DEV Community: Faith

Data Modelling, Relationships & Joins

Faith — Sat, 12 Sep 2026 12:52:11 +0000

Data Modelling in Power BI

Data modeling is the process of analyzing and defining all the different data types a business collects and produces, as well as the relationships between those bits of data. By using text, symbols, and diagrams, data modeling concepts create visual representations of data as it’s captured, stored, and used at the business.
As business determines how data is used and when, the data modeling process becomes an exercise in understanding and clarifying data requirements.

By modeling data, you can document what types of data you have, how you use it, and the data management requirements surrounding its usage, protection, and governance. The benefits of data modeling include:

Creating a structure for collaboration between different teams.
Revealing opportunities for improving business processes by defining data needs and uses.
Saving time and money on IT and process investments through appropriate planning.
Reducing errors (and error-prone redundant data entry) while improving data integrity.
Increasing the speed and performance of data retrieval and analytics by planning for capacity and growth.
Setting and tracking target key performance indicators tailored to your business objectives.

Types of Schemas

A snowflake schema is another way of organizing data. In a schema, dimension tables are split into smaller sub-dimensions to keep data more organized and detailed, just like snowflakes in a large lake.

Star Schema

Structure: A star schema resembles a star with a central fact table containing transactional data (e.g., sales data) surrounded by dimension tables (e.g., product details, customer information, time hierarchy ). These dimension tables are connected to the fact table through foreign keys.
Focus: Optimized for analytical workloads. The pre-aggregated data and clear relationships between tables enable efficient querying and analysis of trends and patterns.
Pros: Fast and efficient for complex analytical queries, reduces redundancy compared to fully flat tables, simplifies data modeling for data warehouses.
Cons: Might not be ideal for storing highly granular data or handling frequent schema changes, can be less efficient for operational tasks like real-time data updates.

Flat tables

Structure: Flat tables store all relevant data points for an entity (e.g., a product) within a single table, often with numerous columns to represent various attributes. This approach prioritizes ease of data access and flexibility.

Focus: Optimized for operational tasks and handling large volumes of detailed data. The denormalized structure allows for faster reads and writes of individual entities.
Pros: Simpler data access for specific entities, efficient for writes and reads of detailed data, can be flexible to accommodate schema changes.
Cons: Can lead to significant data redundancy, potentially slower for complex analytical queries that require joins across multiple tables, requires careful management to avoid data inconsistency.

Snowflake Schema

Structure Snowflake Schema is also a type of multidimensional model used for data warehouses. In the snowflake schema, the fact tables, dimension tables and sub-dimension tables are included. This schema forms a snowflake structure with fact tables, dimension tables and sub-dimension tables
Focus Best for Flexibility and Data Integrity: If we need to handle large datasets with multiple levels of hierarchy and a high degree of normalization, the Snowflake Schema offers greater flexibility. It’s perfect for maintaining data integrity across complex datasets.
Pros : Ideal for large organizations dealing with large, normalized datasets or those with frequent updates, like customer or inventory management systems. It minimizes redundancy and improves storage efficiency.
Storage Considerations: Snowflake is more storage-efficient due to its normalized structure, making it a great choice for scenarios with complex, high-volume data.

Fact Tables and Dimension Tables

In data warehousing, fact tables and dimension tables are key components of star or snowflake schemas. Fact tables store numeric data like sales or order amounts and include foreign keys linking to dimension tables. Dimension tables provide context with descriptive details like product names or customer demographics. The main difference between Fact or Reality table and the Dimension table is that dimension table contains attributes on that measures are taken actually table. Below are the main differences between the two:

Relationships in Power BI

A model relationship propagates filters applied on the column of one model table to a different model table. Filters will propagate so long as there's a relationship path to follow, which can involve propagation to multiple tables.

Relationship paths are deterministic, meaning that filters are always propagated in the same way and without random variation. Relationships can, however, be disabled, or have filter context modified by model calculations that use particular Data Analysis Expressions (DAX) functions.

Cardinality

Each model relationship is defined by a cardinality type. There are three cardinality type options, representing the data characteristics of the "from" and "to" related columns. The "one" side means the column contains unique values; the "many" side means the column can contain duplicate values.

