A data analyst collects, curates, retrieves, and interprets data to make it useful for business, finance, science, manufacturing, medicine, and governmental organizations. There are a magnanimous number of about two and a half quintillion bytes of data existing on earth and is increasing daily. As for recent years, the business analytics market and global big data are rapidly growing and are worth of hundred billion dollars.
Today majority of the businesses recognize the importance of a data-driven business strategy and are looking for competent data analysts with essential skills to help them interpret and make goal-oriented strategies. Job candidates with data analyzing skills are given preference in the related jobs by the recruiters.
However, a job in data analytics will not be worthwhile unless you put in a lot of time and effort to acquire the specific skill-set of a data analyst. To succeed, data analysts need technical knowledge as well as soft skills. There is no one-size-fits-all approach to acquiring these skills.
There is a master’s degree related to data analysis but many people are taking data analyst courses to learn the required skills. However, regardless of which path you choose, you will need to develop a strong set of abilities to become a data expert.
6 Must-Have Skills for Data Analyst
- SQL and NoSQL
- Data Visualization
- Data Cleaning
- R or Python
- Machine Learning
- Microsoft Excel
SQL and NoSQL
Companies all across the world have adopted SQL for long-term efficacy. As a data analyst, you should be familiar with SQL even if you don’t need it. MySQL, for example, is a branded version of SQL that allows you to learn more about relational database management systems. SQL is perhaps the most important skill to acquire to land a data analyst job. Because all data analysts will need to access data from a company’s database eventually. There are thousands of job listings each month that need SQL abilities, and the median income for someone with advanced SQL skills is well over $75,000 per year.
On the other hand, you should work on honing your skills using NoSQL databases. NoSQL systems do not structure data in the same way that SQL does. NoSQL frameworks can successfully arrange their data in any way, as long as the technique doesn’t seem to be interrelated.
Data visualization is the capacity to interconnect data findings using graphics or other representations like exceptional charts and diagrams. Data analysts can use data visualization to assist corporate decision-makers in seeing trends and comprehending complicated concepts at a glance. It allows individuals, even those who aren’t educated in data analysis, to have a better understanding of data-driven insights.
Data analysts who work with data visualization are required to respond to problems more quickly and be able to explore fresh insights. It encourages data research that is more innovative and has an out-of-the-box outlook.
R or Python
R or Python can accomplish anything Excel does and manage more than Excel. They are statistical programming languages used to do analysis and gather analytics on large data sets. R and Python are both open source and free, and most businesses don’t mind whatever their workers use as long as their analyses are correct.
As they are both industry-standard skills, you’ll need to understand at least one language, R or Python, in addition to SQL to become a data analyst expert. However, R was designed particularly for analytics. Thus some analysts prefer it to Python for examining data sets and performing ad-hoc analysis.
While understanding who to collect and analyze data, knowing how to manage those data is also an essential skill. Data architects and engineers, database administrators, and information security analysts are the dedicated roles for data analysts which are assigned in a company for data management.
For data management and storing, different organizations use different cloud services and programs. So, you have to have in-depth knowledge of databases to be a pro in data management.
Machine learning has been highlighted as a vital component of an analyst’s tool-set since AI and predictive analytics are two of the trendiest subjects in the field of data science. Building algorithms to detect patterns in large data sets and increasing their accuracy over time is the emphasis of this expertise. While not every analyst works with machine learning, understanding the tools and ideas is essential for getting forward in the industry.
Microsoft Excel has a lot more analytical ability than just a spreadsheet. Excel is used for developing macros and employing VBA lookups which are still utilized for smaller lifts and rapid analytics. Microsoft’s Analysis ToolPak is designed for statistical modeling and data analysis.
If you work for a small business or a startup, the first version of your database might be Excel. The tool has been a pillar for organizations in every industry throughout the years, so it’s essential to know how to use it.