Who is a Data Analyst?
Data analytics enables organizations to analyze all their data (real-time, historical, unstructured, structured, qualitative) to identify patterns and generate insights to inform and, in some cases, automate decisions, connecting intelligence and action. Today’s best solutions support the end-to-end analytical process from accessing,
Data Analysts are experts dealing with data who can translate numbers, figures, statistics, and other similar data into English so that every member of the organization can easily comprehend it.
Daily, over 2.5 quintillion bytes of data is generated across the world, and it is the job of a Data Analyst to extract valuable information from that pile of data and present a comprehensive form of the same, like reports and visualizations, to the stakeholders.
There is always room for improvement for Data Analysts as it is a fairly new field, and new advancements keep coming in day by day. It is the perfect career option for professionals who have a stronghold in statistics, math, and computer science.
What does a data analyst do
Generally speaking, though, the responsibilities of a data analyst typically include the following:
- Designing and maintaining data systems and databases; this includes fixing coding errors and other data-related problems.
- Mining data from primary and secondary sources, then reorganizing said data in a format that can be easily read by either human or machine.
- Using statistical tools to interpret data sets, paying particular attention to trends and patterns that could be valuable for diagnostic and predictive analytics efforts.
- Demonstrating the significance of their work in the context of local, national, and global trends that impact both their organization and industry.
- Preparing reports for executive leadership that effectively communicate trends, patterns, and predictions using relevant data.
- Collaborating with programmers, engineers, and organizational leaders to identify opportunities for process improvements, recommend system modifications, and develop policies for data governance.
- Creating appropriate documentation that allows stakeholders to understand the steps of the data analysis process and duplicate or replicate the analysis if necessary.
Top Must-Have Skills For Data Analysts
The majority of companies today realize the value of a data-driven business strategy and are in need of talented individuals to provide insight into the constant stream of collected. information.
Essential Skills for Data Analysts
Statistics for Data Analytics
Almost every business requires someone who knows SQL, whether it’s to manage and store data, connect multiple databases (like the ones Amazon uses to suggest alternatives you might like,) or develop or update database architecture entirely. Thousands of job posts requiring SQL abilities are submitted each month, with a median compensation of well over $75,000 for someone with expert SQL knowledge. While even non-techies can benefit from knowing SQL, understanding SQL is the first step toward working with Big Data.
SQL, or Structured Query Language, is the ubiquitous industry-standard database language and is possibly the most important skill for data analysts to know. The language is often thought of as the “graduated” version of Excel; it is able to handle large datasets that Excel simply can’t.
When you think of Excel, the first thing that comes to mind is likely a spreadsheet, but there’s a lot more analysis power under the hood of this tool.
Stressing the importance of Microsoft Excel skills almost seems laughable when one considers the significantly more advanced technology data analysts have at their disposal. To borrow a quote from Irish business writer Anne Walsh, “Mention Excel to techies, and it’s often dismissed with a sniff.”
Using data to find answers to your questions means figuring out what to ask in the first place, which can often be quite tricky. To succeed as an analyst, you have to think like an analyst. It is the role of a data analyst to uncover and synthesize connections that are not always so clear. While this ability is innate to a certain extent, there are a number of tips you can try to help improve your critical thinking skills.
R or Python–Statistical Programming
Anything Excel can do, R or Python can do better—and 10 times faster. Like SQL, R and Python can handle what Excel can’t. They are powerful statistical programming languages used to perform advanced analyses and predictive analytics on big data sets. And they’re both industry standard. To truly work as a data analyst, you’ll need to go beyond SQL and master at least one of these languages.
As the term suggests, data visualization is a person’s ability to present data findings via graphics or other illustrations. The purpose of this is simple: It facilitates a better understanding of data-driven insights, even for those who aren’t trained in data analysis. With data visualization, data analysts can help a business’s decision-makers (who may lack advanced analytical training) to identify patterns and understand complex ideas at a glance. This capability empowers you — the data analyst — to gain a better understanding of a company’s situation, convey useful insights to team leaders, and even shape company decision-making for the better.
As artificial intelligence and predictive analytics are two of the hottest topics in the field of data science, an understanding of machine learning has been identified as a key component of an analyst’s toolkit.
Power BI. Unify data from many sources to create interactive, immersive dashboards and reports that provide actionable insights and drive business results.
Power BI can provide us with not only quick analyses and insights, but also big-scale real-time analytics on enormous amounts of data. As a result, Power BI can support with data analytics demands not only for small businesses, but also for major enterprises dealing with massive amounts of data.
Python is one of the most popular programming languages today.
Many professions will almost certainly demand a thorough understanding of how to use Python for data analytics. Even if it isn’t a needed talent, knowing and understanding Python will provide you an advantage when it comes to showing the importance you can bring to potential employers.
Consider participating in our Python for Data Science: Web Scraping and Data Gathering course if you’re ready to enhance your programming language skills, learn how to manipulate and analyse data, grasp the notion of online scraping and data collection, and begin constructing web applications.
Career and salary
Experience plays a deciding role in finalising the salary of a data analyst. One’s experience in the field depicts more extensive knowledge, practical solutions, agile working and leadership skills to train others. Therefore, an analyst in the industry for more than three years will have an increased payment than the salary of a data analyst for a fresher.
The average pay structure for data analyst based on experience is as following:
Entry Level (> 1 yr of experience) – ₹342,363/yr
Early Career (1-4 yrs of experience) – ₹422,408/yr
Mid- Career (5-9 yrs of experience) – ₹690,734/yr
Experienced (>10 yrs of experience) – ₹942,653 to ₹1,750,000/yr
Data Analyst Salary: Based on Location
The salary of a data analyst for a fresher might be different if located in Mumbai from the data analyst salary in Bangalore. This is because the location is among one of the factors affecting the data analyst job salary.
Every city or state has a different cost of living and demand of the profession and accordingly the pay is decided.
Job Roles in Data Analysis
Job Roles in Data Analysis
- Research Analyst
- Operations Analyst
- Risk Analyst
Why become a data analyst?
1. Highly in-demand field
Data Analysis is one of the most in-demand jobs for 2021. After United States, India is the second prominent hub of jobs for data scientists. Demand is one of the reason data analyst salary is significantly high.
2. Highly Paid & Diverse Roles
Not only is the demand for data scientists booming, but the kinds of job positions are also abundant.
3. Evolving workplace environments
Data science is shaping the workplace of the future. With the advent of artificial intelligence and robotics, more and more routine and manual tasks are getting automated.
4. Improving product standards
Usage of machine learning has enabled companies to customize their offerings and enhance customer experiences.
5. Helping the world
Predictive analytics and machine learning have revolutionized the healthcare industry.
How to become Data Analyst
- Earn a bachelor’s degree in a field with an emphasis on statistical and analytical skills, such as math or computer science
- Learn important data analytics skills
- Consider certification
- Get your first entry-level data analyst job
- Earn a master’s degree in data analytics
Is Coding Required to be a Data Analyst?
It depends upon the job the employer .Some data analysts are proficient in programming languages while others may use analytics software or Excel to analyze data and provide insights. Whether or not coding is required for a data analyst typically depends on the job or the employer