The three) options, together with their shorthand notations, are described in the following list:

One-to-many (1:*)
One-to-one (1:1)
Many-to-many (:)

When you create a relationship in Power BI Desktop, the designer automatically detects and sets the cardinality type. Power BI Desktop queries the model to know which columns contain unique values. For import models, it uses internal storage statistics; for DirectQuery models it sends profiling queries to the data source. Sometimes, however, Power BI Desktop can get it wrong. It can get it wrong when tables are yet to be loaded with data, or because columns that you expect to contain duplicate values currently contain unique values. In either case, you can update the cardinality type as long as any "one" side columns contain unique values (or the table is yet to be loaded with rows of data).

One-to-many (and many-to-one) cardinality

The one-to-many and many-to-one cardinality options are essentially the same, and they're also the most common cardinality types.

When you configure a one-to-many or many-to-one relationship, choose the one that matches the order in which you related the columns. Consider how you would configure the relationship from the Product table to the Sales table by using the ProductID column found in each table. The cardinality type would be one-to-many, as the ProductID column in the Product table contains unique values. If you related the tables in the reverse direction, Sales to Product, then the cardinality would be many-to-one.

One-to-one cardinality

A one-to-one relationship means both columns contain unique values. This cardinality type isn't common, and it likely represents a suboptimal model design because of the storage of redundant data.

Many-to-many cardinality

A many-to-many relationship means both columns can contain duplicate values. This cardinality type is infrequently used. It's typically useful when designing complex model requirements. You can use it to relate many-to-many facts or to relate higher grain facts. For example, when sales target facts are stored at product category level and the product dimension table is stored at product level.

Filter Direction

Relationship Filters

In Power BI, when two tables are connected, filters can flow between them to control which data is visible in visuals. This flow is determined by the filter direction in the relationship settings.

There are two types:

Single-direction filter:

Allows filter context to flow from one table to another (most common).
Bi-directional filter: Allows filter context to flow in both directions between connected tables.
Single-Direction Filters
Best For:
Star schema models (fact and dimension tables)
Maintaining predictable filter behavior
Optimizing model performance
In a star schema, filter direction should typically flow from the dimension tables (e.g., Customers, Products) to the fact tables (e.g., Sales, Transactions). This setup reflects how users usually analyze data: e.g., sales by customer, not customers by sale.

Example:
You have a Sales table and a Products table. You want to filter sales by product category. The relationship should allow filters to flow from Products to Sales (one-to-many).

Bi-Directional Filters

Use with Caution:
Complex reporting scenarios
Many-to-many relationships
Role-based security (RLS) configurations
Bi-directional filtering lets two tables filter each other. While powerful, it can cause:

Ambiguous relationships
Unexpected results in visuals
Performance degradation, especially on large datasets
Example:
Imagine you have a Sales table and a Regions table, but not every sale is assigned to a region. A bi-directional filter might seem helpful, but it can lead to wrong totals or slow down the report.

Best Practices
Default to Single Direction
It’s simpler, faster, and more stable in most cases.

Use Bi-Directional Filters Sparingly
Only when absolutely necessary, and understand the impact on model performance.

Consider Using DAX Instead
Functions like USERELATIONSHIP() or CROSSFILTER() can give you more control without needing a full bi-directional relationship.

Watch Out for Ambiguity
If Power BI warns you about ambiguous paths — check your filter directions. Simplify relationships where possible.

Common Mistakes to Avoid

Using bi-directional filters to fix visuals instead of fixing the data model
Forgetting to evaluate performance after enabling both-direction filters
Not testing RLS impact when filter directions change
Creating circular filter paths, which Power BI won’t allow

Joins in Power Query

Allows you to combine data from different sources, offering comprehensive insights. Here’s an overview of the most common merge options in Power Query, explained with simple analogies and real-life examples.

Join Types

Now we will go into the different types of join functions Power Query has made available to us

