Are you passionate about numbers and algebraic functions?
Does the idea of evaluating, processing, analyzing, and interpreting statistical data makes you roll up your sleeves and get the job done?
Do you love to distinguish the trends and patterns in data?
Do you enjoy sharing your work and communicating your knowledge with others in the team?
Do you have the attitude of self-learning and are able to figure things out on your own?
If you answered yes to one or more of these questions, then a career as a data analyst may be your calling.
463 exabytes of data (equivalent to 212,765,957 DVDs per day) will be created every day by 2025. Organizations across all industries need highly skilled analytic professionals to analyze this ocean of data for solid business decisions.
Going by these statistics and other popular sources like Glassdoor and LinkedIn, “Data Analyst” is going to be one of the most in-demand jobs for 2021 and beyond. Simply put, there is no better time than this to become a data analyst.
“Behind each data point is a living, breathing human."- Shafqat Islam, CEO of NewsCred.
Still wondering, who is a data analyst? That living, breathing human is a data analyst. Organizations count on data analysts to help them make profitable business decisions based on data. A data analyst is an umbrella term and it can also include Business Analyst, Corporate Strategy Analyst, Marketing Analyst, Sales Analyst, Finance Analyst, Management Reporting, Business Product Analyst, Social Media Data Analyst, Budget Analyst, and much more.
Data and numbers have an important story to tell. They rely on a data analyst to give them a clear and convincing voice. A data analyst plays a critical role in guiding businesses in improving the products, services, or business processes by delivering data-driven reports and recommendations to stakeholders. For any data science project to be executed successfully, once the business analyst completes requirements gathering, the requirements are sent to a data analyst. A data analyst is a detective who identifies what all data is important to solve the business use case and from where we can get the data. He/she also suggests other sources of data that can be useful for solving a given business use case. A data analyst takes the data from various sources and tries to understand/interpret the trends and patterns in it.
Understanding the business goals is a crucial thing every data analyst does because it forms the basis of their analysis. A data analyst understands what the client requirements are- do they need a dashboard or they need a report or they need some kind of analysis on their product to provide some recommendation or anything else. After having good know-how of the client’s needs, they put in a plan of action –
It is often a data analyst’s job to communicate the plan of action to the team.
Having clearly defined the business problem, a data analyst determines what data needs to be collected from existing data sources or databases. Collecting data in the real world is not as easy as downloading a dataset from Kaggle. A data analyst does not always find perfectly pre-processed data to solve the business use case. The data can come from multiple sources be it a flat file, an API, or a SQL backup. A data analyst collates all the data in one place and works with developers to create an ETL pipeline for the same by setting up business rules to transform the data on it should look once it is loaded into the system. Having quality data and relevant data are vital to the data analysis process without which a data analysts’ insights would be irrelevant. A data analyst working at a start-up or a small company that has not invested in data infrastructure keeps a constant check on whether the company is collecting the right data or not. He/she also keeps an eye out for additional data sources to ensure data diversity throughout the analytics process.
Data is always messy, and data cleaning is important to make the data usable for later processes. Cleaning data is the most important but time-consuming and the least enjoyable part of a data analyst’s job. A data analyst spends as much as 90% of the time cleaning data (fixing structural errors, handling missing data, removing irrelevant observations, and filtering out unwanted outliers) because clean data is critical for gleaning valuable and accurate insights. So, if you are venturing out on the path of becoming a data analyst grab as many opportunities as you can to practice data cleaning skills on diverse analytics projects. Still not convinced why data cleaning is important to a data analyst job role, let’s understand it with an example.
Suppose that a data analyst collects data of different customers on Amazon who buys products produced by its in-house brand Symbol. Now the goal is to understand which products people buy the most so accordingly, the business can scale up the production of those products. However, if the data is corrupted or does not contain any valid values then the analysis would result in misguided decisions. Data cleaning involves normalizing the data, standardizing the data, and validating it so that it can be used for generating reports and visualizations in the later part of the analysis process.
The next most important thing a data analyst does is creating views for setting up the data for reporting. A view lets a data analyst combine the data from multiple tables into one and then select a subset of the data that is actually needed for creating visualizations or reports. Each view is formatted differently based on what it is going to be used for in the visualization or report.
As a data analyst, your time will be spent in reporting. It could be either maintaining existing reports and pushing them out to the business or creating new reports from scratch. These reports can be in the form of PowerPoint Decks, Excel Dashboards, or a report from any visualization tool like Tableau, Power BI, or QlikView. Some data analysts even use SQL to create reports and automate the process of creating reports with the latest data (weekly or monthly) through stored procedures. A data analyst creates reports or visualizations to make sure they have solved the defined business problem that they were intended to solve.
