If you’re an enthusiast of becoming a Data Analyst, but you lack experience in the field, then this blog is for you. The following details in this blog would help you to endure a career learning path that would make you a Data Analyst in the industry. Demand for Data Analyst is increasing in the market and to become one today is the right time. The blog is a primarily a guide that would let you know about who is a Data Analyst, the role and responsibilities of a Data Analyst and the various Data Analyst Interview Questions and answer that would be helpful for you to excel in the field. Let’s get started!. You might be able to land your dream job soon if you follow these guidelines.
Who is a Data Analyst?
A Data Analyst is a person who is in-charge of analysing organisational data and use them to interpret the data in simple terms so that organisational management are able to make strategic decisions. By analysing data sets and patterns, a Data Analyst is able to solve organisational problems and answer relevant questions for organisations to make informed decisions. Accordingly, they make use of organisational data to turn them into dashboards and use Data Visualisation techniques to demonstrate the information through charts and graphs.
What does a Data Analyst do?
To understand what are the daily tasks and responsibilities of a Data Analyst, the following list can be evaluated which would help you understand what you would do as a Data Analyst:
- Developing and implementing Databases
- Developing and implementing database collection systems
- Data Cleaning and filtering
- Data collection from primary and secondary sources
- Identifying KPIs and critical metrics in collaboration with the management
- Building and customising reports
- Presenting key findings for stakeholders using Data Visualization
- Developing dashboards
- Creating documentation of data models, measures and infrastructure
Data Analyst Interview Questions and Answers
The following section includes the various Data Analyst Interview Questions and Answers that you can find helpful as you progress through your Data Analyst profile. These questions and answers would help you ace your interview efficiently and help you land your dream job.
Q1: Mention the differences between Data Mining and Data Profiling.
Data Mining is the process through which different consumer behaviour and trends are identified and analysed to ensure that businesses are able to fulfil customer demands and needs effectively. The data collected for the purpose of mining are done through Data Analytics tools and different algorithms and are developed to evaluate the consumer behaviour.
Data Profiling refers to the process in which various variables of a dataset are analysed considering the relationships between the data which are essential for analysing data trends and patterns. This helps businesses to evaluate the ways in which it is able to connect and analyse the customers’ engagement with the brand and its products thus, ensuring that customer experiences are improved at every step.
Q2: What’s the difference between Data Scientists, engineers, and analysts?
Data Scientists are those experts who are involved in collecting, gathering, organising and structuring data using different Data Science and Programming techniques and tools. They help in solving complex business problems and provide solutions which help businesses to improve their decisions.
Data Engineers are the experts involved in extraction of raw data and load it into the data warehouse like RedShift, etc to conduct effective Data Analysis.
Data Analysts are those professionals within an organization who are responsible for analyzing raw data and interpret it in laymen terms using visualization techniques and provide insights regarding the problems in a company and provide solutions to solve them.
Q3: Why do you want to become a data analyst?
I want to become a Data Analyst so that I am able to utilise my analytical and problem solving skills within your organisation. I would be able to utilise my skills to interpret the raw data into meaningful insights which would allow you to make appropriate decisions using my knowledge and information.
Q4: What are the best methods for data cleaning?
There are several methods used for Data Cleaning and the best ones are:
- Manual or Hand Scraping which requires you to insert all the data from a document to a spreadsheet using manual skills and further use excel formulas to clean the inadequate and irrelevant data.
- Using software based tools to ensure proper designing of the data thereby cleaning and analysing using proper formatting tools to undertake Data Analysis
Q5: Describe univariate, bivariate, and multivariate analysis
Univariate analysis involves data analysis for a single set of Data. Bivariate involves analysis of of the relationship between two datasets. Multivariate analysis implies for analysing the relationship between datasets which have multiple variables.
Q6: What are the different types of sampling techniques used by data analysts?
In order to make predictions regarding a group of population, data is collected from a by extraction of a small number of objects or variables from that population. Sampling techniques may include censor sampling, random sampling, cluster sampling, stratified sampling and convenience sampling.
Q7: How can you handle missing values in a dataset?
Missing values in a database can occur due to incomlete data entry, or data collection procedures. To handle missing values in a Dataset it is essential to identify the reasons behind the missing values and accordingly, ensure that any row or column having missing values are deleted, or the column or row entirely is deleted, if the values in them are missing.
Q8: Explain the term Normal Distribution.
In a normal distribution of dataset, the mean and median tend to be directly proportional to the central distribution area, and is inversely proportional to the width. The graph of the normal distribution curve looks mainly like a bell-shape having “tails” at both the end of the curve.
Q9: How do you treat outliers in a dataset?
Outliers are those points within a dataset which tend to lie away from the rest of the points of Data. making use of the outlier detection method, it is possible to identify these observations and then remove them from the dataset. However, since the data lies outside the normal range of dataset, it is not possible to remove an entire observation.
Q10: What statistical methods have you used in data analysis?
I have used several statistical methods in Data Analysis which includes using data analysis tools like Tableau, R and Excel along with various other software programs which are available on the web. I think analytical tools are an important way through which it is possible to provide information that can help businesses to make informed business decisions.
Data Science Courses
Many Data Science courses are available in the market including courses from various institutes and e-learning platforms through which you can learn different skills. This may include machine learning, programming languages like R, Python, etc and artificial intelligence. Many of these programs also offer you learning about different tools like Tableau, SQL, etc as well. Opting for a Data Science course opens up various fields whereby you can even choose to be a Data Analyst in the industry. Some of the Data Science courses available include Wizard- Data Science course for Professionals by Pickl.AI, Simplilearn’s Data Science course, Data Science courses by Upgrad, etc. You can opt for your suitable course that also offers you with Job Guarantee and you may find yourself landing your dream job within a matter of months.
Summing up!!
Thus, the above post can act as a proper guide using which you can become a Data Analyst expert without having any experience. You can practice from a range of Data Analyst interview questions and answers and excel for your interview in your desired company. It is suggested that you opt for a suitable course in Data Science that would allow you to develop your skills, have practical experience through live projects and finally, you would be able to land your dream job.
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