Left Outer Join (All from first, matching from second)
• Analogy: Imagine you’re planning a wedding. You have a guest list (Table 1), and then you collect RSVPs (Table 2). A left outer join ensures that everyone from your guest list (Table 1) will be included, even if they didn’t RSVP. For those who did RSVP, you’ll also have their response.
• Practical Example: In a company, if you have a list of all employees (Table 1) and another list of their completed training sessions (Table 2), a left outer join shows all employees, whether or not they have completed any training (Table 2).
Right Outer Join (All from second, matching from first)
• Analogy: Now, you’re only interested in the RSVPs (Table 2). You still want to know if they were on the original guest list (Table 1), but you also want to include people who RSVP’d without being invited. A right outer join shows you all RSVPs, whether or not they were on the guest list.
• Practical Example: If you have a list of all customers who placed orders (Table 2) and another list of all potential customers (Table 1), a right outer join will show all customers who placed orders, even if they weren’t on the original prospect list.
Full Outer Join (All rows from both)
• Analogy: Now you want to see both your guest list and RSVPs entirely. You want to know who was invited and who RSVP’d, and also see if anyone RSVP’d who wasn’t on your list. A full outer join shows all the data from both tables, matching where possible and leaving blanks where there are no matches.
• Practical Example: If you have a list of products sold (Table 1) and a list of products in inventory (Table 2), a full outer join will give you all products, both sold and in inventory, even if some products were sold but are not in inventory and vice versa.
Inner Join (Only matching rows)
• Analogy: You’re only interested in the people who were on your guest list and RSVP’d. If someone was invited but didn’t RSVP, or RSVP’d without being on the guest list, you don’t want to see them. An inner join only shows rows that exist in both tables.
• Practical Example: If you have a list of current employees (Table 1) and a list of people who completed mandatory training (Table 2), an inner join will show only those who are both current employees and have completed the training.
Left Anti Join (Rows only in first)
• Analogy: You want to see who was on your guest list but didn’t RSVP. A left anti join shows only the people who are in Table 1 (your guest list) but have no matching record in Table 2 (RSVPs).
• Practical Example: If you have a list of all employees (Table 1) and another list of those who completed training (Table 2), a left anti join will show you the employees who haven’t completed the training.
Right Anti Join (Rows only in second)
• Analogy: Now you want to see who RSVP’d but wasn’t on the original guest list. A right anti join shows only the people who are in Table 2 (RSVPs) but have no match in Table 1 (the guest list).
• Practical Example: If you have a list of employees who signed up for a training session (Table 2) but weren’t originally required to take the training (Table 1), a right anti join will show you the unexpected attendees.

Key Insights:
• Use left outer join to retain all the data from the first table and match as much as you can from the second.
• Use right outer join to retain all the data from the second table and match as much as you can from the first.
• Use full outer join to combine everything from both tables, regardless of matching.
• Use inner join when you only care about data that exists in both tables.
• Use left anti join and right anti join to find rows that exist only in one of the tables but not both.

Power Query Joins vs Power BI Relationships

Performance
One of the distinguishing features of a reporting system is the performance of it. If you are building a reporting system, and the loading the report takes five minutes, then what is the benefit of this report over doing it in the operational system? because it would have taken that much in the operational system anyway!

When you build a reporting system, you should target creating reports that load fast. Having extra relationships, doesn’t help with the performance. The DAX queries behind the scene would be longer.

Another big deterministic of a good reporting model is the ability to make it easy-to-understand for data visualizers. If a data visualizer have 500 tables in a model all inter-related, anyone would have a hard time to understand the model.

Relationships come with a cost of complexity. You might need a both-directional relationship sometimes, or you might get into the problem of having an inactive relationship and trying to resolve that. Less relationship means fewer problems of this type when it is unnecessary.

Recommended Power BI Model

You should not have tons of relationships, and you should not create one big table with everything in it. The recommended thing is to design a totally different schema. A new layout of tables and relationships. Something that is different from the operating system’s database. Something that helps to get the best of both worlds.

This model, has many names; dimensional model, star schema, data warehouse etc. Through the process of creating this model, you combine some of the tables (dimensions), and keep some of them to be connected through the relationships (dimension to fact table relationships).

What if you don’t have a reference table that you can use as a dimension? what if there are only mapping tables in your data source? Many, have faced the same challenges. If you don’t have a reference table, you can create them from your mapping tables.
Just because you can load all the tables into the model and then create relationship, it doesn’t mean it is the best thing to do. Just because you can merge all the tables using Power Query, it doesn’t mean you should do it.

Power BI modeling starts with a pen and paper. This is very important that you start your modeling by a pen and paper. Yes, no tools or services needed. Just a pen and paper. You need to draw the dimensional model, the layout of the tables. You need to list fields in your dimension and fact tables. All of these are much better to be done using a pen and paper. You can then take the next step of implementing it using Power BI.

A good model should consider many things. It should first start with the requirement. How are you going to build a database diagram of a reporting system, when you don’t know what values should be shown in the report?

