Harvard business review rated the data analyst profession as the most excellent choice for the 21st century. The data science field has become one of the rapidly sought-after domains for technical aspirants and professionals. People from a variety of backgrounds are earning high salaries and getting lucrative benefits, which is why they are preparing for data science. Also, people from different backgrounds are adopting this profession and getting hired by the top-most companies.
This blog provides you with an idea of data analyst job description, data analyst roles and responsibilities, and the salary range for data scientists so that you can grasp the field and become a successful IT professional. Here, we have included a brief introduction to the career of data science and the skills required to become a data analyst, along with some information on the salary structure that the organizations offer these professionals.
How to Become a Data Analyst?
Data analysis involves the process of cleaning, analyzing, interpreting, and presenting data using various approaches and business intelligence tools. By employing data analysis, you can uncover valuable insights that contribute to informed decision-making and overall success. It revolves around transforming raw data into meaningful statistics, information, and explanations. The nature of data analysis can vary depending on the specific question being addressed. There are different types of data analysis, each serving a distinct purpose:
- Descriptive Analysis: It focuses on providing an understanding of what has occurred in the past.
- Diagnostic Analysis: It aims to uncover the reasons behind past events. Predictive analytics utilizes data to make projections and forecasts about the future.
- Prescriptive Analysis: It generates actionable recommendations on the best course of action to take based on the data insights.
These different forms of data analysis offer valuable perspectives and can be applied in various contexts to support decision-making processes and drive positive outcomes.
Who Is a Data Analyst?
If you want to know why data analyst jobs are most in-demand then search on LinkedIn for the most promising jobs and your answer will be there. LinkedIn and Glassdoor like popular websites say that data analysis is one of the top jobs for many years to come. To take the data analyst job, you must have an idea of the profile and appropriate skill set as well. Moreover, to earn the major career advantages and earn fat pay packages you must acquire all possible information about the data analyst field.
Data analyst profile is a part of the Big Data profession in which the professionals are supposed to compile and analyze statistics and information for their businesses so that they can identify the problems and suggest possible solutions. They have to also ensure data accuracy and build a database to store appropriate information. Business managers may also ask for some recommendations from them to improve the quality and efficiency of their business. Analysts have to spend many hours with computers to take out crisp information.
Why Should You Choose Data Analyst as a Profession?
Data scientists have become the most in-demand professionals and it has been declared one of the hottest jobs. The professionals have the skills and background in statistics, Math, programming, business, and other important domains. These skills help them to become perfect professionals and here we have listed the topmost reasons to become a data analyst and to join this profession:
1). Increased Demand
Global demand for skilled professionals is increasing day by day even many experts have said that the demand for skilled professionals will reach 140,000 to 180,000 in a few years. Here the supply of skilled professionals is lower than demand due to the long list of data analyst requirements, this increases the chance of getting a job and every professional is trying their luck in this field.
As per a recent survey by Analytics Insight, by 2021, there will be over 3 million new job openings in data globally. With a projected 73% growth rate in job demand by this year, it is a perfect time to jump into the data analytics field and start learning all you can about this growing industry. Before you begin on the journey of data analyst requirements, let’s first understand the roles & responsibilities of data analysts.
2). High Salaries
It has been seen that the average salary of data analysis professionals is INR 6,50,000 OA in India and even in the USA this is almost $1,20,931. This shows that the initial level salary is quite better for data analysts and it will become even far good for the professionals in coming years. Along with experience, the salary gets better even. Salary is another important factor apart from the increased data analyst requirements in the business.
3). Evolving Field
Data science is supposed to be an evolving field and the demand for scientists is also increasing. As scientists have a variety of skills and can help organizations in making better decisions in a more organized way. They can get many opportunities to work with various organizations.
Data science is part of Big Data and involves knowledge of various tools and technology. Data science has become an essential part of almost every organization, so they are hiring skilled professionals. Scientists or analysts can make the task easier and provide the information in a more organized way. And, most importantly, the path how to be a data analyst is quite straightforward.
Data Analyst Job Profile: Top Career Options
Data analysis has become the gateway to entering the current era of automation. There are various career options in the field. Here are the top data analyst job profiles for you to go for.
- Senior Data Analysts
- Data Scientists
- Data Analytics Managers
- Business Analysts
- Data Architect
- Data Security Analyst
Data Analyst Roles & Responsibilities
Data analysts are mainly focused on understanding the questions the business needs to answer and finding whether those questions can be answered through data. A data analyst should be able to understand the technical issues in collecting data, analyzing data, and reporting. They must be able to recognize trends and patterns. Here are the key data analyst responsibilities:
- Design and maintain databases by managing issues related to data.
