Forem: Kevin Ng'ang'a

My First Time With SQL

Kevin Ng'ang'a — Sun, 12 Apr 2026 12:58:00 +0000

Introduction

This week was my first time learning and working with SQL. It was first a little difficult and confusing, especially when attempting to figure out how different commands interact. However, as I kept practicing and creating basic queries, I began to realize that SQL is, in fact, a simple and understandable language. Its power is what makes it even better; with just a few commands, you can build tables, add data, change it, and even filter precisely what you want to view. My confidence grew as I continued to practice and I also came to understand that mastering SQL takes patience and consistent practice. It gets much simpler to understand and apply efficiently with time and consistency. In this article, we are going to cover some of the SQL concepts that I found interesting such as Data Definition Language and Data Manipulation Language.

Data Definition Language (DDL)

DDL, which stands for Data Definition Language, is used to create and manage the structure of a database. It focuses on the structure of the database, including establishing data types, defining columns, and constructing tables. In my instance, I used DBeaver to develop and execute my SQL queries while working with PostgreSQL as the database system.
One of the main commands in DDL is CREATE, which is used to create new tables. For example, when starting my assignment, I used the CREATE TABLE command to define a students table with columns such as student_id, first_name, and last_name as seen in the image below.
DDL is highly important because before you can store or manipulate any data, you first need to define the structure where that data will live.

Image 1 - CREATE command

Data Manipulation Language (DML)

Another essential concept in SQL is Data Manipulation Language or DML. DML is all about adding, modifying, removing, and retrieving the data inside tables within databases.

As seen in the screenshots below, INSERT, UPDATE, and DELETE are a few of the primary DML commands I used in my first SQL assignment. I was able to add new records, including names and the data that went with them, to my students table using the INSERT command and to change existing data using the UPDATE command, such as giving students. Additionally, I deleted entries that were no longer required using the DELETE command.

Image 2 - INSERT Command

Image 3 - UPDATE and DELETE Commands

The WHERE Clause

The WHERE clause is one of the most useful SQL concepts I've learnt. It is used to filter data so that only entries that satisfy certain criteria are returned. WHERE helps you concentrate on precisely what you are searching for rather than examining all the data in a table. Because of this, it is a crucial tool that is utilized in practically all SQL projects.

In order to identify exact matches, such students who reside in Nairobi, the WHERE clause sets conditions using several operators, such as the = operator. You may locate values greater than a specific quantity, like as students older than 18, by using the > operator. When dealing with ranges, such ages between 18 and 25, the BETWEEN operator is helpful.

Image 4 - WHERE Command

CASE WHEN

The most challenging component for me this week was the CASE WHEN, but after practicing and watching video tutorials, I eventually gained basic knowledge of how it operates. I understood that it is used to apply conditions in SQL and return various outcomes based on those criteria. At first, it was not very clear how to structure the CASE WHEN properly, and I kept getting confused about where each condition goes, but after examining other examples and attempting it myself, I gradually began to get it. I came to see how helpful it is for grouping data.

Image 5 - CASE WHEN Command

Key Takeaway This Week

As mentioned earlier, this week was my first time interacting with SQL, and this is because I hardly knew how important it is. After researching further on it, one key point I came across was a quote by Alice Zhao in her book SQL Pocket Guide where she writes "If there was a programming language award for best supporting actor, SQL would take home the prize", and this made me see the importance of SQL as a language not only for data specialists, but the tech world in general.

How Publish a Power BI report and Embed it into a Website

Kevin Ng'ang'a — Mon, 06 Apr 2026 11:25:10 +0000

Introduction to Power BI

Microsoft created Power BI, a business intelligence application that enables users to transform raw information into information that is useful. It enables users to build interactive graphics like dashboards, graphs, and charts, as well as connect to various data sources to clean and modify data and understanding patterns, trends, and correlations in data is made simpler by these visual representations. Reports indicate that businesses, students, and analysts use Power BI extensively because it makes complicated data easier to understand and visualize. In this sense, users can rapidly observe what is occurring and utilize the knowledge to make better decisions rather than staring at rows of data. The ability to share reports with others is one of Power BI's most crucial features. Users frequently have to show their results to teams, supervisors, or the general public after generating a report with Power BI Desktop. As such, publishing and embedding become crucial at this point. In this regard, when publishing your report, you may upload it to the Power BI Service and view it online and by enabling you to show the report on a website or application, embedding goes one step further. When put together, such features turn Power BI into an effective platform for data sharing and collaboration in addition to an analytical tool.

