Excel for Data Science: A Comprehensive Guide, Learn And Enhance Your Excel Career

Excel is a programme that's quite useful for data science and is widely used in the corporate world.

Excel for Data Science: A Comprehensive Guide, Learn And Enhance Your Excel Career

Naman Sharma | Oct 21, 2023 |

Excel for Data Science: A Comprehensive Guide, Learn And Enhance Your Excel Career

Excel for Data Science: A Comprehensive Guide, Learn And Enhance Your Excel Career

Excel is a program that’s quite useful for data science and is widely used in the corporate world. Excel is an excellent place to start for aspiring data scientists or professionals who need quick and easy data analysis because it has a ton of functions and features and an easy-to-use interface.

We’ll explore how Excel can be used for data science activities in this article. Here’s a detailed guide that covers everything from data cleaning to visualisation:

1. Importing Data

Excel can import data from a variety of formats, including:

  • CSV: Select ‘File’ > ‘Open’ and then your CSV.
  • Use ‘Data’ >Get External Data’ > ‘From Web’.
  • Other Databases: Go to ‘Data’ > ‘Get External Data‘ and select an option (e.g., SQL Server, Access).

2. Data purification

This is most likely the most time-consuming aspect of data analysis.

  • Missing Values: To identify them, use functions like ISNA() or IFERROR(). Interpolation or mean/median values can be used to fill them in.
  • Text to Columns: If your data isn’t properly split, utilise ‘Data’ > ‘Text to Columns’ to separate it.
  • Find and Replace: This feature is useful for changing values or fixing misspellings.
  • Remove Duplicates: For unique values, go to the ‘Data’ tab > ‘Remove Duplicates’.

3. Data Transformation

Transform raw data into a more palatable format.

  • SUM() and AVERAGE() are two simple and advanced formulas.
    Pivot Tables: These are handy for data summarization. Simply select ‘Insert’ > ‘Pivot Table’ from the menu.

4. Data Analysis

This is where the magic takes place.

  • Descriptive Statistics: Use the ‘Data Analysis’ tool (needs the Toolpak add-in) to obtain information such as mean, median, standard deviation, and so on.
  • Trends and Patterns: Functions such as CORREL() can be used to determine the correlation between variables.
  • Regression Analysis: The ‘Data Analysis’ tool, once again, provides basic linear regression functionality.

5. Illustration

Excel includes a plethora of charting capabilities.

  • The ‘Insert’ tab contains basic charts such as bar, line, pie, and more.
  • Histograms and scatter plots are useful for analysing distributions and relationships.
  • Conditional formatting is a non-chart method of displaying data based on circumstances.

6. Powerful Tools and Functions

Excel’s powers extend beyond the fundamentals.

  • Solver and Goal Seek are used to solve optimisation problems and locate particular solutions.
  • Automate repetitive processes and create bespoke routines with macros and VBA.
  • Power Query and Power Pivot are advanced data import and modelling tools.

7. Programming Integration

Excel interfaces with programming languages such as Python and R for more skilled users, allowing for even more rigorous analysis. Python programs can be executed directly in Excel using Microsoft’s Power Query.

8. Communication and Reporting

Finally, once you’ve completed your analysis, Excel provides capabilities for presentation and cooperation.

Dashboards are interactive reports that combine charts, pivot tables, and slicers.
Export: To share your workbook, save it as a PDF or XPS file.
Collaboration: Office 365 allows several users to collaborate in real-time.

Conclusion

While advanced data scientists frequently overlook Excel in favour of specialised tools, it is a versatile and strong application for a range of stages in the data science pipeline. Excel provides a plethora of tools to help you along the road, whether you’re just beginning or searching for a tool to quickly analyse and visualise data.

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