- Do data mining on different data sources before organizing the data in a readable format.
- Take help from the statistical tools in predicting future trends by identifying patterns and trends in the available datasets.
- Create and implement data analysis strategies in order to optimize the statistical quality and efficiency of the organization.
- Design charts and reports to build communication in the insights and make predictions from the data.
- Create documentation to let stakeholders comprehend the entire data analysis process for future use.
Prerequisite Needed to be a Data Analyst
To pursue the DAaa Analyst Career path, you should have a four-year Bachelor's degree in either math, science, computer science, business, or statistics. However, even if you are not from this background, you can still pursue this title, provided you have a professional certificate program certification.
To be a successful data analyst, you don’t necessarily need a high degree. But, having some knowledge of Programming Languages, Data Tools, and Statistics will certainly help you on your path to becoming an efficient professional.
Data Analyst Salary: How Much Data Analyst Earn?
You have already know the data analyst requirements, how to get into data analytics, market demand, and the major roles & responsibilities of a Data Analyst, However, you might be wondering, ‘How much does a Data Analyst earn?,’ ‘What is the average salary of a data analyst? and so on.
To answer these questions, yes, data analysts can earn a high payout.
As per Indeed, the average data analyst salary in the U.S. is $75,685. Indeed notes that data analysts can typically earn the most in non-traditional tech areas.
According to data from Robert Half’s 2021 Technology and IT Salary Guide, the average salary for data analysts depending on their experience breaks down as follows:
Data analysts are among the highest-paid IT jobs. Here, the primary reason is the lack of professional’s supply and high demand due to the increasing data analyst requirements. These professionals are an extreme necessity for small as well as big corporations as most of these companies receive chunks of data on a regular basis.
Data Analyst Certification Details: Earn Big Data Certifications
There are a number of certifications available for the data scientist or data analyst profession to help you through how to be a data analyst. Which one is appropriate for you totally depends on your organizational requirement? Plenty of articles is also available that can help you in getting information about these certifications and how to be a data analyst. A certification can help you in grasping all the information and knowledge and help to prove your skills in front of the employer. The most popular certifications are
1). SAS Certified Base Programmer for SAS 9
SAS provides certifications it is considered one of the topmost credentials. This test checks the ability to import and export data from raw files along with other operations. Other operations include data manipulation, data transformation, and combining various data sets. They must have the skill to correct data and remove all programming and logical errors.
2). EMC Proven Data Analyst Professionals
These professionals are known to have all the required skills for this profession. Certification includes the questions that are relevant to data processing like cleaning or data and the use of various techniques to make it relevant and organized. EMC is the certification that helps the professionals in becoming the relevant ones and supports their questions on how to be a data analyst.
3). CDMP or Certified Data Management Professional
This is authorized by non-profit organizations like DAMA or Data Management International Association. In this certification, there are four credentials for the professional that are associate, practitioner, master, and fellow level. The candidates with 6-month experience can become associate-level professionals and must have strong knowledge of DMBOK principles.
The candidates with more than 5 years of experience can become CDMP professionals and after 10 years they can become the data management professional practitioner and apply for the master CDMP this is how to get into data analytics. CDMP fellows have extensive experience in the data management field and can become proven speakers, publishers, workshop presenters, and other similar ones.
Skills Required to Become Data Analyst in the Talent Market
The rewards of a data analytics job do come without significant training and effort. The data analyst requirements include specific skills and tech central qualifications in terms of data analyst education requirements. This is also to keep in mind when it comes to what to study to become a data analyst. In addition, you also need to have a handful of soft skills.
There is not a single way to gain all the skills required to become a data analyst. While some opt into master’s programs, a growing cohort of learners has begun enrolling in short-term data science courses. But regardless of the route, you decide to choose, you will need to acquire a sturdy set of skills in order to become an in-demand data professional and this is how to become a data analyst:
Technical Skills Required to Become Data Analyst
When it comes to the data analyst prerequisites, the candidate should have a well-developed toolbox of technical skills. Here are a few to focus on:
As far as popular and most used programming skills required for the data analyst profession is concerned then mostly R and Python are used by data analysts. Learn machine learning languages if you want to be a perfect data analyst. As far as machine learning languages are concerned then you must know supervised, unsupervised, and reinforced learning. You must be familiar with the below-listed concepts if you want to be a perfect data analyst and looking for how to get into data analytics:
- Linear and Logical Regression
- Random Forest
- Decision Tree
- K Nearest Neighbor
Do Read Important Interview Questions on Data Structures!
