- 1.What is date science?
- 2.What is the difference between date analytics course and the date science course?
- 3.Is it mandatory to learn coding and statistics for data science?
- 4.What is the difference between big data analytics and big data of engineering?
- 5. Python or R-Which one would you prefer for text analysis?
- 6. What is sampling?
- 7)What are the types of sampling?
- 8.What is cluster sampling?
- 9.What is regression?What are it’s uses?
- 10. Why is resampling done?
- 11.What is bias?
- 12. What is selection bias?
- 13.What do you understand by automation bias?
- 14.What is back propagation?
- 15.What do you understand by the term normal distribution?
- 16. What is an Eigenvalue and eigenvector?
- 17. What is cross-validation?
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1.What is date science?
A)Data science involves exploitation machine-controlled ways to research huge amounts of information and to extract knowledge from them. By combining aspects of statistics, technology, mathematics and visualization, date science are often flip the huge amounts of knowledge, the digital age generates into new insights and new data.
2.What is the difference between date analytics course and the date science course?
A)The analytics course explains techniques for data analysis and communication using techniques like R, Tableau, and Excel.
The data science course focuses on process like data cleansing and processing, predictive modelling, statistical analysis, correlating incongruity date, visualization like python programming language, and topics like machine learning and deep learning.
3.Is it mandatory to learn coding and statistics for data science?
A)Yes, coding and statistics are among the viral skills for data scientists. Knowledge of math and statistics like linear algebra, calculus, probability, and so on is important to learn data science. Although in-depth of knowledge of software programming is not necessary. Having a fair understanding of basic programming tools like python into R will ease the learning process of data science.
4.What is the difference between big data analytics and big data of engineering?
A)Data analytics is the combination of data engineering and data science. There is a minor difference between the analytics and the engineering of data. The reason is the overlapping skills of the professionals in both fields, never the last following other basic differences.
The big data of engineering create platform for a big data analysis. They usually designed to develop and assimilate data from various resources. The chief responsibility of data engineers used to optimize the big data system. It includes the creation of the data warehouse to ease the data accessibility for analysis. Some of the frequently used tools for data engineering are Hadoop, NoSQL, map reduces and MySQL. Knowledge of ETL tools like stitch data or segment is immensely valuable amongst data engineering jobs. On the other hand, big data analytics mostly deals with collecting, manipulating and analyzing the data. The key task of data analysis is preparing reports. These reports could be presented through various formats like graphs, dashboards, charts, and infographics. Some of the viral and software querying and statistical languages include Matlabs, python, SQL, Hive, Pig, Excel, SAS, R, and SPSS. The key responsibility of data analytics is recognized to assess and implement services and tools from external sources, this is to help validation and cleansing.
5. Python or R-Which one would you prefer for text analysis?
A)Python would be the best option because it has Pandas library that provides easy to use data structures and high performance data analysis tools. Where as R is more suitable for machine learning than just test analysis. Python performs faster for all types of text analytics.
6. What is sampling?
A)Sampling is a process that involves taking or making a representative selection of the population and using the data collected as research information. In simple, the sample is a “subgroup of the population”.
7)What are the types of sampling?
A)There are two types of sampling.
|Probability sampling||Non-probability sampling|
|1. Simple random sampling||1. Convenience sampling|
|2. Systematic sampling||2. Purposive sampling/judgmental sampling|
|3. Stratified random sampling(proportionate and disproportionate)
|3. Quota sampling|
|4. Cluster sampling||4. Snowball Sampling|
|5. Area sampling|
8.What is cluster sampling?
A)Cluster sampling is a technique used when it becomes difficult to study the target population spread across a wide area and simple random sampling cannot be applied. Cluster sample is a probability sample where each sampling unit is a collection or cluster of elements.
9.What is regression?What are it’s uses?
A)Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent and independent variable.
There are 3 major uses for regression analysis:
- Determining the strength of predictor.
- Forecasting an effect, and
- Trend for casting.
10. Why is resampling done?
A) Resampling can be done in any of these cases:
Estimating the accuracy of sample statistics by using subsets of accessible data or drawing randomly with replacement from a set of data points.
Substituting labels on data points when performing significance tests.
Validating models by using random subsets (bootstrapping and cross-validation).
11.What is bias?
A)Bias is an intercept or offset from an origin. It is nothing but the amount by which the expected model prediction differs from the true value of the target or how far off our predictions are from real values. It always leads to a high error on training and test data. Model with high bias pays very little attention to the training data and oversimplifies the model.
12. What is selection bias?
A)Selection bias is introduced by the selection of individual, group or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed. 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.
13.What do you understand by automation bias?
A)When a human decision maker favors recommendation made by an automated decision-making system over information made without automation, even when the automated decision-making system makes errors.
14.What is back propagation?
A)The primary algorithm for performing gradient descent in neural networks. First, the output values of each node are calculated in a forward pass. The partial derivative of the error with respect to each parameter is calculated in a backward pass through the graph.
15.What do you understand by the term normal distribution?
A)Data is distributed by different ways with a bias to the left or to the right or it can all be jumbled up. The data can also be distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell shaped curve. The random variables are distributed in the form of an symmetrical bell shaped curve.
16. What is an Eigenvalue and eigenvector?
A)Eigenvalues can be used to as the strength of the information in the direction of eigenvector or the factor by which the compression occurs. Whereas, eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing and stretching. In data analysis, eigenvectors are calculated for a correlation or covariance matrix.
17. What is cross-validation?
A)Cross-validation is a model validation technique for evaluating how the outcomes of statistical analysis will generalize to an independent data set. It is mainly used in backgrounds where the objective is forcast and wants to estimate how accurately a model will accomplish in practice. The importance of cross-validation is to term a data set to test the model in the training phase in order to limit problems like over fiting and to get an insight on how the model will generalize to an independent data set.
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