Data Science has been the buzz word of the IT field for the past few years. Courses like data science course from Imarticus will equip you with all the skills required for a data science job. However, to ace the interviews for data science jobs, you should be well versed with the basic components of statistics too. This article discusses one of the key element in Data Science, statistics and its relevant topics to brush up before a data science job interview.
Preparing for Data science interviews
As in many interviews, the statistics are also going to start with technical questions. Many interviewers try to test your knowledge and communication skills by pretending to have no idea about the basic concepts and asking you to explain them. So, it is important to learn how to convey complex concepts without using the assumed knowledge.
Following are the few important topics you could brush off before attending the interview.
1. Statistical features
They are probably the most used statistics concept in data science. When you are exploring a dataset, the first technique you apply will be this. It includes the following features.
- Percentile and many others.
These features provide a quick, informative view of the data and are important to be familiar with.
2. Probability Distribution
A probability distribution is a function that represents the probabilities of occurrence of all possible values in the experiment. Data science use statistical inferences to predict trends from the data, and statistical inferences use probability distribution of data. So it is important to have proper knowledge of probability functions to work effectively on the data science problems. The important probability distributions in the data science perspective are the following.
- Uniform Distribution
- Normal Distribution
- Poisson Distribution
3. Dimensionality Reduction
It is the process of reducing the number of random variables under consideration by taking a set of principle variables. In Data Science, it is used to reduce the feature variables. It can result in huge savings on computer power.
The most commonly used statistical technique for dimensionality reduction is PCA or Principal component analysis.
4. Over and Under-Sampling
Over and Under Sampling are techniques used to solve the classification problems. It comes handy when one dataset is too large or small relative to the next. In real life data science problems, there will be large differences in the rarity of different classes of data. In such cases, it is this technique comes to your rescue.
5. Bayesian Statistics
Bayesian statistics is a special approach to applying probability to the statistical problems. It interprets probability as the confidence of an individual about the occurrence of some event to happen. Bayesian statistics take evidence to account.
These topics from statistics are very important for a Data Science job and make sure you learn more about them before your interview. You can also try various data science training in Mumbai to begin your career at right note. Genpact data science course from Imarticus is an excellent choice to learn more about data science. Check out and join the course immediately.