A good model also requires an understanding of the existing data, and how that data can be transformed into the star-schema model.

Don’t build your model on this rule that; this is the format that I get data, so can’t really do much about it. This is wrong! You need to transform that format.

My report works just fine, why bother?
Why bothering if your report is running just fine? or if it doesn’t have any performance problems? Do you really need to think about the model if you got only a few tables and everything works as it should? Why spending time on something that the users won’t see?

Yes, you need to work on your model. If you didn’t do that from the beginning of your implementation, do it now. Your model gets bigger and bigger every time. You will have more tables, more relationships, bigger challenges.

Building a proper data model is like building a base for a 100 stories building. would you first build the 100 stories and then come and fortify the base?! of course not. You should do it as soon as possible, or your whole building might collapse at some point.

Your Power BI data model is your world. It is up to you to decide in which world you want to live in. Would you prefer to have a model that is too complicated that every time someone asks for a change it ends up with hours and often days of work because it is not designed in the way it should? Or do you prefer to build a model that is a high performance, simple, easy to change, and simple to integrate? That is your choice; your model, your world.

Data Analysis and Reporting Using Excel

Faith — Fri, 04 Sep 2026 21:14:31 +0000

Introduction

As part of my Data Science course at LuxDevHQ we covered Excel as our first tool. It seemed unnecessary to me at first but the more we covered the more I realized I didn't know as much as is required of me as a data professional. Moreover, I was impressed by the capabilities of Excel, beyond what I normally used it for as a Finance professional who never got structured Excel training.
I used Jumia Discount Dataset for my analysis. Practised everything we covered for the last two weeks from data cleaning, analysis, pivot tables, charts to dashboarding.

Importing

Below is the process I used to import the data after downloading it on my machine.

Open a blank workbook.
Select Data > Get Data > From Text/CSV.
Choose Excel_jumia_dataset.csv and verify that the comma delimiter is detected.
Load the source into Raw_Data.
Duplicate the query or sheet before cleaning and load the result into Cleaned_Data.

Data Cleaning

I first of all had to scan through the entire data to see what it contains and maybe spot mistakes that could be easily corrected. Started off with obvious errors that I tabulated for tracking as shown below:

To avoid being overwhelmed with scattered data, I created these worksheets in my workbook for different purposes during the project;

Raw_Data — an original copy of the CSV;
Cleaned_Data — cleaned fields, derived columns, and an Excel Table;
Analysis — descriptive statistics, correlation, and ranked product tables;
Pivot_Tables — all PivotTables that support the visuals;
Dashboard — KPIs, charts, slicers
Data_Dictionary — field definitions, a cleaning log.

Among others that I realized I needed along the way for further analysis and insights that bore the names of the specific metrics I analyzed.

Analysis

Analyzing data seems a bit easy when you are being taught or when someone else is doing it, its only when you do it that you appreciate the logic that goes into this process. Found out that I need to add some columns to enable analysis on my part, which was a bummer who knew that trick, I didn't, anyway we live we learn.

I realized I wasn't going anywhere without additional columns in my data since I had only a few columns to work with and by themselves they weren't going to be of much help. I created the Price Category, Discount Status, Review Status, and Ratings Status columns, that came in handy especially when I created pivot tables to summarize data for insights and recommendations. Maintained my options between 2 to 4 categories in each newly created column.
Applied the Nested IFs and ISBLANK formula to create my categories as indicated here;

Price Category =IF([@[Current price]]<500,"Economical",IF([@[Current price]]<=1000,"Affordable","High Cost"))

Discount Status =IF([@Discount]<20%,"Low Discount",IF([@Discount]<=40%,"Medium Discount","High Discount"))

Review Status =IF(ISBLANK([@Review]),"No Review", IF([@Review]<10,"Poor Review",IF([@Review]<=20,"Good Review","Excellent Review")))

Ratings Status = =IF(ISBLANK([@Ratings]),"Not Rated",IF([@Ratings]<3,"Poor Review",IF([@Ratings]<=4.5,"Average","Excellent")))

The thing with the analysis part there is no right way or wrong way to do it there is only how you choose to do it, I found this method to be the most straight forward and fast since I am also training to ensure I use the least amount time possible to create a report start to finish.