Deep learning is also recommended for machine learning professionals and is among the top data analyst prerequisites. You must know the below-listed concepts of deep learning if you want to become a perfect machine learning experience:
- Neural Network Fundamentals
- Tensor flow or Keras-like deep learning libraries so that a perfect learning model can be generated.
- Understand how Recurrent, Convolutional Neural network, Autoencoders.
Learn the Data Visualization
Data visualization is also imperative in the data processing life-cycle and among the must-have data analytics prerequisites. Even it also requires a good knowledge of data processing tools. Few tools are listed below that may be required and used by the professionals:
- Google Charts
- Data wrapper
Having knowledge of these tools can make you a perfect and efficient data analyst. It is basically a person’s ability to display data finding through graphics or other illustrations. The purpose is to facilitate a better understanding of data-driven insights. Data visualization helps data professionals help businesses in decision-making.
Master the Big Data Skills
Big data is almost everywhere and is required by almost all data science professionals, Today, every organization wants to collect and store all data that is present across social networking sites and throughout the internet. They do not want to miss out on any information that may be of their use. Today huge data is floating around all over the internet it may be beneficial in various ways for organizations. Even the use of this data depends totally on the organizational requirement.
Big data is in the frontiers of the IT field and it makes the process of decision making and business conduction easier. It has become a crucial part of every organization and can provide cutting-edge solutions for complicated organizational issues. So, data analysts must also know about Big data frameworks like Spark or Hadoop and it is one of the data analytics prerequisites.
Learn Data Ingestion and Data Munging
Data ingestion means importing, transferring, process, and loading the data to and from the database. Here data loading and sourcing can be performed from various sources. Here are many tools used for this process like
Read: 23 Smart Data Analytics Tools For Perfect Data Management
- Apache Sqoop
- Apache Flume
Having knowledge of these tools is one of the requirements for a data analyst.
Python is one of the most important data analyst requirements. This is a high-level general-purpose programming language that offers a remarkable number of specialized libraries, many of which pertain specifically to AI. Understanding Python is a skills data analysts must keep current in the AI-concerned professional landscape.
One of the most well-used languages when looking for how to be a data analyst, R is used in analytical work. It encompasses various built-in, easy-to-use data organization commands by default. This language also appeals to businesses since it can easily handle complex and large quantities of data. No doubt, it is on the top list of data analyst requirements.
SQL & NoSQL
One of the requirements for a data analyst is SQL. SQL persists as the standard means for querying and handling data in relational databases. The branded versions of SQL like MySQL come up with opportunities to gain a better understanding of relational database management systems and is one of the data analytics prerequisites. The NoSQL framework effectively structures its information in any way if the available method is not relational.
Learning ML helps you become competitive in the data analytics industry and perfectly fits with the data analytics requirements. While you may not be working on machine learning projects, having a general understanding of the tools and concepts provides you with an edge over competitors and this is how to become data analyst.
Before applying the data analytical models, data analysts have to perform feature selection also. This is a part of making raw data clean that involves many steps. The complete process to make raw data clean is known as data munging. R and Python packages can also be used for this purpose. It is one of the most important parts of the data life cycle.
Being a data analyst or scientist, you must know which feature is important and must be there in the package, along with the information on the features that must not be part of your package. Any data inconsistency must be removed. This is the task and steps for data munging.
If you are fresher and fixed between whether to pursue business analyst or data analyst, here's a guide for you on – Business analyst vs Data analyst.
Tableau is a popular data analysis application extensively used by data scientists. It stands out for its ability to swiftly evaluate data, allowing for efficient analysis. It allows creating interactive dashboards and visually appealing spreadsheets for data visualizations. With Tableau, businesses can develop dynamic dashboards that provide actionable insights, driving their progress and success. Furthermore, Tableau is compatible with various operating systems and hardware, ensuring smooth performance even in virtualized environments.
SAS (Statistical Analysis System) is a renowned commercial suite of business intelligence and data analytics tools. It has evolved over time, developed by the SAS Institute in the 1960s. Today, its main applications include client profiling, reporting, data mining, and predictive modeling. SAS is specifically designed for the business market, catering to the needs of large enterprises. It offers robust functionality, adaptability, and user-friendliness, which is particularly valuable for organizations with varying levels of in-house programming expertise.
Microsoft Power BI:
Microsoft Power BI is a relatively new addition to the data analytics tools market, with a less than decade history. Originally starting as an Excel plug-in, it has since evolved into a comprehensive suite of corporate data analysis tools in the early 2010s. Power BI empowers users to create interactive visual reports and dashboards effortlessly.
