level Up Your Coding Skills 👨‍💻👩‍💻

🔹 leetcode.com
🔹 hackerrank.com
🔹 w3schools.com
🔹 datasimplifier.com
🔹 hackerearth.com

Best cold email technique to network with the recruiter for the future opportunities 👇👇

Interview Mail Tips-

You can achieve this by sending thoughtful emails.

✅ 𝗔𝗽𝗽𝗹𝘆𝗶𝗻𝗴 𝗳𝗼𝗿 𝗷𝗼𝗯 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Application for [Job Title] - [Your Name]

Dear [Hiring Manager's Name],

I hope this message finds you well. I am writing to express my interest in the [Job Title] position at [Company Name] that I recently came across. I believe my skills and experience align well with the requirements of the role.

With a background in [Relevant Skills/Experience], I am excited about the opportunity to contribute to [Company Name]'s [specific project/department/goal], and I am confident in my ability to make a positive impact. I have attached my resume for your consideration.

I would appreciate the chance to discuss how my background and expertise could benefit your team. Please let me know if there is a convenient time for a call or a meeting.

Thank you for considering my application. I look forward to the opportunity to speak with you.

Best regards,
[Your Name]

✅ 𝗙𝗼𝗹𝗹𝗼𝘄-𝗨𝗽 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Follow-Up on My Interview

Hi [Hiring Manager's Name],

I hope you're doing well. I wanted to follow up on the interview we had for the [Job Title] position at [Company Name]. I'm really excited about the opportunity and would love to hear about the next steps in the process.

Looking forward to your response.

Best regards,
[Your Name]

✅ 𝗥𝗲𝗷𝗲𝗰𝘁𝗶𝗼𝗻 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Appreciation and Future Consideration

Hi [Hiring Manager's Name],

I hope this message finds you well. I wanted to express my gratitude for considering me for the [Job Title] position. Although I didn't make it to the next round, I'm thankful for the chance to learn about [Company Name]. I look forward to potentially crossing paths again in the future.

Thank you once again.

Best regards,
[Your Name]

✅ 𝗔𝗰𝗰𝗲𝗽𝘁𝗮𝗻𝗰𝗲 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Accepting the [Job Title] Position

Hello [Hiring Manager's Name],

I hope you're doing well. I wanted to formally accept the offer for the [Job Title] position at [Company Name]. I'm really excited about joining the team and contributing to [Company Name]'s success. Please let me know the next steps and any additional information you need from my end.

Thank you and looking forward to starting on [Start Date].

Best regards,
[Your Name]

✅ 𝗦𝗮𝗹𝗮𝗿𝘆 𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Salary Discussion for [Job Title] Position

Hello [Hiring Manager's Name],

I hope this message finds you well. I'm excited about the offer for the [Job Title] role at [Company Name]. I would like to discuss the compensation package to ensure that it aligns with my skills and experience. Could we set up a time to talk about this further?

Thank you and looking forward to your response.

Best regards,
[Your Name]

(Tap to copy)

Like this post if you need similar content in this channel 😄❤️

Python Learning Series Part-7

Complete Python Topics for Data Analysis:

Scikit-Learn:

Scikit-Learn is a machine learning library that provides simple and efficient tools for data analysis and modeling. It includes various algorithms for classification, regression, clustering, and more.

1. Introduction to Machine Learning:
   - Supervised Learning vs. Unsupervised Learning:
     - Supervised learning involves training a model on a labeled dataset, while unsupervised learning deals with unlabeled data.

   - Classification and Regression:
     - Classification predicts categories (e.g., spam or not spam), while regression predicts continuous values (e.g., house prices).

2. Supervised Learning Algorithms:
   - Linear Regression:
     - Predicts a continuous outcome based on one or more predictor variables.
     

       from sklearn.linear_model import LinearRegression

       model = LinearRegression()
       model.fit(X_train, y_train)
       predictions = model.predict(X_test)
       

   - Decision Trees and Random Forest:
     - Decision trees make decisions based on features, while random forests use multiple trees for better accuracy.
     

       from sklearn.tree import DecisionTreeClassifier
       from sklearn.ensemble import RandomForestClassifier

       model_tree = DecisionTreeClassifier()
       model_forest = RandomForestClassifier()
       

3. Model Evaluation and Validation:
   - Train-Test Split:
     - Splitting the dataset into training and testing sets.
     

       from sklearn.model_selection import train_test_split

       X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
       

   - Model Evaluation Metrics:
     - Using metrics like accuracy, precision, recall, and F1-score to evaluate model performance.
     

       from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

       accuracy = accuracy_score(y_true, y_pred)
       precision = precision_score(y_true, y_pred)
       

4. Unsupervised Learning Algorithms:
   - K-Means Clustering:
     - Divides data into K clusters based on similarity.
     

       from sklearn.cluster import KMeans

       kmeans = KMeans(n_clusters=3)
       kmeans.fit(X)
       clusters = kmeans.labels_
       

   - Principal Component Analysis (PCA):
     - Reduces dimensionality while retaining essential information.
     

       from sklearn.decomposition import PCA

       pca = PCA(n_components=2)
       transformed_data = pca.fit_transform(X)
       

Scikit-Learn is a powerful tool for machine learning tasks, offering a wide range of algorithms and tools for model evaluation.


