Numbers don't lie data analytics is on rise soon it will be integrate part of all the organizations. Data science has created so much hype in the world of IT sectors that from big to small companies all are now hiring employees who have knowledge regarding this subject. Data science helps an employee to understand data and then synthesize it in a proper way so that they can communicate in a better way which is beneficial for the companies.
What is data science?
The person who is very good in understanding the computer algorithms and understand the statistics and mathematical ideas and applying these to the knowledge's from the computer science and mathematics into a particular application. Where somebody sees the value coming out from the data is called data science.
Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. And by combining aspects of statistics, computer science, applied mathematics and visualization, data science can turn the vast amounts of data the digital age generates into new insights and new knowledge.
Hence, the data science or data-driven science is about asking the right questions and exploring the data after this we do modeling of the data using various algorithms and finally communicating and visualizing the results thereof. Following are the some generic questions asked in data science interview:
What are the important skills to have in Python with regard to data analysis?
This is the second question which people might ask that is there any important skill to become data scientists. But the data scientists must have the knowledge of the Python language and R language s well. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing". A Python has established its ground very strongly in giving a robust framework for designing the data science solutions.
The following are some of the important skills to possess which will come handy when performing data analysis using Python:
Good Understanding of the built in data types especially lists, dictionaries, tuples and sets.
Mastery of N-dimensional NumPy arrays.
Mastery of pandas data frames.
Ability to perform element-wise vector and matrix operations on NumPy arrays. This requires the biggest shift in mindset for someone coming from a traditional software development background that's used to for loops.
Knowing that you should use the Anaconda distribution and the conda package manager.
Ability to write efficient list comprehensions instead of traditional for loops.
Ability to write small, clean functions preferably pure functions that doesn't alter objects.
Knowing how to profile the performance of a python script and how to optimize bottlenecks.
What is Selection Bias?
When people are doing any sort of data analysis they normally face something we know by the name called selection bias. The fundamental place where you start ding data analysis is by selecting a representative sample so that's where we like doing normally analysis.
Selection bias is the bias introduced by the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring the the sample obtained is not representative of the population intended to be analyzed. It is sometimes referred to as the selection effect. It is the distortion of a statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may not be accurate.
So selection bias is a particular sort of characteristics while you are doing in the sampling on a large sample of data. Example, if we want to do exit poll analysis of a particular election this biasness will happen because we take some samples out of the whole population.
The types of selection bias include:
1. Sampling Bias: It is a systematic error due to non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample.
2. Time Interval: A trial may be terminated early at an extreme value, but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.
3. Data: When specific subset of data is chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to previously stated or generally agreed criteria.
4. Attrition: Attrition bias is a kind of selection bias caused by attrition (loss of participants) discounting trial subjects/ tests that did not run to completion.
Following are the other questions which are asked in the interview of data science:
Doing any sort of data with the structured information, which means there are many rows and columns and it looks more a tabular data. So such a data in place there are two different formats like one is long and another one is wide. In the wide format, a subject's repeated responses will be in a single row and each response is in a separate column. In long format, each row is one time point per subject. After this the data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. Then we do A/B testing, which is quite popular approach particularly who are working with product. The goal of A/B testing is to identify any changes to the web page to maximize or increase the outcome of an interest. An example for this could be identifying the click through rate for a banner ad.
In statistics and machine learning, one of the most common tasks is to fit a "model" to a set of training data, so as to be able to make reliable predictions on general untrained data. Thus all these questions and analysis comes under the part of statistics question.
Data Analytics Questions:
For data analytics which language we prefer more like Python or R. Python would be the best option because it has Pandas library that provides easy to use data structures and high performance data analysis tools. R is more suitable for machine leaning than just text analysis. Python performs faster for all types of text analytics. Here various analytics method is used as more clearly explained in the video with examples.
Machine Learning Questions:
Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data. It is closely related to computational statistics, used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics. Various questions related to machine learning are asked in the data science interview for more insights watch video.
Under this, various situational basis questions are asked from the data scientists. And all the data scientists must know the answers of the most popular questions which are asked in the data science interview. Like how can you generate a random number between one to seven with only one die and more questions are also asked in the interview. For more insights watch video.
Hence, a data scientist is someone who is better at statistics. Yes, Data Science is on pace and the hottest demanding role right now both form companies and employee's perspective hence it is the highest paid field to get into.