How to Become a Data Analyst In 2022-23!

Data Analytics Certification

Data Analyst is the hottest job market of the 21st century, but with the hefty hires comes the strike of huge competition. So now, how to get into data as a fresher? How to start as a beginner? By standing out. And how do you stand out?

Let’s decode how

Learn the right skills.
The Internet is filled with an inviting amount of resources, courses, and blogs. The human mind easily gets tricked with the candescent object pattern and goes after new, better coffers rather than erecting a skill only to end up in tutorial hell. It’s time to prioritize the skills you want to learn and the material demanded before starting and sticking to it.
Remember, you don’t need to learn everything in the job description. Utmost JDs are inadequately drafted google search mess and there are only a handful of skills you need.
The top skills you’ll need to be a Data Analyst are
Data visualization tool ( Tableau/ Power BI)

Let’s focus on each one independently

1. SQL
SQL is the meat and potatoes of every Data Analyst. It can be used to
transfigure data from relational databases and is extremely scalable. It’s the skill you need to get if you’re someone just getting into data.
You can use resources like Mode and SQLBolt for hands-on interactive learning in your browser itself.

2. Data visualization tool ( Tableau/ Power BI)
Being a data analyst you spend a lot of time generating reports to communicate your findings to other teams through visualization. Tableau and Power BI is what utmost companies use these days. And adding ANY one of them to your toolbelt would be a great plus.
The stylish resource for this to my knowledge is Youtube, tons of tutorials. But always remember to get your hands dirty. It’s only when we try effects out that we learn.

3. Excel
Excel is used by nearly everyone and isn’t indeed mentioned independently as a skill in the job description since it’s assumed you have it. It’s a MUST-HAVE skill data analytics in 2022 not just for the data community but for everyone.

4. Python
Python is a programming language and extremely useful to controvert, clean, visualize and model data each in one place still it can be daunting for an absolute beginner. So it’s recommended to take this is as the last step while not getting overwhelmed with all it can do. You don’t need to know it all, you can learn while on the job.

Responsibilities of a Data Analyst

The data analyst designation comes with a bunch of places and responsibilities. And the first step to getting a data analyst in understanding the responsibilities of one! Some of the common and expected responsibilities of a data analyst are

1. Understanding the Goal
First and foremost, a data critic must identify the organization’s goal. They must assess the available resources, comprehend the business problem, and collect the right data.

2. Querying
Data analysts write complex SQL queries and scripts to gather, store, manipulate, and retrieve information from relational databases such as MS SQL Server, Oracle DB, and MySQL.

3. Data Mining
Data is mined from a plethora of sources and organized to obtain new details from it. By doing so, data models are built to increase the effectiveness of the system.

4. Data Cleansing
Cleaning and data wrangling is the vital duties of a data analyst. The data gathered initially will often be messy and have missing values. Hence, it’s pivotal to clean the collected data to make it ready for the analysis purpose.

5. Data Examining
Data analysts use analytical and statistical tools, including summer programming languages, for carrying out a logical examination of data.

6. Interpreting Data Trends
Data analysts use colorful packages and libraries to spot trends and patterns from complex datasets, thereby discovering unseen business insights.

7. Preparing Summary Reports
Data analysts prepare summary economy reports with the help of data visualization tools. These reports guide the leadership platoon to make timely decisions.

8. Uniting with Other Teams
Data analysts interact with the management platoon, development team, and data scientists to ensure proper implementation of business requirements and figure out process improvement opportunities.


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