Hope it helps :)

Python Learning Series Part-8


8. Time Series Analysis:

Time series analysis deals with data collected or recorded over time. It is widely used in various fields, such as finance, economics, and environmental science, to analyze trends, patterns, and make predictions.

1. Working with Time Series Data:
   - Datetime Index:
     - Use pandas to set a datetime index for time series data.
     

       df['Date'] = pd.to_datetime(df['Date'])
       df.set_index('Date', inplace=True)
       

   - Resampling:
     - Change the frequency of the time series data (e.g., daily to monthly).
     

       df.resample('M').mean()
       

2. Seasonality and Trend Analysis:
   - Decomposition:
     - Decompose time series data into trend, seasonal, and residual components.
     

       from statsmodels.tsa.seasonal import seasonal_decompose

       result = seasonal_decompose(df['Value'], model='multiplicative')
       

   - Moving Averages:
     - Smooth out fluctuations in time series data.
     

       df['MA'] = df['Value'].rolling(window=3).mean()
       

3. Forecasting Techniques:
   - Autoregressive Integrated Moving Average (ARIMA):
     - A popular model for time series forecasting.
     

       from statsmodels.tsa.arima.model import ARIMA

       model = ARIMA(df['Value'], order=(1,1,1))
       results = model.fit()
       forecast = results.forecast(steps=5)
       

   - Exponential Smoothing (ETS):
     - Another method for forecasting time series data.
     

       from statsmodels.tsa.holtwinters import ExponentialSmoothing

       model = ExponentialSmoothing(df['Value'], seasonal='add', seasonal_periods=12)
       results = model.fit()
       forecast = results.predict(start=len(df), end=len(df)+4)
       

Time series analysis is crucial for understanding patterns over time and making predictions.


Hope it helps :)

Python Learning Series Part-9

Web Scraping with BeautifulSoup and Requests:

Web scraping involves extracting data from websites. BeautifulSoup is a Python library for pulling data out of HTML and XML files, and the Requests library is used to send HTTP requests.

1. Extracting Data from Websites:
   - Installation:
     - Install BeautifulSoup and Requests using:
     

       pip install beautifulsoup4
       pip install requests
       

   - Making HTTP Requests:
     - Use the Requests library to send GET requests to a website.
     

       import requests

       response = requests.get('https://example.com')
       

2. Parsing HTML with BeautifulSoup:
   - Creating a BeautifulSoup Object:
     - Parse the HTML content of a webpage.
     

       from bs4 import BeautifulSoup

       soup = BeautifulSoup(response.text, 'html.parser')
       

   - Navigating the HTML Tree:
     - Use BeautifulSoup methods to navigate and extract data from HTML elements.
     

       title = soup.title
       paragraphs = soup.find_all('p')
       

3. Scraping Data from a Website:
   - Extracting Text:
     - Get the text content of HTML elements.
     

       title_text = soup.title.text
       paragraph_text = soup.find('p').text
       

   - Extracting Attributes:
     - Retrieve specific attributes of HTML elements.
     

       image_url = soup.find('img')['src']
       

4. Handling Multiple Pages and Dynamic Content:
   - Pagination:
     - Iterate through multiple pages by modifying the URL.
     

       for page in range(1, 6):
           url = f'https://example.com/page/{page}'
           response = requests.get(url)
           # Process the page content
       

   - Dynamic Content:
     - Use tools like Selenium for websites with dynamic content loaded by JavaScript.

Web scraping is a powerful technique for collecting data from the web, but it's important to be aware of legal and ethical considerations.


Hope it helps :)

Python Learning Series Part-9


Web Scraping with BeautifulSoup and Requests:

Web scraping involves extracting data from websites. BeautifulSoup is a Python library for pulling data out of HTML and XML files, and the Requests library is used to send HTTP requests.

