5 tools to boost productivity in data analytics

1. Power BI Dashboard Powerhouse:

Power BI is a business analytics tool from Microsoft. Users can create interactive dashboards and reports for their needs and also connect with a wide variety of data sources.

Why it boosts productivity:

Power BI turns complex datasets into clean and structured formats with the help of Power Query, allowing for easy analysis to find the advantages and disadvantages related to predictions for the future, improve productivity, and create interactive and real-time dashboards within minutes. Automation data refreshing, scheduled updating hourly or weekly (trigger refresh, scheduled refresh, on-demand refresh, and direct query refresh are the types of schedule).

In Power BI, more visual graphs are there that are used to create interactive dashboards easily. Its user-friendly interface (drag and drop the fields) is easy for beginners.

KPI helps to indicate the target values and compare actual profit with target profit. It views the almost reached place and finds how much percentage to reach our goal.

DAX language is used to create advanced calculation and aggregation functions, it is super-fast compared with other fields. Based on that, we create visuals, and it is suitable for additional custom visuals as well, and explore KPI and specific-downs (tables have relationships). Example: country, region, state, city.

Example: Power BI used to analyze EuroMart sales and global superstore sales, reducing reporting time by 60%.

2. MySQL—Mastering the data—Structured Querying Database:

SQL—Structured Query Language is used to manage relational databases (primary and foreign keys) like data management, powerful data querying, high performance for large datasets widely supported by all major dataset systems, easy to learn, and essential for data analysis.

Why it boosts productivity:

SQL is the basis for a data analyst's skill set, it structures data in a tabular form. It is mostly suitable for large datasets for querying data, and it generates insights without needing to export data into Excel or Python. SQL queries using common table expressions make code easier to maintain and debug. (select, from, where, group by, order by, limit, offset—these are the steps used in code). Joins are also important to combine tables based on related columns for analysis, creating tables helps to create a new dataset to their needs, and sub-query, call function, partition, group by, and rank function help to sort input for analysis.

Example: In the EuroMart sales project; it helps to segment customers and calculate frequency and monetary values to understand the purchasing behavior of customers, tailor marketing efforts, and analyze peak order times efficiently.

3. Python—Automation—ETL Process:

Python is a high-level interpreted programming language known for its simplicity and readability , extensive libraries, object-oriented nature, strong community support, and simple syntax for rapid development.

Why it boosts productivity:

Python, automate tasks that used to take hours, like cleaning (remove duplicates, fill the suitable values in missing rows, and reduce inconsistencies) and running advanced analytics. Extensive libraries like pandas (data manipulation), Matplotlib (basic visualization), seaborn (advanced visualization), and scipy (statistical analysis) are lifesavers. Use Jupyter notebooks for interactive analysis and documentation. In Python we use types of operation, range functions, conditional statements, handling datasets, exceptional handling, decorators, OOP functions, and modules. Build reusable scripts for repeatable tasks like EDA (Exploratory Data Analysis).

Example: In global superstore raw dataset and output clean visuals, saving 70 minutes per analysis.

4. Excel—Good tool with the right trick:

Microsoft Excel is a widely used spreadsheet program primarily used for organizing, analyzing, and manipulating data. It's a powerful tool for performing various calculations, creating charts using pivot tables, and generating reports. For automation, use macros and the Visual Basic Editor.

Why it boosts productivity:

Don’t underestimate Excel because it has pivot tables, power query, and conditional formatting. Excel can slice and dice data without writing complex code. It's better to learn shortcut keys and create templates for repeated tasks.
Explore Power Query inside the Get Data feature for data transformation and create visual graphs using Pivot or PowerPivot Tables.

Example: Ola and Pizza sales use dashboard in Excel to understand trend patterns using dynamic charts and slicers.

5. Notion/Google Keep—Knowledge Management:

Why it boosts productivity:

Users used to lose a lot of time switching between tasks and searching for old code , but now it's useful to stay organized using Notion to document user analysis and save reusable snippets of code and track the projects and work much more efficiently and easily to manage and automate tasks.

Conclusion:

The real game changer is using these tools to analyze datasets efficiently. Each tool plays a role, and together they make users a faster, smarter, and more efficient analyst.