What is Data Analysis, in brief?
Data analysis is the structured procedure that involves working with data by performing activities such as ingestion, cleaning, transforming, and assessing it to provide insights, which can be used to drive revenue.
Data is collected, to begin with, from varied sources. Since the data is a raw entity, it has to be cleaned and processed to fill out missing values and to remove any entity that is out of the scope of usage.
After pre-processing the data, it can be analyzed with the help of models, which use the data to perform some analysis on it.
The last step involves reporting and ensuring that the data output is converted to a format that can also cater to a non-technical audience, alongside the analysts.
What are some of the problems that a working Data Analyst might encounter?
There can be many issues that a Data Analyst might face when working with data. Here are some of them:
- The accuracy of the model in development will be low if there are multiple entries of the same entity and errors concerning spellings and incorrect data.
- If the source the data being ingested from is not a verified source, then the data might require a lot of cleaning and preprocess before beginning the analysis.
- The same goes for when extracting data from multiple sources and merging them for use.
- The analysis will take a backstep if the data obtained is incomplete or inaccurate.
What are the top tools used to perform Data Analysis?
There is a wide spectrum of tools that can be used in the field of data analysis. Here are some of the popular ones:
- Google Search Operators
- RapidMiner
- Tableau
- KNIME
- OpenRefine
What is an outlier?
An outlier is a value in a dataset that is considered to be away from the mean of the characteristic feature of the dataset. There are two types of outliers: univariate and multivariate.
What are some of the popular tools used in Big Data?
There are multiple tools that are used to handle Big Data. Some of the most popular ones are as follows:
- Hadoop
- Spark
- Scala
- Hive
- Flume
- Mahout
What are the steps involved when working with a Data Analysis project?
Many steps are involved when working end-to-end on a data analysis project. Some of the important steps are as mentioned below:
- Problem statement
- Data cleaning/preprocessing
- Data exploration
- Modeling
- Data validation
- Implementation
- Verification
Can you name some of the statistical methodologies used by Data Analysts?
There are many statistical techniques that are very useful when performing data analysis. Here are some of the important ones:
- Markov process
- Cluster analysis
- Imputation techniques
- Bayesian methodologies
- Rank statistics
Where is Time Series Analysis used?
Since time series analysis (TSA) has a wide scope of usage, it can be used in multiple domains. Here are some of the places where TSA plays an important role:
- Statistics
- Signal processing
- Econometrics
- Weather forecasting
- Earthquake prediction
- Astronomy
- Applied science
What is the difference between the concepts of recall and the true positive rate?
Recall and the true positive rate, both are totally identical. Here’s the formula for it:
Recall = (True positive)/(True positive + False negative)
What is the simple difference between standardized and unstandardized co-efficient?
In the case of standardized co-efficients, they are interpreted based on their standard deviation values. While the unstandardized coefficient is measured based on the actual value present in the dataset.
