Top 8 skills required to become Data Analyst
1- Excel
2- SQL
3- PowerBI or Tableau
4- Communication and Presentation skills
5- Domain knowledge
6- Programmming language like python or R
7- Basic maths and Statistics knowledge
8- Problem solving
1- Excel
2- SQL
3- PowerBI or Tableau
4- Communication and Presentation skills
5- Domain knowledge
6- Programmming language like python or R
7- Basic maths and Statistics knowledge
8- Problem solving
Interview Question
Define F-test. Where would you use it?
An F-test is any statistical hypothesis test where the test statistic follows an F-
distribution under the null hypothesis. If you have 2 models that have been fitted to
a dataset, you can use F-test to identify the model which best fits the sample
population.
Define F-test. Where would you use it?
An F-test is any statistical hypothesis test where the test statistic follows an F-
distribution under the null hypothesis. If you have 2 models that have been fitted to
a dataset, you can use F-test to identify the model which best fits the sample
population.
Interview Question
Define Sampling. Why do we need it?
Sampling is a process of choosing a subset from a target population which would
serve as its representative. We use the data from the sample to understand the
pattern in the population as a whole. Sampling is necessary because often we can
not gather or process the complete data within a reasonable time. There are many
ways to perform sampling. Some of the most commonly used techniques are
Random Sampling, Stratified Sampling, and Clustering Sampling.
Define Sampling. Why do we need it?
Sampling is a process of choosing a subset from a target population which would
serve as its representative. We use the data from the sample to understand the
pattern in the population as a whole. Sampling is necessary because often we can
not gather or process the complete data within a reasonable time. There are many
ways to perform sampling. Some of the most commonly used techniques are
Random Sampling, Stratified Sampling, and Clustering Sampling.
Interview Question
Define Confidence interval.
A confidence interval is an interval estimate which is likely to include an
unknown population parameter, the estimated range being calculated from the
given sample dataset. It simply means the range of values for which you are
completely sure that the true value of your variable would lie in.
Define Confidence interval.
A confidence interval is an interval estimate which is likely to include an
unknown population parameter, the estimated range being calculated from the
given sample dataset. It simply means the range of values for which you are
completely sure that the true value of your variable would lie in.
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How to become a data scientist after completing UG on CS engineering
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Why to study machine learning
Why Study Algorithms?
Computer scientists learn by experience. We learn by seeing others solve problems and by solving problems by ourselves. Being exposed to different problem-solving techniques and seeing how different algorithms are designed helps us to take on the next challenging problem that we are given. By considering a number of different algorithms, we can begin to develop pattern recognition so that the next time a similar problem arises, we are better able to solve it.
Algorithms are often quite different from one another. Consider the example of sqrt seen earlier. It is entirely possible that there are many different ways to implement the details to compute the square root function. One algorithm may use many fewer resources than another. One algorithm might take 10 times as long to return the result as the other. We would like to have some way to compare these two solutions. Even though they both work, one is perhaps “better” than the other. We might suggest that one is more efficient or that one simply works faster or uses less memory. As we study algorithms, we can learn analysis techniques that allow us to compare and contrast solutions based solely on their own characteristics, not the characteristics of the program or computer used to implement them.
Why Study Algorithms?
Computer scientists learn by experience. We learn by seeing others solve problems and by solving problems by ourselves. Being exposed to different problem-solving techniques and seeing how different algorithms are designed helps us to take on the next challenging problem that we are given. By considering a number of different algorithms, we can begin to develop pattern recognition so that the next time a similar problem arises, we are better able to solve it.
Algorithms are often quite different from one another. Consider the example of sqrt seen earlier. It is entirely possible that there are many different ways to implement the details to compute the square root function. One algorithm may use many fewer resources than another. One algorithm might take 10 times as long to return the result as the other. We would like to have some way to compare these two solutions. Even though they both work, one is perhaps “better” than the other. We might suggest that one is more efficient or that one simply works faster or uses less memory. As we study algorithms, we can learn analysis techniques that allow us to compare and contrast solutions based solely on their own characteristics, not the characteristics of the program or computer used to implement them.
Interview Question
Q. How is k-Nearest Neighbors (k-NN) different from k-Means algorithm?
A. The fundamental difference between these algorithms is that k-NN
is a Supervised algorithm whereas k-means is Unsupervised in nature.
B. k-NN is a Classification (or Regression) algorithm and k-means is a
Clustering algorithm.
C. k-NN tries to classify an observation based on its "k" surrounding
neighbors. It is also known as a lazy learner because it does absolutely
nothing at the training stage. On the other hand, k-means algorithm partitions
the training data set into different clusters such that all the data points in a
cluster are closer to each other than the data points from other clusters. The
algorithm tries to maintain enough separability between these clusters.
Q. How is k-Nearest Neighbors (k-NN) different from k-Means algorithm?
A. The fundamental difference between these algorithms is that k-NN
is a Supervised algorithm whereas k-means is Unsupervised in nature.
B. k-NN is a Classification (or Regression) algorithm and k-means is a
Clustering algorithm.
C. k-NN tries to classify an observation based on its "k" surrounding
neighbors. It is also known as a lazy learner because it does absolutely
nothing at the training stage. On the other hand, k-means algorithm partitions
the training data set into different clusters such that all the data points in a
cluster are closer to each other than the data points from other clusters. The
algorithm tries to maintain enough separability between these clusters.
Interview Question
Define Sampling. Why do we need it?
Sampling is a process of choosing a subset from a target population which would
serve as its representative. We use the data from the sample to understand the
pattern in the population as a whole. Sampling is necessary because often we can
not gather or process the complete data within a reasonable time. There are many
ways to perform sampling. Some of the most commonly used techniques are
Random Sampling, Stratified Sampling, and Clustering Sampling.
Define Sampling. Why do we need it?
Sampling is a process of choosing a subset from a target population which would
serve as its representative. We use the data from the sample to understand the
pattern in the population as a whole. Sampling is necessary because often we can
not gather or process the complete data within a reasonable time. There are many
ways to perform sampling. Some of the most commonly used techniques are
Random Sampling, Stratified Sampling, and Clustering Sampling.