My Data Analysis Journey as a Beginner

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Hi everyone,

I'm excited to share my journey as I learn data analysis as a beginner. I'll be sharing everything from mastering Excel to exploring advanced tools, tracking my progress, challenges, and growth. Stay tuned as I explore, learn, and grow!

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Today, I want to share what I’ve learned about data generation, data structures, the ETL process, and how to effectively use pivot tables in Microsoft Excel.

Data Generation

Data generation refers to the process of creating data through various tools and methods. The way data is generated can significantly impact the value derived from it. For example, if we use accurate and reliable tools to gather data, the resulting information will be more trustworthy and useful for analysis. Conversely, poor data generation practices can lead to inaccurate conclusions, affecting decision-making and insights.

Types of Data Structures

In my studies, I learned about three main types of data structures:

1. Structured Data: This data is organized in a tabular format, making it 90% ready for analysis. Examples include spreadsheets and databases where information is easily accessible.

2. Semi-Structured Data: This data does not conform to a strict tabular format. It requires processing before it can be analyzed. An example is JSON or XML files that contain tags but lack a fixed structure.

3. Unstructured Data: This type of data is stored in file formats such as text documents, images, or videos. It needs to be structured or organized before analysis can take place.

ETL Process

I also learned about the ETL process, which stands for Extract, Transform, and Load. This technical process is essential for converting raw data into a structured format suitable for analysis. The goal of ETL is to produce data that can be easily analyzed, leading to actionable insights.

Data Analysis Cycle

Understanding the data analysis cycle was another crucial part of my learning. The cycle consists of the following steps:

1. Ingest the Data: Collect and input data from various sources.

2. Transform: Clean and organize the data to make it usable.

3. Modeling: Create a model to represent the data for analysis.

4. Visualization: Use charts and graphs to represent data visually.

5. Analysis: Interpret the data to extract meaningful insights.

6. Presentation: Share the findings with stakeholders clearly and effectively.

Each of these steps can be practised through real-life data projects, allowing me to understand the entire data analysis process.

Using Pivot Tables in Excel

Pivot tables are powerful tools in Excel that help summarize and analyze large datasets. Here’s a simple way to use them:

1. Create a Pivot Table:
Highlight the existing data.

Copy and paste it into a new location.

Go to the Pivot Table field list and uncheck any fields you don’t want to include.

2. Understanding the Four Buckets: When working with pivot tables, it’s important to understand the four buckets: Rows, Columns, Values, and Filters. Each bucket helps organize your data effectively.

3. Conditional Functions: Excel provides various conditional functions, such as:

IF: Used to return one value if a condition is true and another value if it's false.

SUM: Adds up a range of numbers.

4. Extracting Data from Excel: If you want to extract parts of text from a cell, you can use:

LEFT: Returns a specified number of characters from the start of a string.

MID: Extracts characters from the middle of a string.

RIGHT: Returns a specified number of characters from the end of a string.

5. Changing Text Case: To convert letters to different cases, you can use:

LOWER: Converts all letters to lowercase.
UPPER: Converts all letters to uppercase.
PROPER: Capitalizes the first letter of each word.

6. Generating Random Numbers: To create random numbers in Excel, you can use:

RAND: Generates a random decimal number between 0 and 1

RAND.BETWEEN: Generates a random whole number between two specified values.

Conclusion

As I continue to learn about data analysis, I’ve realized the importance of using updated software like Microsoft Excel. Older versions may limit my ability to perform tasks efficiently, as newer functionalities enhance the analysis process. I look forward to working on real datasets soon to apply everything I’ve learned. Stay tuned for more updates on my learning journey using SQL ( Structured Query Language) and Power BI.

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Hey friends! 👋🏼

I hope you’re all doing great. I wanted to share an exciting update from my journey learning data analysis as a beginner. It’s been a fun and eye-opening experience so far, and I’d love for you to learn alongside me!

When I first started, I thought mastering Excel would be the key to handling all my data needs, but as I went deeper into the field, I discovered there’s so much more to explore. Let me walk you through how Excel and SQL have both shaped my understanding of data analysis, and how they complement each other in ways I never expected. Let's get started 💪🏼

When I First Started Learning Data Analysis

When I Started Learning Data Analysis when I first started learning data analysis, I was excited to learn more on Excel. It felt like the ultimate tool for organising data and making sense of numbers. I could easily use Excel’s pivot tables to summarise data in ways I never thought possible. I was convinced that mastering Excel was the key to unlocking all my data analysis needs. It felt like I had everything figured out.

Progressing with Excel and Facing Its Limitations

But as I progressed and began working with more data, I learned that Excel can struggle when it comes to handling large data sets, and the larger the data the slower Excel becomes, and I began to realise that Excel, as powerful as it is, had its limits.

