Interview QnA | Date: 19-03-2024
Company - Google
Role- Jr.ML Engineer
Topics: Machine Learning

1.How will you handle missing values in data?

There are several ways to handle missing values in the given data-

1.Dropping the values

2.Deleting the observation (not always recommended).

3.Replacing value with the mean, median and mode of the observation.

4.Predicting value with regression

5.Finding appropriate value with clustering

2. What is SVM? Can you name some kernels used in SVM?

SVM stands for support vector machine. They are used for classification and prediction tasks. SVM consists of a separating plane that discriminates between the two classes of variables. This separating plane is known as hyperplane. Some of the kernels used in SVM are –

Polynomial Kernel
Gaussian Kernel
Laplace RBF Kernel
Sigmoid Kernel
Hyperbolic Kernel

3.What is market basket analysis?

Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.

4.What is the benefit of batch normalization?

The model is less sensitive to hyperparameter tuning.

High learning rates become acceptable, which results in faster training of the model.

Weight initialization becomes an easy task.

Using different non-linear activation functions becomes feasible.

Deep neural networks are simplified because of batch normalization.

It introduces mild regularisation in the network.

Python Learning Series Part-3


3. Pandas:

Pandas is a powerful library for data manipulation and analysis. It provides data structures like Series and DataFrame, making it easy to handle and analyze structured data.

1. Series and DataFrame Basics:
   - Series: A one-dimensional array with labels, akin to a column in a spreadsheet.
   

     import pandas as pd

     series_data = pd.Series([1, 3, 5, np.nan, 6, 8])
     

   - DataFrame: A two-dimensional table, similar to a spreadsheet or SQL table.
   

     df = pd.DataFrame({
         'Name': ['Alice', 'Bob', 'Charlie'],
         'Age': [25, 30, 35],
         'City': ['New York', 'San Francisco', 'Los Angeles']
     })
     

2. Data Cleaning and Manipulation:
   - Handling Missing Data: Pandas provides methods to handle missing values, like dropna() and fillna().
   

     df.dropna()  # Drop rows with missing values
     

   - Filtering and Selection: Selecting specific rows or columns based on conditions.
   

     adults = df[df['Age'] > 25]
     

   - Adding and Removing Columns:
   

     df['Salary'] = [50000, 60000, 75000]  # Adding a new column
     df.drop('City', axis=1, inplace=True)  # Removing a column
     

3. Grouping and Aggregation:
   - GroupBy: Grouping data based on some criteria.
   

     grouped_data = df.groupby('City')
     

   - Aggregation Functions: Computing summary statistics for each group.
   

     average_age = grouped_data['Age'].mean()
     

4. Pandas in Data Analysis:
   - Pandas is extensively used for data preparation, cleaning, and exploratory data analysis (EDA).
   - It seamlessly integrates with other libraries like NumPy and Matplotlib.

Here you can access Free Pandas Cheatsheet


Hope it helps :)

System Design.pdf

Python Learning Series Part-4

Complete Python Topics for Data Analysis:

4. Matplotlib and Seaborn:

Matplotlib is a popular data visualization library, and Seaborn is built on top of Matplotlib to enhance its capabilities and provide a high-level interface for attractive statistical graphics.

1. Data Visualization with Matplotlib:
   - Line Plots, Bar Charts, and Scatter Plots: Creating basic visualizations.
   

     import matplotlib.pyplot as plt

     x = [1, 2, 3, 4, 5]
     y = [2, 4, 6, 8, 10]

     plt.plot(x, y)  # Line plot
     plt.bar(x, y)   # Bar chart
     plt.scatter(x, y)  # Scatter plot
     plt.show()
     

   - Customizing Plots: Adding labels, titles, and customizing the appearance.
   

     plt.xlabel('X-axis Label')
     plt.ylabel('Y-axis Label')
     plt.title('Customized Plot')
     plt.grid(True)
     

2. Seaborn for Statistical Visualization:
   - Enhanced Heatmaps and Pair Plots: Seaborn provides more advanced visualizations.
   

     import seaborn as sns

     df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

     sns.heatmap(df, annot=True, cmap='coolwarm')  # Heatmap
     sns.pairplot(df)  # Pair plot
     

   - Categorical Plots: Visualizing relationships with categorical data.
   

     sns.barplot(x='Category', y='Value', data=df)
     

3. Data Visualization Best Practices:
   - Choosing the Right Plot Type: Selecting the appropriate visualization for your data.
   - Effective Use of Color and Labels: Making visualizations clear and understandable.

