Tools to crack your data science Interviews
A histogram is a bar graph that uses the height of the bar to convey the frequency of an event occurring. Each bar in a histogram corresponds to the frequency of occurrence of a specific event. A Pareto chart displays bars by the height of the bars, signifying the order of impact. It follows the Pareto philosophy (the 80/20 rule) through…
ACID properties are important in an RDBMS setting where operations are transnational and there are database updates involved as a part of the task. For instance a banking or an e-commerce application where real-time user data is updated typically needs an RDBMS. A data analyst typically handles structured data using query languages such as SQL. However,…
Here are the different types of the JOINs in SQL: (INNER) JOIN: Returns records that have matching values in both tables LEFT (OUTER) JOIN: Returns ALL records from the left table, and the matched records from the right table RIGHT (OUTER) JOIN: Returns ALL records from the right table, and the matched records from the…
Some of the problems encountered by a data analyst are : Biased Data : Data could be biased due to the source from which it is collected. For instance, suppose you collect data to determine the winner of an electoral campaign, collecting from a specific region alone introduces one form of a bias, while collecting…
In Supervised Learning the algorithm learns from labeled training data. In other words, each data point is tagged with the answer or the label the algorithm should come up with. Using such labeled data, the goal is to predict labels for new data points. The two common forms of supervised learning are classification and regression….
Deep learning algorithms are capable of learning arbitrarily complex non-linear functions by using a deep enough and a wide enough network with the appropriate non-linear activation function. Traditional ML algorithms often require feature engineering of finding the subset of meaningful features to use. Deep learning algorithms often avoid the need for the feature engineering step….
A common pre-processing step is to normalize/rescale inputs so that they are not too high or low. However, even on normalized inputs, overflows and underflows can occur: Underflow: Joint probability distribution often involves multiplying small individual probabilities. Many probabilistic algorithms involve multiplying probabilities of individual data points that leads to underflow. Example : Suppose you…
It is important to get comfortable dealing with data as a data scientist. One might have done a PhD and have learnt many statistical techniques. HOWEVER: Given a problem, first try to think how you can solve the problem – Data Science or no data science. Try to spend time visualizing data in a different…
Runtime considerations are often important for many applications. Typically you should look at training time and prediction time for an ML algorithm. Some common questions to ask include: Training: Do you want to train the algorithm in a batch mode? How often do you need to train? If you need to retrain your algorithm every…
Missing data is caused either due to issues in data collection or sometimes, the data model could allow for missing data (for instance, the field ‘maximum credit limit on any of your cards’ might not make sense for someone who has no credit cards…). With missing data, typically the ML algorithm implementation might fail with…