DEV Community: Christopher Njoroge

Understanding the Git Workflow:Working directory,staging ,commit and push.

Christopher Njoroge — Mon, 24 Aug 2026 12:42:33 +0000

What is Git and Github

This is a version control system or tool used to track changes by developers.
When one installs git it comes with an inbuilt terminal called gitbash

Github is a cloud based platform for storing git repositories online.
Just sign up for free,verify via email and your account is created.
git and github are connected using a SSH KEY.

How Git works.

We start by installing git on my Pc, after installation check if git is installed by opening a terminal eg powershell on windows and run git --version

Stages

Git/Github is broken into four simple stages:

working directory is where we write code and amend and delete files. Here changes are made but cannot be tracked unless they are instructed to commit.
staging phase is an area where files are modified.
commit phase is where git takes everything from the staging area and sends it to our local repository.
push phase is where the saved commits are sent to a remote repository like GitHub.

Creating folders and files on git bash

First identify where we want the folder to be located ls is used to list mkdir "name of the folder" (means make directory) cd " name of the folder"(change directory)

Readme texts README.md end with .md since they are written using markdown language.Can use echo,touch or nano commands to write a readme file.
If i want to know the contents of my readme file we use:cat README.md

git config-this is basically telling it my identity
git config --user.name"user" git config --user.email "useremail"
git init-this command is used to create or initialize a repository in main/master.
git init main
git status-shows the repository status. This command shows changes and what is happening in git
git status
git add-stages changes made
git add .this means stage all or one can specify what to be added e.g i want to add only a javascript folder git add script.js
git commit-commits records that have been staged in the local git repository.Its like getting a snapshot or memory of the file.
git commit -m "commit message"
git log-shows a history of all the commits that were made.
git log

git branch should return main

To upload my git folder on github:

Open the github account,create a new repository,click on ssh and copy the link
git remote add origin "paste the ssh key link"

git remote -v

git push-this command to send commits to the remote repository git push -u origin main. Git will send the files to my github repository,i can refresh my github and see them.

What is a SSH KEY?

SSH stands for secure shell which is a protocol that allows a computer to communicate with another computer over a network.

Generating an SSH key?
ssh-keygen -t ed25519 -C "email signed on github.com"and press enter, the passphase(password authentication)is optional.
This command will generate two keys:example
ed25518
ed25518.pub
The one with .pub is the public key which is the one that we share with github while the other one is a private key which should not be shared.

Adding the SSH Key to Github-open my github account,on settings click on SSH and GPG keys, create a new key and copy the public key that we generated.
Testing the SSH connection
Test before pushing a project using ssh -T git@github.com

Understanding Your Data: The Essentials of Exploratory Data Analysis

Christopher Njoroge — Fri, 16 Aug 2024 10:41:36 +0000

Exploratory Data Analysis(EDA) is a vital process in Data science since it helps in understanding the data you are dealing with and also making conclusions about it. EDA serves as a bridge between the process of data collection and the processes of building machine learning models.

What is EDA?

EDA is the process of analyzing data the discover insights, trends, patterns, anomalies, test hypotheses and also making conclusions from the data.

Types of EDA

1.Univariate Non-graphical- The data has only one variable and no relationships in univariate non-graphical EDA.

2.Multivariate Non-graphical- This depicts the relationship between two or more data variables using cross-tabulation or statistics.

3.Univariate graphical- Quantitative and objective, they are not able to give the complete picture of the data; therefore, graphical methods are used more as they involve a degree of subjective analysis, also are required.

4.Multivariate graphical- It represents the relationship between two or more data sets. It uses graphics to display relationships between two or more sets of knowledge. The most popular graphic is a bar plot or a bar chart.

Process of EDA

1.Cleaning your Dataset
When your dataset is first loaded into the coding environment of your choice, the most crucial step is to clean the dataset before analysis begins as a 'dirty' dataset is compromised and will affect the accuracy of your analysis. Some of the key steps in this stage including
checking for null values; once you have identified any null values in your dataset you can replace them using the mean, median or mode of that column. In some instances where there are too many null values in one column you can drop the entire column.
checking for outliers; outliers are data points that significantly deviate from the norm of your dataset. They can impact your data visualization, distort your summary statistic and negatively affect your models.
identifying duplicate data; duplicate data is another factor that affects the integrity of your data and accuracy of your analysis. The most common practice when dealing with duplicate data is to drop the duplicate.
Then the final stage of data cleaning is to ensure that there is data uniformity in your columns. Ensure that none of your columns has two or more distinct data types within it simultaneously.

2.Visualize your Dataset
Once you have cleaned up your original dataset, you can now visualize what remains. Depending on the numbers and type of variables you can choice any means of visualization. For instance you can elect correlation matrices or scatter plots to visualize data with 2 or more variables, you can choose bar graphs or pie charts to visualize categorical data and box plots for visualizing data with one variable.

3.Perform analyses on your variables
This step will help us gain insight into the distribution of and correlation between our variables. Once again the technique of analysis varies depending on the number of variables and datatypes. Once we analyze our variables, we can then identify the relationships between them.

4.Identifying data Patterns
This step is crucial because it allows us to observe the behavior of our variables and in the long term make predictions on them, both independent and dependent. This is a major step because it is a core reason for why EDA is performed in the first place.

The final step of EDA is documentation and reporting as you will need to present your findings in an 'easy to understand' manner. After all, the whole point of data analysis is to make sense of facts and figures.
Some of the tools that are necessary for EDA are Python, R and in some cases even SQL.

Building a successful Career in Data science

Christopher Njoroge — Sun, 04 Aug 2024 18:27:01 +0000

Data science is currently among the fields that are growing at a very high rate in the modern day world. The field is basically a combination of statistics, Computer science and also some knowledge in the domain you are specializing in. In this article we delve in some ways to succeed as a data scientist.

Education

To succeed in this area one has to have enhance your education in some areas such as computer science, Mathematics and more so statistics. These two are the fundamental incase one is building a career in Data science.

Skills

Some of the skills needed in this field include.

Programming- Some knowledge in programming is needed for data manipulation, doing analysis, and also Modelling. Some of the languages mostly used in data science include R and Python
Statistics- Knowledge in mathematics and statistics come in handy for any data scientists. Understanding of certain mathematical concepts helps one perfect in this field.Some of the concepts include probability, time series, linear algebra among others.
Machine learning- This assists in building of models that could be used in predicting. Some of the frameworks used for this include scikit-learn, tensor flow and pytorch.
Data Visualization- Visualizing data assists one to communicate easily what the data shows. This could include graphs that show trends, distributions among others. Some tools that could assist in visualizations are Power BI, SQL and python libraries such as pandas, seaborn and matplotlib.
Big Data - One should also be aware of how to handle large or big data sets. Several tools such as hadoop and spark could assist in this.

Job Searching

As any other job, job searching could be an uphill task to any candidate. Quick tips that would assist a data scientist in getting a job or accelerating their career include;

Networking - This invloves linking up with other like minded persons. This could be either in social media, joining data science workshops and communities and also participating in online forums.
Learning and knowledge acquiring- Data science is an evolving field and hence requires one to continously expand their knowledge. This could include online classes, learning from other people and also participating in projects.
Building a good portfolio - Taking up projects and working on them has proven to be the best way of learning things. It also assists in demonstrating your ability to solve real world problems. Also Showcasing these projects to people could help in job searching.
Improving you curriculum vitae/resume - Go through an application, understanding what are the requirements and customize your application well to ensure you meet the set requirements.

By combining all the above factors, one is in a position to create a successful career in data science.