4. Advanced Visualization:
   - Interactive Plots with Plotly: Creating interactive plots for web-based dashboards.
   - Geospatial Data Visualization: Plotting data on maps using libraries like Geopandas.

Visualization is a crucial aspect of data analysis, helping to communicate insights effectively.


Hope it helps :)

Python Learning Series Part-5

Complete Python Topics for Data Analysis:

Data Cleaning and Preprocessing:

1. Handling Missing Data:
   - Identifying Missing Values:
   

     df.isnull()  # Boolean DataFrame indicating missing values
     

   - Dropping Missing Values:
   

     df.dropna()  # Drop rows with missing values
     

   - Filling Missing Values:
   

     df.fillna(value)  # Replace missing values with a specified value
     

2. Removing Duplicates:
   - Identifying Duplicates:
   

     df.duplicated()  # Boolean Series indicating duplicate rows
     

   - Removing Duplicates:
   

     df.drop_duplicates()  # Remove duplicate rows
     

3. Data Normalization and Scaling:
   - Min-Max Scaling:
   

     from sklearn.preprocessing import MinMaxScaler

     scaler = MinMaxScaler()
     df_scaled = scaler.fit_transform(df[['feature']])
     

   - Standardization:
   

     from sklearn.preprocessing import StandardScaler

     scaler = StandardScaler()
     df_standardized = scaler.fit_transform(df[['feature']])
     

4. Handling Categorical Data:
   - One-Hot Encoding:
   

     pd.get_dummies(df['categorical_column'])
     

   - Label Encoding:
   

     from sklearn.preprocessing import LabelEncoder

     label_encoder = LabelEncoder()
     df['encoded_column'] = label_encoder.fit_transform(df['categorical_column'])
     

Understanding data cleaning and preprocessing is crucial for ensuring the quality and suitability of your data for analysis.


Hope it helps :)

Data Analyst Interview Q&A.pdf

🔐"Key Python Libraries for Data Science:

Numpy: Core for numerical operations and array handling.

SciPy: Complements Numpy with scientific computing features like optimization.

Pandas: Crucial for data manipulation, offering powerful DataFrames.

Matplotlib: Versatile plotting library for creating various visualizations.

Keras: High-level neural networks API for quick deep learning prototyping.

TensorFlow: Popular open-source ML framework for building and training models.

Scikit-learn: Efficient tools for data mining and statistical modeling.

Seaborn: Enhances data visualization with appealing statistical graphics.

Statsmodels: Focuses on estimating and testing statistical models.

NLTK: Library for working with human language data.

These libraries empower data scientists across tasks, from preprocessing to advanced machine learning."

Python Learning Series Part-6

Complete Python Topics for Data Analysis:

6. Statistical Analysis with Python:

1. Descriptive Statistics:
   - Measures of Central Tendency:
     - Calculate mean, median, and mode to understand the central value of a dataset.
     

       mean_value = df['column'].mean()
       median_value = df['column'].median()
       mode_value = df['column'].mode()
       

   - Measures of Dispersion:
     - Assess variability with measures like standard deviation and range.
     

       std_dev = df['column'].std()
       data_range = df['column'].max() - df['column'].min()
       

2. Inferential Statistics and Hypothesis Testing:
   - T-Tests:
     - Compare means of two groups to assess if they are significantly different.
     

       from scipy.stats import ttest_ind

       group1 = df[df['group'] == 'A']['values']
       group2 = df[df['group'] == 'B']['values']

       t_stat, p_value = ttest_ind(group1, group2)
       

   - ANOVA (Analysis of Variance):
     - Assess differences among group means in a sample.
     

       from scipy.stats import f_oneway

       group1 = df[df['group'] == 'A']['values']
       group2 = df[df['group'] == 'B']['values']
       group3 = df[df['group'] == 'C']['values']

       f_stat, p_value = f_oneway(group1, group2, group3)
       

   - Correlation Analysis:
     - Measure the strength and direction of a linear relationship between two variables.
     

       correlation = df['variable1'].corr(df['variable2'])
       

Statistical analysis is crucial for drawing meaningful insights from data and making informed decisions.



Hope it helps :)

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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?
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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.

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