Publishing in Power BI

Creating a report in Power BI Desktop and uploading it to the web platform, Power BI Service, is the process of publishing in Power BI. A report that is still in Desktop mode is exclusive to your computer and cannot be accessed by anyone else. This is resolved by publishing the report to the cloud, where a browser may access it. This facilitates sharing your report with others and gives you access to it from any location. It is a very important step because it transforms your report from a private file into something that can be used and interacted with online.

Understanding the unique distinctions between Power BI Desktop and Power BI Service facilitates appreciation of publishing. The primary purpose of Power BI Desktop is to produce reports, which involve data cleaning, relationship creation, and visual design. It is where you work. In contrast, reports are controlled, shared, and saved in Power BI Service once they are published. Without opening Desktop, users may interact with reports, apply filters, and see dashboards within the Service. While others can concentrate just on reading and utilizing the final report, this division aids in maintaining the organization of the development process.

Publishing is vital because it makes your thoughts accessible to a larger audience and facilitates actual decision-making. A company or team cannot benefit from a report that remains on your computer. After publication, the report is made available to others, who do not require technical expertise to examine the data and obtain insights. Additionally, it permits the integration of reports for public viewing on other platforms, such websites. Publication guarantees that your work is not only produced but also shared and utilized in a meaningful way, and is an essential stage in the data analysis process.

Workspace in Power BI Service

In Power BI Service, a workspace is a specific location where you keep and handle your dashboards, datasets, and reports. It functions similarly to a folder and aids in the hierarchical organization of your work. Workspaces enable you to organize similar material, such sales data or marketing dashboards, rather than having all reports in one location. This facilitates finding, updating, and managing your files. Collaboration is another feature of workspaces; based on the permissions granted, several people can access and collaborate on the same content. To put it simply, a workspace is the main area where your published Power BI material resides.

You have to select a workspace to store a report that you publish using Power BI Desktop. You would not be able to properly organize or distribute your reports without a workspace. Additionally, it gives you the ability to manage who may change or read your work, which is crucial in collaborative settings. Workspaces also facilitate the maintenance of reports over time, particularly when new data or modifications are involved. All things considered, they are crucial to making sure your Power BI projects are accessible, structured, and prepared for teamwork.

Steps of Creating a Workspace in Power BI

Open your web browser and go to the Power BI Service website
Sign in using your Power BI account
On the left-hand panel, click on Workspaces
Click on New workspace
Enter a name for your workspace (e.g., Sales Reports)
(Optional) Add a description
Click Save

Uploading and Publishing a Report

Make sure your report is correctly prepared in Power BI Desktop before publishing it. This entails verifying that all of your visualizations are functioning properly, your data is clean, and your calculations, such as those using measures and columns, are precise. Additionally, you should make sure that the charts and titles in your report are appropriately labeled and that the design is clear. Reviewing your report thoroughly before publishing it helps prevent mistakes and guarantees that consumers will have a seamless online experience. You can post your report after everything appears to be in order.

Steps to Publish a Report

Open your report in Power BI Desktop and Sign in
Click on Publish in the Home tab
Select the workspace where you want to publish the report
Click Select to confirm
Wait for the upload to complete
Click the link provided or go to Power BI Service to view your report

Open the workspace of your choice in the Power BI Service after publishing. Click to open your report after finding it in the list. Verify that all images are shown accurately and that interactions and filters function as they should. This step is crucial because it verifies that your report was posted successfully and is operating well in the internet environment.

Embedding the Report

You may more easily and interactively share your work with a larger audience by embedding a Power BI report on your website. This is frequently utilized in online reports, blogs, business websites, and portfolios where people may immediately examine and interact with data. Embedding allows you to show the report within a webpage, making it seem professional and accessible, as opposed to delivering files or links separately. The user experience and engagement are enhanced by the ability for visitors to examine charts, apply filters, and obtain insights without ever leaving the website.

Using an HTML iframe or a straight link are the two primary methods for embedding your report. A direct link is just a URL that users may access in their browser when you share it. However, the HTML iframe technique is more suited for embedding within a website. You may copy and paste the embed code that Power BI offers into the HTML of your website.

The report displays as a component of the webpage when this code is added to the webpage. Similar to Power BI Service, users may interact with the graphics by scrolling and clicking.

Key Considerations

It is crucial to realize that your report becomes publicly accessible when you use Power BI's "Publish to web" option. This implies that anybody who has access to the URL can see your report without requiring specific permissions or logging in. For reports that contain sensitive or private information, such as financial statistics, private client information, or internal corporate plans, you should never utilize public embedding. A report can be indexed by search engines when it is posted online, increasing its accessibility. As a result, before deciding to make their reports public, people must thoroughly examine what they are made of.