One of its standout features is its seamless data connectivity. While it integrates smoothly with Excel, as expected from a Microsoft product, it also offers compatibility with various other data sources, including text files, SQL servers, and popular cloud platforms such as Google and Facebook analytics.
Soft Skills Required to Become Data Analyst
Here are the major soft skills every data analyst should have to shine in the industry:
1). Attention to Detail
To make yourself aligned with the data analyst requirements, you must have the ability to notice every minor detail. You should be able to notice the small clues pointed toward a larger message. The skills come in handy when data analysts work on building processes to capture and sort data. A single error in the code can affect the whole workflow awry.
2). Critical Thinking
Data analysts are responsible for uncovering and synthesizing connections that are not very clear. Here, you should be able to think analytically about data, find patterns and extract actionable insights. It is one of the vital data analyst requirements to go above and beyond and apply yourself to thinking, as opposed to only processing.
Having excellent communication skills, problem-solving, research and team management are some of the major data analyst requirements one needs to have to shine in the career.
Steps to Become a Data Analyst
A good data analyst must have the above skills to perform his tasks. Below is the step-wise process on how to become data analyst –
Step 1: Check Educational Requirements
When you are looking for a data analyst education requirements, having knowledge of Science, Technology, Engineering, and Mathematics is considered the best starting point to get the basic skills. However, an undergraduate or a postgraduate degree in a relevant discipline such as Computer science, Economics, IT, and Mathematics is vital for becoming a data analyst.
Step 2: Focus on Learning Programming
To become a data analyst, you should have some level of coding knowledge and experience. You need to have the knowledge of SQL, and basic Python skills and master the key libraries required for this role, including Pandas, Matplotlib, Seaborn, Numpy, and Scikit Learn.
Step 3: Take A Data Analysis Course
In the next step, consider taking up a data analysis course to gain the most required data analysis skills. These certification courses are meant to provide you with a comprehensive overview of the subject in a shorter period. There are numerous short-term and long data analysis course options that you can choose at your convenience.
Step 4: Apply for Jobs & Interview Preparation
After you have finished your certification training course, it’s time to showcase your qualification and knowledge and land your dream job. Don’t forget to update your CV and portfolio before applying for the job openings. Prepare for the interview to give your best and you are all set to fly. Here are some of the top 30 Data Analyst Interview Questions and answers.
The role of a data analyst is very high-paying. If you want to become a data analyst without having any prior experience then you should strengthen your basics of data analysis first, take up some data analysis projects and build your profile. Some project work experience and some added skills would make your data analyst resume stand out and can help you land your dream job.
Here are the tips for becoming a data analyst and getting lucrative salary packages:
So, if you are passionate about data, statistics, and algebraic functions then just shape your career and grasp all the required skills on becoming a data analyst. The below-listed skills are required to be a data analyst:
- Basic SQL skills
- Pattern finding and matching ability
- Some knowledge of development skills
- Ability to find actionable insights from processed data
- Knowledge and experience in Microsoft Excel
In the end, we can say that data analyst requirements involve higher mathematics and statistics. The job involves both software development and programming.
Looking for a career in data but confused between data science and data analyst, this comprehensive guide on data science vs data analyst might help you to take the right decision!
Last Verdicts on Data Analyst Requirements
With the mentioned salary range, and increasing market demand, it is clear that the data analyst job is very lucrative and high-paying. If you fulfill the basic data analyst requirements, you can explore data analytics jobs in all sorts of industries, and there’s more than one path toward securing your first job in this high-demand field. Whether you’re just getting started in the professional world or pivoting to a new career, data analysis is an ideal career option. And, this is how to become data analyst.
If you are confused about what to study to become a data analyst? You can begin your journey towards becoming a data analyst by getting a free consultation from your subject matter experts at JanBask Training. We will guide you on what skills to work on your skills and learn data analysis from the scratch!.
Do connect with us by sharing your experiences if you have been successful in this field. Till then, happy learning!
Question 1: What does data analyst do?
Ans:- A data analyst is responsible for collecting, analyzing, and interpreting large sets of data to identify patterns, trends, and insights. They use various tools and techniques to organize and cleanse data, perform statistical analysis, and create meaningful reports and visualizations to support decision-making processes.
Question 2: What skills are required to become a data analyst?
Ans:- To excel as a data analyst, you need strong analytical and critical thinking skills, proficiency in programming languages like Python or R, knowledge of data visualization tools such as Tableau or Power BI, and expertise in SQL for data querying and manipulation. Additionally, statistical analysis, data modeling, and problem-solving skills are highly beneficial.
Question 3: What industries typically employ data analysts?
Ans:- Data analysts are in demand across various industries, such as finance, healthcare, retail, e-commerce, marketing, and technology. Almost any industry dealing with large volumes of data can benefit from the insights and analysis data analysts provide.
