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A data analyst reviews data to identify key insights into a business’s customers and ways the data can be used to solve problems. They also communicate this information to company leadership and other stakeholders.
What are a data analyst’s day-to-day responsibilities?
It involves:
Gathering data
Cleaning data
Processing data
Producing reports
Spotting patterns
Collaborating with others and setting up infrastructure
Planning meetings:
They typically meet with the analytics team to discuss the tasks of the day and brainstorm for possible solutions. When everything is clear, They will start working on the data. Analyzing data consists of three main tasks: gathering the data, cleaning the data, and finally processing the data.
Data gathering:
Depending on the problem . gathering data is usually the most simple part of the process.
Data cleaning:
Cleaning the data, which is the next step, simply means going through the data and trying to understand it, making corrections where needed such as moving outliers or data that should not be included in the analysis.
This step can take a lot of time, but understanding the data is crucial in order for me to start processing the data.
Data processing:
The data processing part of the process is where they get to use my programming skills, which they use alongside several different data tools. They use these skills and tools to analyze the work and come up with solutions for the problem at hand.
Producing Reports:
After processing the data we will do this by putting together visualizations like charts and graphs, writing reports in Power BI or Tableau and presenting information to interested parties.
What tools do data analysts use?
During the process of data analysis, analysts often use a wide variety of tools to make their work more accurate and efficient. Some of the most common tools in the data analytics industry include:
Microsoft Excel
Google Sheets
SQL
Tableau
R or Python
SAS
Microsoft Power BI
Jupyter Notebooks
While Excel is a powerful tool in data analysis, it still has a lot of serious limitations. Excel can’t handle datasets above a certain size, and does not easily allow for reproducing previously conducted analyses on new datasets.
The main weakness of such programs is that it was developed for very specific uses, and do not have a large community of contributors constantly adding new tools. This is why I prefer using and R and Python.
Lets us talk about R ,Python, Power BI, Tableau and SQL
R and Python are the two most popular programming languages used by data analysts and data scientists.
Both are free and open source. R is used for statistical analysis, and Python is a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are both godsends.
R
To go into a bit more detail, R is one of the most frequently used tools in data science and machine learning. Over the last few years R has become the golden child of data science.
It’s used frequently to unlock patterns in large blocks of data and was designed by people like me, statisticians, to make our work easier. It’s one of the most must-know programming languages in the field of data analytics and data science.
Python
Python is also one of the most popular languages in data analysis. Python is an ideal language because it’s widely used in scientific computing, data mining, and others.
Power BI
Power BI is a business analytics service by Microsoft. It provides interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create reports and dashboards. It allows users to connect to multiple data sources, clean and transform data, create custom calculations, and visualize data through charts, graphs, and tables.
Power BI can be accessed through a web browser, mobile device, or desktop application and integrated with other Microsoft tools like Excel and SharePoint. With its powerful data modeling and analysis capabilities, Power BI enables organizations to make data-driven decisions and gain insights into their business performance.
Tableau
Tableau is a data visualization and business intelligence tool that enables users to connect, visualize and share data in a highly interactive and intuitive way. It allows users to quickly analyze and explore large and complex datasets using a drag-and-drop interface without requiring coding or programming skills. Tableau provides a wide range of chart types and visualization options, such as line charts, bar charts, maps, scatter plots, and many more.
Tableau can connect to various data sources, including databases, spreadsheets, big data platforms, and cloud services. It also allows users to perform data cleaning and transformation tasks, create custom calculations, and generate insights using advanced analytics features.
Tableau provides a suite of products, including Tableau Desktop for creating and publishing visualizations, Tableau Server for sharing and collaborating on data and visualizations across an organization, and Tableau Prep for preparing and cleaning data before analysis. With its user-friendly interface and powerful visualization capabilities, Tableau has become popular for businesses and organizations looking to make data-driven decisions.
SQL
Structured query language (SQL) is a programming language for storing and processing information in a relational database. A relational database stores information in tabular form, with rows and columns representing different data attributes and the various relationships between the data values.
Data analyst career advancement
Being a data analyst can also open doors to other careers. Many who start as data analysts go on to work as data scientists. Like analysts, data scientists use statistics, math, and computer science to analyze data. A scientist, however, might use advanced techniques to build models and other tools to provide insights into future trends.
Reference:
https://www.coursera.org/articles/what-does-a-data-analyst-do-a-career-guide
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