Moreover, when integrating Power BI reports publicly, data privacy is a major risk that should always be treated seriously. You should use Power BI Service's secure sharing options rather than public embedding if your data contains confidential or restricted information. Public embedding works best for educational initiatives, portfolios, or publicly accessible data. Additionally, since your embed codes might be deleted if necessary, it is crucial to keep an eye on and manage them on a regular basis. In conclusion, public embedding is an effective feature that has to be handled carefully to preserve confidence and safeguard data.

Conclusion

In conclusion, Power BI offers a comprehensive solution for efficiently distributing insights through publishing and embedding in addition to data analysis. Users may create reports in Power BI Desktop, publish them to the Power BI Service, and integrate them into websites to make their work professionally and interactively available to a larger audience. Each step, from setting up a workspace to producing embed codes, is crucial to making sure the report is well-structured and user-friendly. However, while employing public embedding, data privacy must also be taken into account. All things considered, becoming proficient in these procedures enables users to convert data into insightful knowledge that can be distributed and applied to improve decision-making.

Understanding Data Modeling in Power BI: Joins, Relationships, and Schemas Explained.

Kevin Ng'ang'a — Mon, 30 Mar 2026 17:39:51 +0000

Data Modelling

In Power Bi, data modelling involves the process of arranging data so that it is simple for people to understand, interconnect, as well as use for analysis. It this sense, it entails determining the relationships between various data sources or tables in Power BI, such as connecting consumers to their orders or products to the stores where they were bought. It is essential to note that data is divided into smaller, useful tables that are logically connected rather than being kept in a single huge table, for instance, a table consisting of customers only, another one of products that the customers bought, and and another one to show the stores where the customers bought the products. This increases clarity and thus, when filtering, grouping, or summarizing data, a clear data model guarantees that reports provide correct findings and makes searching for information easier for users.
Data modeling helps transform unprocessed data into valuable insights that facilitate improved decision-making by organizing data and establishing distinct relationships. We can say that data modeling is a reflection of how a business functions in real-world situations. In a retail operation, for instance, clients make purchases, goods are sold, and sales occur. In order to help users answer crucial questions like which goods sell the most or which stores make the most sales, an effective data model clearly shows these relationships. As such, reports could become unreliable, unclear, or even inaccurate without the right modeling. One key takeaway from my Data Science and Analytics class was that in modelling, all tables should have primary keys, which then can be considered as foreign keys in other tables. Primary keys are unique identifiers.

SQL Joins

As mentioned earlier in this article, we can have data stored in many tables. The data from these tables may be joined using SQL joins so that similar information can be analyzed together. Research indicates that many businesses usually store data in structured maintained distinct tables, such as sales transactions in one database and customer information in another. Using Joins, businesses and users can easily combine this data by grouping it according to a shared field, such as a customer ID. In Power Bi, there are six main types of SQL joins as follows:

Inner Join
Left Join (Left Outer)
Right Join (Right Outer)
Full Outer Join
Left Anti Join
Right Anti Join

In this view, if you only want to examine customers who have made purchases, an INNER JOIN will only return the entries that are present in both databases or tables. This makes it easier to concentrate on important information and prevents your analysis from containing unwanted information.
In our example above, in order to showcase the customers who have not made any purchases, an LEFT JOIN is used and retains all entries from the first table, even if there is no matching data in the second table. A RIGHT JOIN performs the reverse
A FULL OUTER JOIN incorporates all records from both tables and aids in identifying dataset discrepancies or missing data. Another helpful way of locating missing data, such as transactions with invalid customers, is to use the LEFT ANTI and RIGHT ANTI joins. The following is a step guide of using the joins in Power Bi:
Open Transform Data
Choose a base table (your primary business entity)
Merge with another table based on a shared key
Select the join type depending on your goal
Expand only the columns you need

Power BI Relationships and the Difference Between Joins and Relationships

Relationships are used in Power BI for connecting tables so they can "interact" without being merged into a single table. This makes it possible to analyze data from several tables while maintaining data organization. For instance, still in our example earlier, a common column like Customer ID may be used to link a sales table to a customer table. The sales data immediately changes to represent only those customers when a user selects customers based on any criteria. Depending on how the data is organized, in Power BI, relationships can be one-to-many, many-to-many, or one-to-one. Joins and relationships differ primarily in how and when they are applied. Before analysis starts, joins are used in Power Query to physically merge data into a single table. In contrast, relationships are made in the model view retaining a separation of tables while enabling dynamic interaction. To put it simply, a relationship connects data, whereas a join combines data. Relationships enable quicker performance and more engaging reporting, which is why Power BI is best suited for them.