1. Extracting Data from Websites:
   - Installation:
     - Install BeautifulSoup and Requests using:
     

       pip install beautifulsoup4
       pip install requests
       

   - Making HTTP Requests:
     - Use the Requests library to send GET requests to a website.
     

       import requests

       response = requests.get('https://example.com')
       

2. Parsing HTML with BeautifulSoup:
   - Creating a BeautifulSoup Object:
     - Parse the HTML content of a webpage.
     

       from bs4 import BeautifulSoup

       soup = BeautifulSoup(response.text, 'html.parser')
       

   - Navigating the HTML Tree:
     - Use BeautifulSoup methods to navigate and extract data from HTML elements.
     

       title = soup.title
       paragraphs = soup.find_all('p')
       

3. Scraping Data from a Website:
   - Extracting Text:
     - Get the text content of HTML elements.
     

       title_text = soup.title.text
       paragraph_text = soup.find('p').text
       

   - Extracting Attributes:
     - Retrieve specific attributes of HTML elements.
     

       image_url = soup.find('img')['src']
       

4. Handling Multiple Pages and Dynamic Content:
   - Pagination:
     - Iterate through multiple pages by modifying the URL.
     

       for page in range(1, 6):
           url = f'https://example.com/page/{page}'
           response = requests.get(url)
           # Process the page content
       

   - Dynamic Content:
     - Use tools like Selenium for websites with dynamic content loaded by JavaScript.

Web scraping is a powerful technique for collecting data from the web, but it's important to be aware of legal and ethical considerations.


Hope it helps :)

Python Learning Series Part-10


SQL for Data Analysis:

Structured Query Language (SQL) is a powerful language for managing and manipulating relational databases. Understanding SQL is crucial for working with databases and extracting relevant information for data analysis.

1. Basic SQL Commands:
   - SELECT Statement:
     - Retrieve data from one or more tables.
     

       SELECT column1, column2 FROM table_name WHERE condition;
       

   - INSERT Statement:
     - Insert new records into a table.
     

       INSERT INTO table_name (column1, column2) VALUES (value1, value2);
       

   - UPDATE Statement:
     - Modify existing records in a table.
     

       UPDATE table_name SET column1 = value1 WHERE condition;
       

   - DELETE Statement:
     - Remove records from a table.
     

       DELETE FROM table_name WHERE condition;
       

2. Data Filtering and Sorting:
   - WHERE Clause:
     - Filter data based on specified conditions.
     

       SELECT * FROM employees WHERE department = 'Sales';
       

   - ORDER BY Clause:
     - Sort the result set in ascending or descending order.
     

       SELECT * FROM products ORDER BY price DESC;
       

3. Aggregate Functions:
   - SUM, AVG, MIN, MAX, COUNT:
     - Perform calculations on groups of rows.
     

       SELECT AVG(salary) FROM employees WHERE department = 'Marketing';
       

4. Joins and Relationships:
   - INNER JOIN, LEFT JOIN, RIGHT JOIN:
     - Combine rows from two or more tables based on a related column.
     

       SELECT employees.name, departments.department_name
       FROM employees
       INNER JOIN departments ON employees.department_id = departments.department_id;
       

   - Primary and Foreign Keys:
     - Establish relationships between tables for efficient data retrieval.
     

       CREATE TABLE employees (
           employee_id INT PRIMARY KEY,
           name VARCHAR(50),
           department_id INT FOREIGN KEY REFERENCES departments(department_id)
       );
       

Understanding SQL is essential for working with databases, especially in scenarios where data is stored in relational databases like MySQL, PostgreSQL, or SQLite.


Hope it helps :)

Python Learning Series Part-11

Advanced Data Visualization:

Advanced data visualization goes beyond basic charts and explores more sophisticated techniques to represent data effectively.

1. Interactive Visualizations with Plotly:
   - Creating Interactive Plots:
     - Plotly provides a higher level of interactivity for charts.
     

       import plotly.express as px

       fig = px.scatter(df, x='X-axis', y='Y-axis', color='Category', size='Size', hover_data=['Details'])
       fig.show()
       

   - Dash for Web Applications:
     - Dash, built on top of Plotly, allows you to create interactive web applications with Python.
     

       import dash
       import dash_core_components as dcc
       import dash_html_components as html

       app = dash.Dash(__name__)

       app.layout = html.Div(children=[
           dcc.Graph(
               id='example-graph',
               figure=fig
           )
       ])

       if __name__ == '__main__':
           app.run_server(debug=True)
       

2. Geospatial Data Visualization:
   - Folium for Interactive Maps:
     - Folium is a Python wrapper for Leaflet.js, enabling the creation of interactive maps.
     

       import folium

       m = folium.Map(location=[latitude, longitude], zoom_start=10)
       folium.Marker(location=[point_latitude, point_longitude], popup='Marker').add_to(m)
       m.save('map.html')
       

   - Geopandas for Spatial Data:
     - Geopandas extends Pandas to handle spatial data and integrates with Matplotlib for visualization.
     

       import geopandas as gpd
       import matplotlib.pyplot as plt

       gdf = gpd.read_file('shapefile.shp')
       gdf.plot()
       plt.show()
       

3. Customizing Visualizations:
   - Matplotlib Customization:
     - Customize various aspects of Matplotlib plots for a polished look.
     

       plt.title('Customized Title', fontsize=16)
       plt.xlabel('X-axis Label', fontsize=12)
       plt.ylabel('Y-axis Label', fontsize=12)
       

   - Seaborn Themes:
     - Seaborn provides different themes to quickly change the overall appearance of plots.
     

       import seaborn as sns

       sns.set_theme(style='whitegrid')
       

Advanced visualization techniques help convey complex insights effectively.