Pivot Tables and Charts

After creating necessary columns and coming to the conclusion that my analyzed data is sufficient for drawing inference I proceeded to create Pivot tables based on some objectives:

which products may need a different pricing or marketing strategy

On the pivot table for this category, I placed the product and price on the rows field but made sure to group by 500, then added Average of Review to the values field and finally, Rating status to the columns as well as a slicer for the rating status.

As for the other Pivot tables, I was a bit experimental, tried different variables in different field until I found was fitting to what I intended to represent.

Used two bar charts, two column chart and a pie chart for the visualization. Created a bar and column chart interchangeably on data that represented a numerical variable along side a categorical variable. For instance, when I needed to visualize the relationship between product pricing and review:

Dashboard

Finally the dashboard!
Made the KPI's and the charts form the analysis I made on the data. My dashboard includes what I thought is of importance from the data I analyzed, sure you don't have to include every chart made, It should only have that which is essential to the stakeholders and maybe what they wish to know or what you wish to communicate as an analyst.

Recommendation and Insights

Do Larger Discounts Drive More Reviews?

From the data it seems as though higher discount drive more review as shown in the figure. High discounts which were discounts ranging from 41%-100%, but the highest recorded discount was 64%, so essentially the discount range from 41%-64% generated 30 reviews, which is the highest of the three categories.
This suggests that aggressive discounting can build customer trust or volume.

Do Highly Rated Products Attract More Engagement?

Yes, but the gap is modest.

Excellent-rated products lead in reviews, but interestingly, poorly-rated products carry the highest average discount (43%), suggesting discounts may be used to compensate for weak ratings, rather than reflecting genuine value.

Do Price and Rating Move Together?

There's a gentle positive relationship between price and rating. However, not sufficient for us to make an inference form this data only.

Best Performing Products (Ratings + Reviews)

The standout performers combining both high ratings and strong review counts:

Products Needing a Strategy Rethink

20 products have zero ratings and zero reviews. These span all price categories and carry discounts averaging ~38%, which clearly isn't working.

another category would be the products with the lowest reviews and ratings such as:

Recommended actions:

Run targeted campaigns to seed initial reviews
Consider bundling with top-performing products
Reassess whether the product-market fit is right before deepening discounts further.

Conclusion

This will serve as my lesson learnt from the whole process as I have gained one or two tips for later.
It seems like a ton of work but I believe it gets easier with time, so I will hold on to that. As long as something works use that, don't go over your head trying to remember what you learnt to apply it, and always remember you can consult, no man is an Island we all need each other at some point.

Getting Started with Excel for Data Analytics: From Basics to Data Cleaning.

Faith — Sun, 30 Aug 2026 14:23:33 +0000

I just came to the realization that Microsoft Excel is one of the most commonly tools used at the workplace. Not for anything else but for data analysis. I have never thought of it this way. Given my background in Finance, we use Excel for bragging rights depending on how many formulas you created and run on excel to make your your financial models run.

Data Cleaning

Before any analysis, most data requires to be cleaned or adjusted in a way that it is easily used for any analytical work. Data collection sometimes comes with variant responses that make it hard to use the data as it is. Making it necessary to clean data before usage. A really cool tool I learnt this week was Data Validation. This helps restrict the type of feedback you get per column making it easier when you use the data later. An example would be to restrict product categories that one can enter or maximum and minimum limits. Enforcing it is a easy as:

Selecting the necessary cells
On the ribbon, click Data then Data Validation
Under Allow, select List
In Source, type values separated by commas
Click OK

Applied Data Validation on the Employee column Validation to ensure that only whole numbers are allowed after removing duplicates from the dataset.

Filtering

Another important tool in Excel has to be filtering, thinking of it now I should have started with this one.
This gives an over view of how the data looks like, if you actually need to apply changes to the data or leave it as it and so on. I believe it is impossible to use excel especially when interacting with tons of data and not use filtering.
It's like a sneak peek of the data displaying only the rows that meet criteria while the rest stay hidden.

How do we exactly do that:

Click anywhere in the dataset
On the Home tab, go to Sort and Filter, Filter
Click on a dropdown on the header row
Choose the values to show or use

Before I realized that some columns have entries that are wrongly spelt and written, I filtered my data to see that and replaced the entries with what aligned with the options available.