Fact vs Dimension Tables

As learned in class today, a fact table usually contains measurable data, often quantifiable quantities that may be examined, such as sales volume, quantity sold, or overall profit. We can say that it contains numerical data and these tables document business-related events and transactions, such as purchases and payments. Because fact tables keep extensive data throughout time, they are typically rather big. Additionally, they have keys that link them to other tables, enabling you to analyze the data in various ways. On the other hand, dimension tables include descriptive details that help fact tables' data make sense or descriptive details about the business. They contain information such as dates, locations, product categories, and customer names. The data in reports is filtered, grouped, and labeled using these tables. For instance, dimension tables let you view sales by product, location, or time period in addition to overall sales. Compared to fact tables, dimension tables are often more reliable and smaller. It is vital to note that fact and dimension tables work together to provide a structure that facilitates the investigation and understanding of data.

Schemas: Star, Snowflake, and Flat Table (DLAT)

In a data modeling, schemas explain how tables are arranged and connected. We are going to focus on three main schemas as follows: the star, snowflake, and flat schemas.
The most popular and preferred schema in Power BI is the star schema. It is shaped like a star and features a central fact table that is directly linked to several dimension tables. This style is perfect for company reporting since it is straightforward and simple to read and understand. For instance, users may swiftly examine sales from several angles by connecting a sales table to customer, product, and date tables.
The snowflake schema is a more complex alternative of the star schema, because it further subdivides dimension data into smaller, linked tables. For example, a product table, could be connected to a category table, which in turn links to a department table. In Power BI, research indicates that this minimizes data duplication, but it also makes the model more difficult for people to understand.
The flat table (DLAT) schema, which can be helpful for small datasets or faster analysis, combines everything into a single large table. However, repetitive data make flat tables inefficient as data volumes increase.

Role-Playing Dimensions and Common Modeling Issues

Role-playing dimensions is when the same table is utilized for several purposes. For example, the order, shipment, and delivery dates, can be loaded into a Date table. The additional connections can be utilized with formulae as necessary, but only one connection is active at a time. Common issues in data modelling may revolve around the axis of too many connections, relationship loops, duplicate information, and unclear keys. These may cause reports produce inaccurate findings. Such issues may be avoided and analysis is made easier and more accurate by keeping the model straightforward, employing clear keys, and carefully managing connections.

How Excel is Used In Real-World Data Analysis

Kevin Ng'ang'a — Tue, 24 Mar 2026 17:38:27 +0000

What is Excel?

Excel, also known as MS Excel, is a Microsoft Office application used in handling and editing of documents (spreadsheets) which data is laid out. It is a vastly used application for students and professionals alike for collecting and processing data by performing mathematical calculations, organizing, cleaning, presenting, summarizing, visualizing, and storing it.

Real- World Uses of Excel

While Excel has a wide array of uses in real-world scenarios, we are going to focus on a few common uses as follows:

Business Calculations

As mentioned earlier, MS Excel is used to perform mathematical calculations and this comes in handy for small and large business. For instance, with a huge data set of prices of goods and quantity of goods sold, it is easy to find the revenue by using simple Excel formula such as: = Quantity * Price or (=A2*B2), A2 and B2 being cells in rows and columns that contain the data set of quantity of goods sold and price of goods sold respectively. It should be noted that MS Excel can perform even complex mathematical calculations. For instance, by using in-built functions or formula, users can easily calculate business concepts such as profit margins, discount percentages etc. Users can use in-built function to evaluate how a business is performing, all these without manual computations which would otherwise take a lot of time. In business calculations, it is crucial to mention that users can automate repetitive calculations and this massively saves time.

Data Analysis Using Charts

Using structured data, one can create interactive charts in MS Excel to show the association of data sets, for instance, how a product sells in a certain region compared to other regions using bar charts or graphs. This is significantly essential in decision making for businesses because such visualization simplifies complex data and make it easier to interpret. When looking at raw large data set, it is difficult to understand patterns, but with visuals such as bar charts and graphs, it is easier. Also, these visuals are highly useful in presentation where the audience need a quick understanding of trends and insights. Moreover, in designing the visuals, MS Excel allows users to customize them so as to portray key findings such as Key Performance Indicators, which further makes interpretation easier.