Also Read: Data Analyst vs Data Scientist
Data Analyst Responsibilities: A Day in the Life of a Data Analyst
Your real value to a company as a data analyst is being able to take a business problem or wanted outcome and figure out how to find the answer to the problem for which you will need the gold mine i.e. “Data”. Whenever you join a new company as a data analyst, the first thing your manager would ask you to do is figure out how to extract the data. You’ll have multiple ways to extract data it could be either through writing some SQL queries or programming with Python or R. Having done the data extraction, you would be expected to manipulate and visualize the data based on your previous experience and working knowledge of data visualization tools. Some companies have in-house data visualization tools that one might be required to learn. The next most important thing that you will have to do is analyze the data by going over the visualizations you’ve created and pinpoint if there are any data anomalies. The last step is creating reports wherein every organization has its own template for reporting and you’ll have to match up with their standard of reporting.
Once you match up with the standard of reporting, fit into the team and culture as a data analyst you will have to spend more time with data and identify any overlooked minute details missed out during the creation of reports. As a data analyst, your goal should always be to produce meaningful business insights and not noise. While the data analyst responsibilities will vary based on the specific position and the organization, however, there are some general data analyst day-to-day responsibilities that will cover almost every kind of data analyst job role. Here’s a quick breakdown of other day-to-day data analyst responsibilities apart from meetings and reporting–
Also Read: Types of Analytics
Most of the data analyst job descriptions that you come across are going to need a Bachelor’s Degree in Mathematics, Computer Science, Statistics, Engineering, Finance, Economics, or other relevant subjects. As you can see from the below data analyst job description that the data analyst qualification does mention a Bachelor's degree but at the same time emphasizes on hands-on experience -
Some senior data analyst job descriptions may list a Master’s degree as a good to have but not many data analyst jobs have that as a need to have requirement.
This does not mean that a Bachelor’s degree or a Master’s degree is a mandated data analyst educational qualification to become a data analyst. If working with data and numbers really excites you- and you have a knack for problem-solving- data analyst may be the right career choice for you regardless of your data analyst degree or education. Yes, you read that right! One can also become a data analyst without a degree. Choose your own self-learning path and hone the required data analyst skills by exploring and practicing diverse real-world business problems. Real-world data analytics projects in domains like Retail, Finance, Media, and others can teach you a lot about how analytics is actually implemented in the industry.
Having a data analyst degree may make it easier to land your first data analyst job, but a degree isn’t required if you’re skilled in analytics tools like Excel, SQL, Tableau, PowerBI, QlikView, and others. Whatever learning path you choose, make sure you build a strong data analytics portfolio.
One of the common questions that aspiring analysts ask our Project Advisors is “What data analyst certification should I get?” Beginners who do not have a formal data analyst degree or data analyst education think that getting some sort of a data analyst certification is important to landing their first data analyst job. Let’s make one thing clear: There is no need for any kind of data analytics certification to get an entry-level data analyst job. Recruiters do not care much about which data analyst certificate you have but rather they evaluate your hands-on experience using Python, R, SQL, Excel, Tableau, and other tools for tasks like predictive and statistical analysis, data collection, and data visualization. However, this does not mean that having a data analyst certification is not valuable.
A highly adept data analyst must master these data analyst skills -
This is not an exhaustive list of data analyst skills. The data analyst skill set is versatile and also includes many other technical and soft skills which would be covered in detail in the upcoming post. Of course, there’s a whole lot to learn as a data analyst but do not feel like you’ve to learn it all to become a data analyst. This quick analyst skillset checklist is enough and a must-have for anyone getting started with a career in data analytics. ProjectPro helps you learn the crucial data analyst skills through diverse projects because that’s what’s actually going to help you land top data analyst jobs.
According to PayScale as of April 7, 2021, the median data analyst salary in the US is $61,000 while in India it is 433, 395 INR.
On the other hand, the popular job portal Indeed puts the average data analyst salary in the US at $71,598. Apart from the salary there are other benefts that include a company pension scheme, variblae performance bonus, medical insurance, and commuter assistance. The location, level of experience, certifications, and education have a radical effect on the salary a data analyst earns.
For people interested in pursuing a career in data analytics it can be hard to know where to start. Data analytics is a subject that needs to be learned by doing, so all you need to do is get your hands dirty to completely grasp various analytic concepts. Practice as much as you can by getting your hands on a variety of real-world business problems to build your data analytics portfolio. The more projects you do and the more diversified problems you solve, the stronger your data analyst skillset is going to become.
When working as a data analyst learning will be a daily thing because the analytics industry is moving at a fast pace. The job role of a data analyst never stops evolving or improving, it is a never-ending whirlwind of discovery with data and numbers using novel tools and technologies. Use diverse large, intricate datasets to build real-life analytic solutions and learn how to productionize end-to-end data analytics projects. If you want to get started on your journey of becoming a data analyst without a degree, ProjectPro has a library of solved end-to-end analytics projects with valuable insights on how to productionize them in the real world. Employers often ask about the analytics projects in the portfolio and why they are there, with our end-to-end solved data analytic projects you get to know what and why a particular code piece is included. These analytic projects will take you through everything you need to know as a data analyst, from how to build and deploy an ETL pipeline to how to combine it with the analytics application. You’ll also build a job-winning data analyst portfolio so you can start applying for data analyst jobs. Breaking into your first data analyst job is a lot easier if you have a portfolio of independent projects that support your competence to do data analysis in the real world.
Also Read: Data Analyst Interview Questions and Answers