Data Formatting and Cleaning

Number formatting

This function of Excel would come in handy when you probably are trying to perform a mathematical operation but it just wouldn't work. Chances are the formatting isn't appropriate of the data type. A hint that would help to determine if the formatting is appropriate is; numerical data aligns to the right while text to the left. However, this sometimes isn't enough.

Let's now get to how it is done:

Percentage formatting

Select cells
Home > Number group> Click % symbol
Numbers convert to %

Currency formatting steps

Select the cells with numbers
Home tab > Number group
Click the dropdown arrow on the number format box
Select Currency or Accounting
Change the currency by clicking the small dialog launcher

A simpler way to do it on the Home tab to go to the number segment and click on the drop down on the bottom right and any changes can be made from there

Conditional Formatting

This is a useful tool especially if you need to pay attention to a criterion or a group of data. It highlights cells automatically based on the rules set, helping with locating with trends and that which you wish.
An example is when handling credit data and if the loan is way past due time, conditional formatting can be used to categories the different classes of default.

Go to Home > Conditional Formatting
Choose a rule type:

Highlight Cell Rules (Greater than, Less than)
Top/Bottom Rules
Data Bars, Color Scales, Icon Sets

For example, select Highlight Cell Rules > Less Than
Enter a value
Choose a formatting style (e.g., green fill)
Click OK

There is so much that one can do when it comes to data analysis. However, I have focused on the ones that I use a lot more, but on the contrary they don't seem to be that obvious such as removing duplicates and data sorting.
Hope this makes data analysis on Excel a bit lighter and faster for you as it did for me.
On a lighter note, I would always recommend videos incase anything isn't clear, practice does it better than any other way.

My First GitHub Project: From a Local Folder to GitHub Using Git and SSH

Faith — Sat, 22 Aug 2026 17:29:53 +0000

It took me to set up my Git and GitHub to realize how technology is sometimes not that straight forward at least for beginners. I mean setting up an account should be the easiest part of technology right? However Git and GitHub are packaged differently, forcing us to bear the brunt of its's convolution.

I'll break down the process to make it ingestible without intimidating a soul, because this too shall pass. Later, we will look back and be astonished of how something simple got our necks on a chokehold.

1. Installation

First things first, you have to set up a GitHub account on GitHub and then download git and Git Bash. I'll share a more detailed guide of this process don't worry. Git Bash is sort of the terminal that we'll use to write our commands while connecting our git to Git Bash. Git runs locally and keeps track of changes during the project while GitHub is an online version that does the same. Depending on your Operating System there are different ways to download Git. Alternatively, VS Code can be used to push your projects to GitHub and that's what we'll focus on, since its the simplest one. At this point I'm assuming we have a project that needs pushing to GitHub.

2. Creating a folder locally

There are shortcuts to create and edit files using Git Bash when doing projects and I thought it best to include them here for posterity and experiment as well.

mkdir makes a directory/creates a folder eg mkdir Analytics Project

Make sure not to include space on the name of the folder, use hyphens or underscore instead. Alternatively, enclose the name of the folder in quotation marks if spaces are used in naming the folder, then and add the your data set into the folder created.

These commands are useful in Git Bash depending on which stage of the process you are in.

cd changes to another directory (cd "Analytics Project') cd/path/to/your/repository
cd .. takes you back to previous directory
ls lists down what's in the folder
rm deletes document in the folder
rm -r deletes the folder in the folder

Creating a README file

touch creates a README document (touch README.md)

A readme file is important for documenting the process of a project. It usually acts as a contact point with anyone interacting with the your digital product.

On Git bash or terminal, access the folder cd Analytics Project

To edit content in README.md file, we'll use the command echo "Analytics Project" >README.md
To check what is written, we'll use the command cat README.md
Nano can be used to simplify editing. This has to be my preferred method of editing as it is the easiest to use, especially when you are a beginner.
Running the command nano README.md will make a text editor appear where in we shall edit our content.
To Save content, press Control + O,
Press Enter Key to confirm the file, then press Control + X to exit._

3. Git Configuration

This is probably the simplest part of this process after creating a GitHub account. On Git Bash, check user name user email by running

git config --global --list

If the account that exists isn't yours or you'd like to use a different one we can overwrite the existing account by using

git config --global user.name "YourGitHub Profile Name"

Then you'll be prompted to add a passphrase which is basically a password, it's advisable not to write one incase you forget it. So just *ENTER * to move to the next step.