Question 4: What is the educational background required to become a data analyst?
Ans:- While a specific degree may not be mandatory, most employers prefer candidates with a bachelor's or master's degree in fields such as mathematics, statistics, computer science, economics, or data science. However, practical experience, relevant certifications, and a strong portfolio showcasing data analysis projects can also be valuable.
Question 5: What are the career prospects for data analysts?
Ans:- Data analysts have promising career prospects due to the increasing reliance on data-driven decision-making. With experience and continuous learning, data analysts can progress to senior analyst roles, become data scientists, or move into managerial positions. The demand for data analysts is expected to grow significantly in the coming years, offering ample career growth and advancement opportunities.
Question 6: What are the benefits to opt for the data scientist certification course?
Ans:- The data scientist certification course comes with several benefits. It provides you with a deep knowledge of machine learning and artificial intelligence implementation, along with providing arithmetic knowledge. The online master data science course also makes you proficient in data visualization and data analytics. Furthermore, the data scientist course improves your interaction skills and organizational potential.
Question 7: How important are the roles and responsibilities of data analyst?
Ans:- A data analyst is in charge to arrange the data relevant to sales count, market research, or various other behaviors. They use technical skillset to guarantee that the information is precise and top-quality. The roles and responsibilities of data analyst is vital since they possess the potential to create data-driven decisions that offer the companies a sharp competitive edge in today’s market scenario.
Question 8: Can you give a brief synopsis on data analyst responsibilities?
Ans:- To state in brief, a data analyst responsibilities comprise the thorough analysis of data and then finding and implementing the best possible method to display it visually to managers and shareholders. The data analyst responsibilities also include guaranteeing quality assurance and method documentation and highlight Key Performance Indicators.
Question 9: How is a day in the life of a data analyst?
Ans:- The roles and responsibilities of data analyst is extremely huge. The data analyst responsibilities comprise various intricate works. During the workdays, data analysts spend the time working on big data and creating it as a usable data for the organization where they work. Hence, a data scientist certification is necessary along with an online master data science course from an organization. The data scientist course makes you ready to become a professional data scientist.
Question 10: Can you comment on the data analyst job description?
Ans:- A data analyst cooperates with COOs, VPs, or managers and even with the directors. The data analyst job description comprises entire lifecycle evaluation to include necessities, tasks, and design. Data analyst roles and responsibilities also include forming analysis and reporting possibilities. The role of data analyst also comprises monitoring performance and quality control strategies to detect modifications. The data analyst job requirements calls for technical skillset and a solid knowledge on database frameworks also.
- AWS & Fundamentals of Linux
- Amazon Simple Storage Service
- Elastic Compute Cloud
- Databases Overview & Amazon Route 53
-1 day 29 Sep 2023
- Intro to DevOps
- GIT and Maven
- Jenkins & Ansible
- Docker and Cloud Computing
6 days 06 Oct 2023
- Data Science Introduction
- Hadoop and Spark Overview
- Python & Intro to R Programming
- Machine Learning
-1 day 29 Sep 2023
- Architecture, HDFS & MapReduce
- Unix Shell & Apache Pig Installation
- HIVE Installation & User-Defined Functions
- SQOOP & Hbase Installation
-1 day 29 Sep 2023
- Salesforce Configuration Introduction
- Security & Automation Process
- Sales & Service Cloud
- Apex Programming, SOQL & SOSL
0 day 30 Sep 2023
- Introduction and Software Testing
- Software Test Life Cycle
- Automation Testing and API Testing
- Selenium framework development using Testing
-1 day 29 Sep 2023
- BA & Stakeholders Overview
- BPMN, Requirement Elicitation
- BA Tools & Design Documents
- Enterprise Analysis, Agile & Scrum
-1 day 29 Sep 2023
MS SQL Server
- Introduction & Database Query
- Programming, Indexes & System Functions
- SSIS Package Development Procedures
- SSRS Report Design
6 days 06 Oct 2023
- Features of Python
- Python Editors and IDEs
- Data types and Variables
- Python File Operation
-1 day 29 Sep 2023
- Components of AI
- Categories of Machine Learning
- Recurrent Neural Networks
- Recurrent Neural Networks
-1 day 29 Sep 2023
- Introduction to Machine Learning & Python
- Machine Learning: Supervised Learning
- Machine Learning: Unsupervised Learning
34 days 03 Nov 2023
- Introduction to Tableau Desktop
- Data Transformation Methods
- Configuring tableau server
- Integration with R & Hadoop
-1 day 29 Sep 2023