Tracking of Expenses

Tracking of expenses over an extended period of time can be hectic if it is done manually. However, with the use of MS Excel, users and/or businesses can easily manipulate the data and acquire their expenditure insights within minutes. One of the features I found interesting is the use of conditional formatting. Conditional formatting involves changing the look of a cell or data entry based on set rules, that is, highlighting a data entry using a specific color if it meets a certain condition. When tracking expenses, instead of manually scanning huge data sets, we can easily spot overspending immediately using conditional formatting.

Data Cleaning

Businesses across the world often rely on data to make insights and decisions. More often than not, the data is acquired in its raw form, meaning that it is not only incomplete, but it is also filled with inconsistences; it is disorganized and incomplete. This is where MS Excel comes in! Perhaps one of its most important uses in a real-world scenario, MS Excel is widely used to clean the data and ensure a well-organized, complete and consistent set of data. In this sense, MS Excel offers many formulas and features that are used for data cleaning. Key among them is the FIND & REPLACE, function, which as the name suggests, is used to find a data in a range, and replace it with a desired data. As far as data cleaning is concerned, using MS Excel, users can filter data based on set requirements and also remove duplicates. All these steps are very essential because a disorganized data can lead to wrong conclusions which might affect the operations of a business. In this view, we can assert that MS Excel is used to improve the reliability of data.

Business Forecasting

One of the most versatile benefits of MS Excel is forecasting future trends. In this regard, businesses can be able to predict market trends and assess risks. Additionally, businesses can use historical data in Excel to plan for future growth, strategies, and objectives. Evaluating past performance is useful for growth rate calculations. Businesses that need to plan ahead may need to utilize Excel to forecast, and budget ahead or even estimating future demand or products on services.

Data Summarization

As mentioned earlier in this article, MS Excel can be useful in summarizing large data sets to offer quick insights. Instead of users going over hundreds of rows and columns manually, they can easily group the data in MS Excel based on specific variables such as product, discount, department, etc. and breakdown large complex data into manageable sections. One can, therefore, identify key relationships from those manageable sections. Moreover, users can compare different kinds of variables from their data set, without changing the original data. For instances, a business can compare the performance of departments. This feature is useful for businesses in exploring different perspectives and evaluate if there are trends. All these processes are mainly done with the use of Pivot tables in Excel. Pivot tables further allow dynamic and interactive reporting, where the summary can automatically update upon selecting new data within the data set. This is highly crucial in reporting where the data changes a lot and insights are required in real time. Also, it is crucial when decision makers in a business need to use the latest information to ensure accuracy.

Conclusion

In conclusion, it is clear that MS Excel is a significantly important tool in real-word scenarios, especially when it comes to data analysis and management. Its versatility coupled with its availability is a significant advantage for many users, especially business owners. As seen from the article, MS Excel allows users to efficiently handle large data sets through process such as performing calculations and creating meaningful visuals. Through these processes, users transform raw, unorganized, and inconsistent data to meaningful analysis which support proper decision making. Perhaps one underrated feature of MS Excel is simplifying highly complex data, making the software effective for beginners. Also, we have seen that MS Excel performs beyond data handling but also it is effective in data cleaning, summarization, and forecasting. These processes ensure that the data provided is accurate, and can be used in strategic planning for businesses. Business can easily rely on MS Excel to pinpoint important trends, make predictions, plan for the future, and compared performances. All these can ultimately reduce uncertainties and risks in a highly competitive environment. All in all, MS Excel is a game changer in data analysis, due to its wide range of features and accessibility. Whether it is used for personal projects such as personal budgeting or business operations and academic work, MS Excel excels in the overall management and handling of data. It is, therefore, important for people to master MS Excel, especially for building data analytical skills.

Forem: Kevin Ng'ang'a

My First Time With SQL

Introduction

Data Definition Language (DDL)

Data Manipulation Language (DML)

The WHERE Clause

CASE WHEN

Key Takeaway This Week

How Publish a Power BI report and Embed it into a Website

Introduction to Power BI

Publishing in Power BI

Workspace in Power BI Service

Steps of Creating a Workspace in Power BI

Uploading and Publishing a Report

Steps to Publish a Report

Embedding the Report

Key Considerations

Conclusion

Understanding Data Modeling in Power BI: Joins, Relationships, and Schemas Explained.

Data Modelling

SQL Joins

Power BI Relationships and the Difference Between Joins and Relationships

Fact vs Dimension Tables

Schemas: Star, Snowflake, and Flat Table (DLAT)

Role-Playing Dimensions and Common Modeling Issues

How Excel is Used In Real-World Data Analysis

What is Excel?

Real- World Uses of Excel

Business Calculations

Data Analysis Using Charts

Tracking of Expenses

Data Cleaning

Business Forecasting

Data Summarization

Conclusion