Incase you'd like to change your branch from master to main run;

git config --global init.defaultBranch main

4. Key Generation using SSH

In this part we'll be generating a SSH key that we'll use to connect our Git and GitHub. SSH helps us with security because our computer needs a secure way to prove to GitHub that it is authorized to access our GitHub account
SSH stands for: Secure Shell
With SSH authentication, you create a pair of cryptographic keys.
GitHub requires the public key to be added to your account, while the corresponding private key stays on our computer. Now, this is where things a bit technical.

When we generate an SSH key, you will normally get 2 keys:

id_ed25519 - private key
id_ed25519.pub - Public key

But, I'd like us to generate a public key using a shortcut:

ssh-keygen -t ed25519 -C "your_email@example.com"

Then a prompt to overwrite will appear, write "y" meaning yes and press Enter. If you chose to have a password input, if you didn't press Enter then run

clip < /c/Users/user/.ssh/id_ed25519.pub to save they public key in a file.

Testing

We've already generated a key, now we use an IDE in this case VS Code to push our project to GitHub.

To confirm if we've connected our Git to GitHub using the SSH Key that we have generated. Go to Git Bash terminal and run
ssh -T git@github.com

There shouldn't be any problem with this but if anything isn't seeming right, you can always Chat GPT.

Okay now let's copy our generated key to GitHub

Open your GitHub account
Go to settings
Click SSH and GPG
Delete and add new or simply add the generated key if there are no keys yet

Using VS Code

You can create a folder or choose a folder through VS Code.

Go to file
Open your folder containing your project
select your folder

Connecting Git to GitHub

Now we'll configure GitHub by;

Clicking Source control on the left side
Choose Manage work trust
Initialize repository - this is the equivalent of git innit when using Git Bash. It creates a hidden .Git directory which contains information that our Git uses to track the developments and changes.
Initializing the repository allows our project folder to become a Git Repository.
Add change - this is a write up of the content you would like to indicate in present tense eg "Add Dataset". This is analogous to writing git commit -m " ", while using terminal or Git Bash.
Against commit on the far right click the drop down and choose commit and sync. Comparable to this is git push origin main

After pushing our project to GitHub, you can run command Git status to confirm everything is as you wanted it to be and maybe adjust accordingly.

From here you'll see a number of prompts, only click "Always", "Add remote", "Publish Branch", "Allow- if configuring for the first time."

Lastly, choose a browser of your choice and authorize access remembering to chose between creating a public or private key depending on your choice and preference, Sign in and viola, The project is posted on GitHub.

Something to note the name of the repository will bear the name of the folder used.

Conclusion

That's it imagine, there are other ways to push your projects to GitHub if that's something you are interested in , be sure to find that out, there's also a lot more to know in GitHub and GitHub Configuration but baby steps, right? That's all for today all and the best at pushing your projects on GitHub.

Build A Calculator Using Python

Faith — Mon, 10 Aug 2026 17:41:52 +0000

Using Loops

This has to be the easiest way to apply your python skills after learning the basics. The best and only way to learn anything is through rolling your sleeves and practicing, python for this case is no exception. Just like any other rookie, I did really struggle with what to start with given some projects online are quoted "beginner" but ended up not being beginner friendly to me.
Yeah, I know this project is not all that, but it helps retain the concepts learnt and keeps the spark alive, at least it does for me.

Lines of Code

print("===== Advanced Calculator =====")
print("Basic: +, -, *, /")
print("Adv: power (^) and Modulus (%)")
print("Sci: sqrt, log")
print("Trig: sin, cos, tan")

# get the operator
op = input("\nEnter Operator: ")

# Binary operations
if op in ['+', '-', '*', '/', '^', '%']:
    num1 = float(input("Enter 1st num: "))
    num2 = float(input("Enter 2nd num: "))

    if op == '+':
        print(f"result: {num1 + num2}")
    elif op == '-':
        print(f"result: {num1 - num2}")
    elif op == '*':
        print(f"result: {num1 * num2}")
    elif op == '/':
        print(f"result: {num1 / num2}")
    elif op == '^':
        print(f"result: {math.pow(num1 , num2)}")
    elif op == '%':
        print(f"result: {num1 % num2}")

This are lines of code that can be used to explore loops in python because theory alone doesn't cut it. The first part of the code, the one consisting of print() at the beginning, are an explanation of the arithmetic operators used, you don't really need to have it.