1. How do you become a Data Analyst?
In order to become a Data Analyst, you need to cultivate certain specific skills. These include:
- Sound understanding of statistical and mathematical concepts
- Capability to work with data models and data packages
- Knowledge of different programming languages including Python and others
- Sound knowledge of SQL databases
- Comprehensive understanding of the fundamentals of Web Development
- Knowledge of Microsoft Excel
- Being able to understand processes such as data mapping, data management, data manipulation and so on
2. What are the prime responsibilities of a Data Analyst?
In general, the position of a Data Analyst would include the execution of the following responsibilities:
- They need to derive meaning from data with the aim of analyzing it, in line with the requirements of the business.
- Data analysts have the responsibility to generate findings in the form of reports which help other individuals to decide upon the further course of action.
- They need to perform an analysis of the market in order to make sense of the strengths and shortcomings of its competitors.
- Data Analysts need to utilize data analysis for improving business performance in line with the requirements and needs of the customers.
3. What is the difference between Data Mining and Data Analytics?
Data Mining
Data Mining is the process of identifying patterns in stored data. It is performed on well-documented and clean data, and is generally used for Machine Learning wherein analysts simply identify patterns with the help of algorithms. The results obtained from the process are not easily comprehensible.
Data Analytics
Data Analytics is the process of deriving insights from raw data by way of cleaning, organizing and ordering it in a meaningful manner. This raw data might not necessarily be originally present in a well-documented form. The results obtained from the process are far more easily comprehensible than in the case of Data Mining.
4. What is the process of Data Analytics?
The process of Data Analytics follows a certain path:
- Identification of the Problem: This step would encompass understanding a problem within a business enterprise, identifying the objectives and goals to be achieved and drafting a solution for solving the problem.
- Collection of Data: This step would involve collection of relevant data from all possible sources in order to address the problem.
- Organizing and Data Cleaning: The data collected is most likely to be in an unrefined form. It would be required to organize it as well as clean it by removing all forms of irrelevant, redundant and unwanted bits, in order to make it suitable for analysis.
- Analysis of Data: This step is the final rung of the Data Analytics ladder wherein the professional applies the different Data Analytics tools, techniques and strategies in order to analyze data, derive insights from it and consequently, predict future outcomes as well as generate solutions to the problem concerned.
5. What is the difference between Data Mining and Data Profiling?
Data Profiling
Data Profiling is the process of analyzing the individual particular attributes of data. Thus, it helps in providing information on specific attributes like length, data type, value range, frequency and so on. This process is usually undertaken in order to assess a dataset for its consistency, uniqueness and logic.
Data Mining
Instead of focusing upon particular attributes, Data Mining lays emphasis upon the relation between different attributes. It seeks to identify data clusters, discover sequences, identify unusual records, dependencies and so on. The process is undertaken in order to find out relevant information which was not identified earlier.
6. What is Data Validation?
As the name suggests, the process of Data Validation involves determining the quality of the source and the accuracy of data. The process of Data Validation can take different forms:
- Form Level Validation: After the user completes the entire form and submits it, this process of validation begins. It scrutinizes the entire data entry form, validates all fields and highlights errors
- Search Criteria Validation: This technique is used to provide the user with the most accurate and relevant matches and results for their searched phrases and keywords
- Field Level Validation: Data Validation is done at the level of each field as the user enters data for each of them
- Data Saving Validation: When a database record or an actual file is being saved, this technique is used
7. What is Data Cleaning? How to practise it?
Data Cleansing is also referred to as Data Wrangling. It is the process of cleaning, enriching and structuring raw data into a usable desired format which could be used for decision making. It involves the process of identifying and removing inconsistencies, bugs and errors from the data in order to improve its quality.
The best practices for Data Cleaning include:
- Segregating and classifying data on the basis of their attributes
- In case of large datasets, it would be wise to dissociate them into smaller chunks which increases the iteration speed
- Moreover, in case of large datasets, it is important to perform data cleaning step wise, until one is convinced of the quality of the data at hand
- Analyze the statistics for each column
- Developing a set of scripts or utility functions for performing common cleaning activities
- It is important to keep a track of every cleaning activity and operation so that operations can be removed and changes can be introduced, if required
8. What are some of the Common Problems faced by a Data Analyst?
Some of these problems are:
- Spelling mistakes and duplicate entries which adversely affects the quality of data
- The usage of multiple sources of data might result in varying value representations
- Dependency on unreliable and unverified sources for data extraction results in acquisition of poor quality data. This will in turn increase the time spent on Data Cleaning
- Overlapping and Incomplete data also pose significant challenge to a Data Analyst
- Missing and Illegal Values
9. Define Outlier and Collaborative Filtering?
This is one of the typical Data Analyst Interview Questions.
Outliers
An outlier refers to a value which appears to be divergent or far removed from a set pattern in a sample. In other words, in a dataset, it is a value which is away from the mean of the characteristic feature of the dataset. There are two kinds of outliers: Univariate and Multivariate.
Collaborative Filtering
It is an algorithm which creates a recommendation system on the basis of the behavioral data of the user. The components of Collaborative Filtering are: users, items and interest.
For instance, you might come across a recommended section as you browse through your Netflix account. The particular shows or movies or series which form a part of the recommended section, are carefully curated on the basis of your past searches and watch history.
The way in which Collaborative Filtering works through Matrix Factorization for business giants, is quite an interesting aspect of Data Analytics. If you wish to know more about the process, you can refer to the video here:
10. What is the KNN Imputation method?
KNN or K-Nearest Neighbor is a method of comparing the values of the missing attributes with the values of those attributes which are most similar to the attributes whose values are missing. The similarity between the two attributes is determined using the distance function.
Conclusion
By the end of this blog, you must have developed a fair understanding of some of the classic, common and yet important Data Analyst Interview Questions. This list of Data Analyst Interview Questions and Answers is definitely by no means exhaustive. There can be several other Data Analyst Interview Questions for Freshers, for Experienced, technical ones, and so on. However, this article can serve as an important point of reference by giving you an idea of the important topics and issues to focus upon as you prepare to face the Interview Questions for Data Analyst.
Certifications in the field of Data Analytics, along with rigorous training in developing Data Analyst skills and hands-on experience in Data Analytics projects, can be valuable additions to your resume. Online Bootcamps can serve as wise choices if you wish to prepare yourself as a Data Analytics expert. With end to end assistance, we, at Syntax Technologies, provide you with an exciting opportunity to achieve your goals with our Data Analytics and Business Intelligence course.
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