How to Become a Data Analyst

By Nandini 

Last updated on Sep 28 2021

How to Become a Data Analyst

Guide to Become a Data Analyst in 2021


Major Requirements for Becoming a Data Scientist


The candidates have to check off numerous things on this list to become a data scientist or analyst. The requirements which the candidates need to fulfil are:


  • The candidates are required to have a proper understanding of the field of programming languages. The programming languages might include JavaScript, XML, or Frameworks of ETL.
  • The candidates are also required to have proper knowledge of business objects (reporting packages).
  • The candidates are required to have the capability to gather, organize, manage, analyze, and disseminate Large Data effectively and efficiently.
  • The candidates must bear a technical and substantial understanding of data mining, database modification, and designing and segmentation techniques.
  • The candidates are also required to become prompt in statistical packages. They are also required to understand analyzing huge datasets like SPSS, Excel and SAS, and many more.



Roles and Responsibilities of a Data Analyst


The candidates are also required to have proper knowledge of the job they do. A data analyst is required to carry out the following tasks:

  • The data analyst has to gather and interpret the necessary data from several sources and analyze the outputs for the same.
  • The candidates designated as data analysts have to clear and filter the acquired data from numerous sources.
  • The candidates who have achieved data analysis design also have to encourage every aspect of data analysis.
  • The data analysts also have to analyze the complicated datasets and verify the hidden patterns in-between the datasets.
  • The data analysts also have the responsibility to safeguard the databases.


Important Skills and Knowledge Areas for Data Analysts


Data Cleansing


Data cleansing in data analysis means the process by which errors in a database or dataset are classified and corrected. The data analysts make use of this method to remove the flaws in databases or datasets. Data analysts also use this to enhance the quality of data.

The best ways by which data can be cleaned are:

  • The candidates are required to segregate data as per the respective attributes of the candidates. The candidates have to carry out the following in a careful way.
  • The candidates have to break large data into small datasets, and the candidates then have to clean the following data.
  • The data analysts have to analyze the statistics of every data column.
  • The candidates have to build a set of utility functions or a cluster of steps to deal with general cleaning activities.
  • The candidates designated in the post of data analyst have to maintain a record of the cleansing operations carried out to facilitate easy addition and the elimination of data from the datasets. The candidates have to do the following if it is essential.



Handling Suspected or Missing Data


The data analysts need to know what they can do with the missing or suspected data. If anytime, the data goes missing, the data analyst has to:

  • Make use of data analysis principles and strategies like single imputation methods, detection methods, and model-based methods for the detection of missing data.
  • The candidates designated as data analysts have to prepare a validation report which would contain all the necessary information regarding the missing or suspected data.
  • The candidates also have to scrutinize the missing or suspicious data to access the validity of the data.
  • The data analysts have to remove or replace all the invalid data (in case it is present) by using a proper validation code.


Data Validation Methods


The candidates can prefer several ways and methods for the validation of databases. The general data validation methods which are basically used by Data Analysts are:

  • Field Level Validation: In the following method, the candidates validate necessary data in each field. The candidates also validate the data when the users enter the information. The following method also helps the candidates to eliminate the errors as you proceed.
  • Form Level Validation: In the following method, the data in a dataset is validated only after the candidates or the users finishes the form and submits the following. The Form Level Validation verifies the whole data or information in one go. The following method also validates all the fields present in it. This method demonstrates the errors (if they are present in a project), which allows the user to check the following and correct those.
  • Search Criteria Validation: The Search Criteria Validation is the validation technique used to offer the candidates or the users the correct and relevant matches for the phrases or keywords they have searched previously. This validation method's main objective is to make sure that the search issues of the users can return the most relevant results.
  • Data Saving Validation: The following data validation method is used while saving an actual file or database record is actually used. Basically, the following is carried out when numerous data entry forms require validation.


Important Statistical Concepts for Data Analysts


There are some statistical methods that the data analysts use regularly. The methods or concepts mostly used by data analysts are:

  • Markov Process
  • Imputation
  • Bayesian Method
  • Simplex Algorithm
  • Rank statistics, outliners detection, and percentile
  • Mathematical optimization
  • Spatial and cluster processes


Data Analysis Steps


There are certain steps that a data analyst needs to follow. These steps are actually important for the project to be carried out effectively. The steps which a data analyst need to follow are:

  • A Data Analysis project's main requirement is that it requires proper knowledge of the business demands and business requirements.
  • The next step that the candidates need to carry out is to classify the most appropriate information or data sources essential for effective business management. These elements need to fit into the project properly, and these things also aim to get relevant data from verified and reliable sources.
  • The third step that the candidates need to carry out is to explore datasets, refining, cleaning the data, and organizing the data to understand the data at hand better. The candidates need to follow these steps to carry out the projects effectively.
  • The fourth step is the easiest step that the candidates need to carry out. In the fourth step, the candidates need to validate the information or data.
  • In the fifth step of the process, the individuals need to deploy and track the datasets. This is also an important part of data analysis.
  • The sixth and final step is the main step which the candidates need to carry out properly. The last and the main step is to make a list of the common outcomes which are more likely to come out. The outcomes also need to be iterated till the probable results are completed.



Issues Faced During Data Analysis Process


Knowing about the issues and problems that are to arise in the project is essential. The candidates need to know about the problems first before analyzing the data. The candidates going for the data analysis interview need to be aware of the following question and be prepared for the following. This is because answering this question will be helpful for them while carrying out their work. The problems which the candidates are more likely to face are:


The candidates need to be aware of the presence of duplicate entries as well as spelling mistakes. Eliminating these errors is essential because these errors can hamper the quality of data or show some adverse effects.


The candidates need to eliminate the bad quality data, which is achieved from unreliable sources. In the following case, a data analyst will have to spend an important amount of time cleaning the data.

The data that is achieved from numerous sources have chances of differing in representation. Once the candidates gather the data and combine it after cleaning it and organizing it properly. The difference noted in the data representation has a chance of causing a delay in the data analysis process.


Incomplete data is one of the major challenges that the candidates are likely to face during data analysis. The following would unavoidably lead to faulty or wrong results.


Best Tools for Data Analysis


Popular tools which data analysts can use are:

  • Google Fashion Tables
  • Google Search Operators
  • Tableau
  • Solver
  • RapidMiner
  • OpenRefine
  • io
  • model


To explore certification programs in the Data Science field, chat with our experts, and find the certification that fits your career requirements. 


Get certified with Data Science Master Program Certification


Suggested Reads:

Why Data Science is the Best Career Option

How to Become a Successful Data Scientist




About the Author

Sprintzeal   Nandini 

With over 3 years of experience in creating informative, authentic, and engaging content, Nandini is a technology content writer who is skilled in writing well-researched articles, blog posts, newsletters, and other forms of content. Her works are focused on the latest updates in E-learning, professional training and certification, and other important fields in the education domain.

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