Discovering SQL as a Complement to Excel

That’s when I was introduced to SQL (Structured Query Language). At first, I was sceptical, could there really be a tool better than Excel for data analysis? But the more I researched and learned, the more I saw how SQL complements Excel, especially when handling large datasets.

Key Excel Functions and How They Relate to Data Analysis

Some key Excel features that build a strong foundation for data analysis are:

Pivot Tables: An excellent way to summarise and reorganise data. They can be compared to SQL's GROUP BY function, which also helps group data for analysis.

Conditional Formulas (IF, SUMIF, COUNTIF): These allow you to perform calculations based on specific criteria. In SQL, similar operations are performed using the WHERE clause to filter data based on conditions.

Text Manipulation (LEFT, RIGHT, MID): In Excel, these functions help extract specific portions of text. In SQL, you can achieve the same with functions like SUBSTRING().

What is a SUBSTRING IN SQL?

A SUBSTRING is a smaller portion or section of a larger text (also called a string). Imagine you have a sentence or a word, and you want to extract only a part of it. That's what a SUBSTRING does. It allows you to select a specific part of a string by defining where to start and how many characters to take.

For example: Extracting the First Name

Suppose we have a table with people's full names, and we want to extract just the first name.

If the full name is "John Smith", the SUBSTRING function can help us extract "John" from it.

SELECT SUBSTRING('John Smith', 1, 4) AS first_name;

'John Smith' is the full name.

1 means the extraction starts at the first letter.

4 means we want the first 4 characters.

The result will be: "John".

From Spreadsheets to Databases: How Excel Skills Prepare You for SQL

Excel and SQL are both tools for manipulating and analysing data, but they work on different scales. Excel is perfect for smaller datasets, while SQL is built for handling large datasets within a Relational Database Management System (RDBMS).

Similarities Between Excel and SQL

Data Filtering: In Excel, you use filters to find specific rows. In SQL, the SELECT statement with a WHERE clause is used to query and retrieve particular rows from a database.

Aggregate Functions: Excel’s SUM and AVERAGE functions allow you to calculate totals and averages. Similarly, in SQL, you can use COUNT, SUM, and AVG to aggregate data.

Data Linking: Just as Excel allows you to connect multiple worksheets or reference data between them, SQL uses JOINS to combine data from different tables within a database. For instance, you might use a LEFT JOIN to link employee data with their salary records in different tables.

Peculiarities of SQL

While both tools handle data, SQL is specifically designed to work with databases. In Excel, you work with cells, rows, and columns. But in SQL, you're dealing with tables, and each table can relate to others in the database through keys such as primary keys and foreign keys.

Primary Key: Uniquely identifies each record in a table. Each table can have only one primary key. For example, an Employee ID in an employee table is a primary key that uniquely identifies each employee.

Foreign Key: Establishes a link between two tables. For instance, an Employee ID in a salary table might be a foreign key, linking the salary record to the corresponding employee in the employee table.

Surrogate Key: A unique identifier for a record, usually created automatically by the database, like an auto-incrementing integer.

The More I Learned, the More I Realised SQL Wasn't a Replacement for Excel

Both tools have their strengths. Excel is perfect for smaller, more straightforward datasets, but when the data gets too big or too complex, SQL steps in to handle the load. SQL isn’t just for professionals working with giant databases; even as a beginner, I quickly saw how it could make my data analysis much easier.

If You’re Just Getting Started with Data Analysis

Excel is a fantastic place to begin. It builds a strong foundation and helps you develop key skills for organising and analysing data. But don’t stop there, SQL is the next step to take when your datasets grow and you need more efficient tools to handle them.

Let’s Keep Exploring Together

I’m still early in my journey, but exploring the connection between Excel and SQL has been eye-opening. Both tools work together, each bringing its own set of strengths to the table. So, if you’re like me starting out and eager to learn know that mastering both Excel and SQL will unlock a whole new world of possibilities in data analysis.

Let’s keep learning💪🏼

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Hey Friends 👋🏼

In today’s lesson, I’m diving deeper into SQL and picking up a few more practical tips that are making things clearer for me.

SQL Basics

SQL is great for managing large datasets in databases. One important thing I learned is that when you declare something as a date, you must put an apostrophe (') around it. This tells SQL to recognize it as a date value, similar to how text values are handled.

Another helpful tip: When writing your code or data query, it’s good to number your lines. To do this, go to Tools, then Options. In the pop-up page, scroll down to Text Editor, select Transact-SQL, click on General, and check the box for Line Number. This helps keep your queries organised, especially when you're working with long codes.

Commands in SQL

Now, let's go over some basic SQL commands:

SELECT: Retrieves data from the database.

INSERT: Adds new data to a table.

UPDATE: Changes existing data in a table.

DELETE: Removes data you no longer need.

A key note for the DELETE command: You can combine it with the WHEN clause to specify the condition under which certain rows should be deleted. For example, you can say "delete rows when a specific condition is met," much like how you’d use conditions in Excel.

