The basic job of a data analyst is to collect, store, and analyze data and draw some insights from it. Size/Volume/Complexity of data depends upon the type of company/industry in which one is working on.

Most small-scale companies will not have a large volume of data that needs to be analyzed. Microsoft Excel or similar spreadsheet solutions can be used to process or summarize data. One caveat of Microsoft Excel is that it cannot be used to summarize data with millions of records. In this scenario, one should have the skills to query the database tables directly using Structured Query Language(SQL) and summarize the data. Most of the time you need to use SELECT statement, GROUP BY, ORDER BY, JOIN, UNION, and other complex queries and SQL functions to summarize it.

There are different kinds of organizations (commercial and non-commercial ones) that need to analyze their data to derive intuition and future directions from them. The analysis and tools needed by a data analyst depend upon the complexity and size of the data. One can use either Spreadsheet or SQL for simple analysis. If the data is big and complex, they might have used some big data technologies that can deal with unstructured data.

If the size of the data is big in a company to be called big data, it would be stored in the Data Warehouse based on Hadoop or a similar solution. Once you are good with SQL, you can also learn HQL (Hive Query Language) which is a data warehouse solution Language on top of Hadoop/HDFS.

Examples of Data Analysis in Organizations

Data analysts play a variety of roles in different industries. Some important ones are listed below.

• E-Commerce/Retail

Organizations are interested to know the Yearly/Quarterly/Monthly/hourly sales/revenue of certain products at certain geographical locations about certain demography (Age/Gender/income).

• Hospital/Healthcare

Organizations are interested to know the total patient’s intake/outtake time/cost/success rate from their existing data, the total number of health insurance filed from a certain region, and age groups.

• Food

Organizations are interested to know the good restaurants nearby with 4+ ratings out of 6, Mexican/Chinese/Nepalese/Indian nearest in certain areas.

• Tourism

Organizations in the tourism sector are interested in knowing the below information.

> the total number of tourists visiting a certain country or a region during a certain time of the year.

> Number of days tourists stay in certain hotels

> Types of food tourists are interested in buying.

• Weather

Organizations are interested in knowing today/tomorrow’s weather. This is provided by a third-party company through a weather API (Application Programming Interface), which will query a database(SQL, NoSQL) on the back end.

• Advertisement

Organizations are interested to know how many people saw the advertisement on TV/Website/YouTube Channel, and how many people clicked on the advertisement. So we want to calculate Reach, Impression, and CPM (Cost per Thousand) for the advertisement.

Some of the above use cases will be executed in the background when you use some kind of app/website. But the underlying mechanism to pull the data should be useful for all data analysts as data will be stored in a database or a Hadoop cluster.

Skills Needed for Data Analysis

If you work in a typical tech company with millions or billions of dollars of revenue, you are expected to know or learn many of these tools and skills mentioned for data analysis.

Communication:

Since a data analyst meets with the client on a day-to-day basis, you need to have good communication skills.

Microsoft Excel:

It is a widely used tool by Data Analysts, Data Engineers, Data Scientists, and their managers. Make sure you learn different ways in which Excel can be used for data analysis.

SQL or Structured Query Language :

SQL is a powerful language using which a data analyst can pull data from tables having a thousand, a million, or a billion records. Once you learn SQL properly, you can use it to query data from a Data Warehouse built on either RDBMS (Relational Database management System) like Oracle, MS-SQL, or Hive based on the Hadoop stack.

HQL or Hive Query Language is used along with Big data tools, which is a similar language to SQL. Knowing SQL and HQL will make you take on data analyst or Big data analyst solutions pretty easily.

Programming Language:

There are not many data analysts that use languages like Python, SAS, and R language for day-to-day tasks. But if you want to progress in your career within an organization, you better start learning tools like Python, SAS, and R.

Conclusion

In this blog post, we learned about the different skills data analyst needs and the examples of different organizations where data analyst is needed.

Please share this blog post on social media and leave a comment with any questions or suggestions.