Hope it helps :)

Interview QnA | Date: 01-04-2024
Company Name: Accenture
Role: Data Scientist
Topic: Silhouette, trend seasonality, bag of words, bagging boosting

1. What do you understand by the term silhouette coefficient?

The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score.


2. What is the difference between trend and seasonality in time series?

Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metric’s value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again.


3. What is Bag of Words in NLP?

Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order.


4. What is the difference between bagging and boosting?

Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm

+50 Git Use Cases.pdf

Thanks for the amazing response. Here are the answers to each question 👇👇

1. How do you reverse a string?
Example:

   def reverse_string(s):
       return s[::-1]
   
   print(reverse_string("hello"))  # Output: "olleh"
   

2. How do you determine if a string is a palindrome?
Example:

   def is_palindrome(s):
       return s == s[::-1]
   
   print(is_palindrome("radar"))  # Output: True
   

3. How do you calculate the number of numerical digits in a string?
Example:

   def count_digits(s):
       return sum(1 for char in s if char.isdigit())
   
   print(count_digits("abc123def456"))  # Output: 6

Interview QnA | 07-04-2024
Company - The Math Company
Role- Data Analyst

1.How to create filters in Power BI?

Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.

Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.

Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)


2.How to sort data in Power BI?

Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.

3.How to convert pdf to excel?

Open the PDF document you want to convert in XLSX format in Acrobat DC.

Go to the right pane and click on the “Export PDF” option.

Choose spreadsheet as the Export format.

Select “Microsoft Excel Workbook.”

Now click “Export.”

Download the converted file or share it.



4. How to enable macros in excel?

Click the file tab and then click “Options.”

A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”

Go to the “Macro Settings” and select “enable all macros.”

Click OK to apply the macro settings.

Python Learning Series Part-12

Complete Python Topics for Data Analysis:

Natural Language Processing (NLP)

Natural Language Processing involves working with human language data, enabling computers to understand, interpret, and generate human-like text.

1. Text Preprocessing:
   - Tokenization:
     - Break text into words or phrases (tokens).
     

       from nltk.tokenize import word_tokenize

       text = "Natural Language Processing is fascinating!"
       tokens = word_tokenize(text)
       

   - Stopword Removal:
     - Eliminate common words (stopwords) that often don't contribute much meaning.
     

       from nltk.corpus import stopwords

       stop_words = set(stopwords.words('english'))
       filtered_tokens = [word for word in tokens if word.lower() not in stop_words]
       

2. Text Analysis:
   - Frequency Analysis:
     - Analyze the frequency of words in a text.
     

       from nltk.probability import FreqDist

       freq_dist = FreqDist(filtered_tokens)
       

   - Word Clouds:
     - Visualize word frequency using a word cloud.
     

       from wordcloud import WordCloud
       import matplotlib.pyplot as plt

       wordcloud = WordCloud().generate_from_frequencies(freq_dist)
       plt.imshow(wordcloud, interpolation='bilinear')
       plt.axis("off")
       plt.show()
       

3. Sentiment Analysis:
   - VADER Sentiment Analysis:
     - Assess the sentiment (positive, negative, neutral) of a piece of text.
     

       from nltk.sentiment import SentimentIntensityAnalyzer

       analyzer = SentimentIntensityAnalyzer()
       sentiment_score = analyzer.polarity_scores("I love NLP!")
       

4. Named Entity Recognition (NER):
   - Spacy for NER:
     - Identify entities (names, locations, organizations) in text.
     

       import spacy

       nlp = spacy.load('en_core_web_sm')
       doc = nlp("Apple Inc. is headquartered in Cupertino.")
       for ent in doc.ents:
           print(ent.text, ent.label_)
       

5. Topic Modeling:
   - Latent Dirichlet Allocation (LDA):
     - Identify topics within a collection of text documents.
     

       from gensim import corpora, models

       dictionary = corpora.Dictionary(documents)
       corpus = [dictionary.doc2bow(text) for text in documents]
       lda_model = models.LdaModel(corpus, num_topics=3, id2word=dictionary)
     

Hope it helps :)