DEV Community: Peter Maina

Understanding the Git Workflow: Working Directory, Staging, Commit and Push

Peter Maina — Sun, 23 Aug 2026 12:35:02 +0000

Git is a version control system used in data and software related projects. It helps the analysts and engineers in tracking the changes of their projects, collaborating with other engineers and maintaining the history of their projects.
The git workflow can be categorized into four main stages:

Working Directory
Staging Area
Commit
Push

Knowing how to use the above stages makes it easier for you to manage projects using GitHub platform and git.

Working Directory

It is a folder in your local repository where the project files are located. If you have a folder called Weather_Project_Analysis. Inside the folder, you can have project files such as:

Weather_statistics.csv
README.md
Weather_analysis.sql

Git tracks the changes and manages the history of these files.
First command;

pwd

which means Print Working Directory. It helps you locate your project folder in your local computer before running git commands.

Git status

It checks the status of your project. Which projects are untracked and modified.

The above screenshot shows the output after running a git status command. It shows that my files are untracked by git and the changes only exist in my working directory.

Staging Area

A staging area is a preparation area where you tell git which files to be included for tracking changes.

To add a README.MD file to the staging area use:

git add README.md

To add several files in your staging area, you add by specifying their names;

Git add README.md weather_analysis.sql weather_statistics.csv

To add all the files use the command;

Git add .

Now we have staged the files, lets now check the status.

The screenshot above shows that our files are ready to be committed. The staging area is very important since it controls which files you want to be committed.

Commit

It is a saved snapshot of your project at a particular point so that git remembers how your project looked like at that particular time.
Now you can create a commit of your changes in the staging area using:

Git commit -m ”Add my weather project files”

The text inside your double quotation marks is called a commit message. A commit message describes which changes were made. At this point, the changes are now saved in your local git repository.

The above screenshot shows that your changes are committed and saved in your local git repository.

Push

The last stage is publishing your commit to a remote repository such as GitHub, GitLab etc. A remote repository is an online storage that allows you to back up your project files. Allows you to access your work from any other computer and also collaborate with other engineers and developers.
To publish your changes to GitHub, use the command;

Git push origin main

Git push; pushes your commits to an online repository.
Origin; It’s the remote repository.
Main; It’s the branch you are pushing your commits to.

After a successful push, your changes will be published on GitHub.

The git workflow can be simplified as follows:

Working Directory

git add

Staging Area

git commit

Local Repository

git push

Remote repository (GitHub)

The commands move your changes from one point to another.

Conclusion

Git workflow is a critical skill for any software developer/engineer, data analyst/engineer or data scientist. The git workflow follows a simple sequence.

Working Directory -> Staging Area -> Commit -> Push

A working Directory is where you make changes, staging area is where you want your changes to be saved, a commit records your changes in your local repository and a push publishes your commits to a remote repository such as GitHub.

Introduction to python as a data analytics tool

Peter Maina — Thu, 10 Oct 2024 09:06:26 +0000

Python is one of the most popular programming language for data analysis, because of its simplicity and flexibility. It also has a lot of frameworks and libraries designed to handle analysis and visualization of data. Its friendly to both beginners and experienced analysts as it offers a wide range of tools that optimizes and streamlines their workflows.

Why python for data analytics?
Easy to learn. Python is very easy for beginners since its syntax is clear and readable. It helps analysts to focus more on problem solving rather than trying to understand complex syntax.

Data manipulation and cleaning. Pandas is a powerful Python library tool for cleaning and manipulating data into useful and insightful data. These tasks include handling missing values, filtering data, creating new features , and merging datasets.

Data visualization. Libraries such as matplotlib and seaborn has a range of plots and charts for data visualization, helping analysts to communicate insights visually. These libraries can generate bar plots, line charts, heatmaps, and many more.

Integration with databases. Python is able to integrate with a wide range of databases such as SQL, Mongo dB, and SQLite which makes it easier to extract, manipulate, and analyze data.

Basic workflow of data analytics with python.
Data collection. Involves extracting data from various sources such as excel, databases, APIs, and scrapping the web.

Data cleaning. Handling missing data, correcting data types, removing duplicates, and filtering irrelevant data.

Exploratory Data Analysis. Analyzing data and generating visualizations to identify patterns and to draw insights.

Visualization and reporting. Visualization tools and libraries are used to create dashboards, plots and reports.

Conclusion.
Python is a powerful and flexible tool for data analytics. Its ecosystem provides everything from basic data manipulation and cleaning to advanced machine learning. Python libraries offer solutions for analyzing and visualizing data while working with both small and large datasets.

AN ULTIMATE GUIDE TO DATA ANALYTICS, TECHNIQUES AND TOOLS

Peter Maina — Tue, 06 Aug 2024 14:12:41 +0000

An ultimate guide to Data analytics, techniques and Tools

Data analytics is the process of analyzing raw data in order to draw meaningful and useful insights, which are used by businesses to make informed and smart business decisions. It helps to predict future trends and behaviors instead of basing your strategies and decisions on guess work, thus making informed decisions according to what the data is telling you.

There are four key types of data analytics: predictive, descriptive, diagnostic, and prescriptive. Each of them helps the organization to make informed decisions and they tells us the following:
 Descriptive analytics tells us what happened in the past.
 Diagnostic analytics tells us why something happened.
 Predictive analytics tells us what will likely happen in the future.
 Prescriptive analytics tells us how to act and decisions that should happen.
The engineers who work with data explores these four areas using data analysis process, which includes identifying the question, collecting raw data, cleaning data, analyzing data, and interpreting the results.
Below are some of the data analytics techniques used by data analysts.
 Regression analysis which is used to estimate the relationship between a set of variables.
 Factor analysis helps the data analysts to identify the underlying variables that drives people’s behavior and the choices they make.
 Cluster analysis helps in identifying structures within a dataset.
 Time-series analysis helps in measuring the same variable at different points in time
Now lets us look at some of the tools which data analytics engineers might work with.
 Microsoft Excel
 Tableau
 SAS
 RapidMiner
 PowerBI