Climate Connect Digital Interview Questions, Process, and Tips

Q1. Bassic angular question
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Q1. Normal behaviourla Question
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Q1. Matching brackets - Stack Question
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Q2. High Level Design of Twitter with APIs
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Twitter is a social media platform where users can post and interact with short messages called tweets.
• Twitter allows users to create an account and follow other users.

• Users can post tweets, which can include text, images, and videos.

• Tweets can be liked, retweeted, and replied to by other users.

• Twitter provides APIs for developers to access and interact with its platform.

• APIs include endpoints for user authentication,
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Q1. About cnn, rnn and lstm
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Q2. About statistical modelling, various statistical parameters, hypothesis testing, clustering
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Exploratory Data analysis on time series dataset

Q1. Difference between inferential statistics and descriptive statistics.
Ans. 
Inferential statistics infers properties of a population from a sample, while descriptive statistics describes the sample itself.
• Descriptive statistics summarizes and organizes data, while inferential statistics makes predictions and inferences about a larger population based on a sample.

• Descriptive statistics includes measures of central tendency (mean, median, mode) and measures of variability (range, standard deviat...read more
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Q2. How to check for stationarity.
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To check for stationarity, we need to look for constant mean, variance, and autocovariance over time.
• Check for constant mean by plotting rolling statistics and performing Dickey-Fuller test.

• Check for constant variance by plotting the moving average of the squared series and performing statistical tests.

• Check for constant autocovariance by plotting autocorrelation function (ACF) and partial autocorrelation function (PAC...read more
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Q3. Techniques to deal with missing values in time series data.
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Techniques to handle missing values in time series data.
• Imputation using mean, median or mode of the previous or next values.

• Interpolation using linear or spline methods.

• Extrapolation using regression models.

• Dropping missing values if they are insignificant in number.

• Using deep learning models like LSTM to predict missing values.
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Q4. Explain all the steps you will take to build a regression model given a time series dataset.
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To build a regression model for a time series dataset, several steps need to be followed.
• Preprocess the data by checking for missing values, outliers, and transforming the data if necessary.

• Split the data into training and testing sets.

• Select a suitable regression algorithm such as linear regression, decision trees, or neural networks.

• Train the model on the training set and evaluate its performance on the testing set.

• T...read more
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