One thing to avoid—don’t confuse DELETE with DROP. DROP completely removes a table or database, while DELETE only removes specific data from it.

How to Update a Table in SQL

When you want to update data in a table, the UPDATE command is used. Here’s a simple way to do it:

1. Use UPDATE to specify the table you want to modify.

2. Follow it with SET to identify the column and the new value you want to assign.

3. Include a WHERE clause to specify which rows should be updated. This is important, as it prevents you from accidentally updating the entire table.

For example:

UPDATE SalesTable

SET total_sales = 5000

WHERE sales_id = 10;

This updates the total_sales column for the row where sales_id is 10.

SQL Syntax and Colours

SQL recognizes certain keywords by highlighting them in different colours in your editor. This helps make your code more readable. For instance, commands like SELECT, INSERT, DELETE, and UPDATE are usually coloured differently to stand out, which is a useful feature when writing long queries.

Wrapping Up

SQL really shines when dealing with large amounts of data, and when you pair it with Excel, you get the best of both worlds: SQL for retrieving and filtering data, and Excel for analysing and visualising it. As I continue learning, it’s becoming clearer how these two tools work hand-in-hand for effective data analysis.

That’s all for today! Stay tuned for more updates as I learn more on SQL and Excel.

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Hey Friends 👋🏼

Today, I’m excited to share what I’ve been learning about SQL clauses and SQL logical operators! These are essential for anyone who wants to interact with databases, and they help make data analysis much easier. I'll break down the basics and give you some practical examples to help you understand. If you're new to this, don’t worry, I'm still learning too🤭 , and I’m here to answer any questions you might have!

What are SQL Clauses?

SQL clauses are commands that tell your database how to handle data. They define specific conditions, modify data, and control how your results are returned. Simply put, clauses help you filter, sort, and group data to get exactly what you need from a database.

1. Group By Clause

The GROUP BY clause is used when you want to organise identical data into groups. It’s mostly used with aggregate functions like COUNT(), SUM(), MAX(), MIN(), and AVG().

Example: Imagine you have a table of sales transactions, and you want to see how many sales each employee made. You could group the sales data by employee ID:

SELECT employee_id, COUNT(*) AS total_sales

FROM sales_transactions

GROUP BY employee_id;

(*) - * is in a bracket because we are counting all the sales transactions made by employees

This query groups the data by each employee's ID and counts the total number of sales they made.

Tip: You can only group qualitative data (like names or IDs), while quantitative data (like prices) are typically summed, averaged, or counted.

2. Having Clause

The HAVING clause works like the WHERE clause but is used to filter groups of data after applying the GROUP BY clause. It’s useful when you want to filter specific groups.

Example: Let’s say you want to see only the employees who made more than 10 sales:

SELECT employee_id, COUNT(*) AS total_sales

FROM sales_transactions

GROUP BY employee_id

HAVING COUNT(*) > 10;

This filters the groups, showing only employees with more than 10 sales.

3. Order By Clause

The ORDER BY clause is used to sort your results either in ascending (default) or descending order.

Example: If you want to list employees by the number of sales in descending order:

SELECT employee_id, COUNT(*) AS total_sales

FROM sales_transactions

GROUP BY employee_id

ORDER BY total_sales DESC;

This sorts the results so the employee with the most sales appears first.

SQL Logical Operators

SQL operators are divided into three categories: arithmetic, comparison, and logical operators. Today, I’ll focus on the logical operators.

Logical operators are used to combine conditions or specify the logic in your queries.

Common Logical Operators:

AND: Both conditions must be true.

OR: At least one condition must be true.

NOT: Reverses the condition.

EXISTS: Searches for the presence of a row in a specified table.

LIKE: Compares a value to similar values using a wildcard operator.

Example: To find employees who have made more than 10 sales and work in the Sales department:

SELECT employee_id, COUNT(*) AS total_sales

FROM sales_transactions

WHERE department = 'Sales' AND total_sales > 10;

Practical Example: Combining Clauses

Let’s combine some of these clauses for a practical use case. Say you want to get the total sales for each employee, but only for employees who made more than 5 sales, and you want the results sorted by total sales in descending order.

SELECT employee_id, SUM(sales_amount) AS total_sales

FROM sales_transactions

GROUP BY employee_id

HAVING SUM(sales_amount) > 5

ORDER BY total_sales DESC;

GROUP BY groups the data by employee ID.

HAVING filters out employees with fewer than 5 sales.

ORDER BY sorts the results from highest to lowest total sales.

My Final Thoughts

I still have a lot to learn about SQL, but it's amazing how far I've come. When I started, I knew nothing about data analysis, and now I’m not just learning but applying what I’ve learned to real-world data!

Just like learning SQL, data analysis can feel overwhelming at first, but with practice, it gets easier. Remember, You can't learn Data Analysis all on your own, join a learning community or find an accountability partner to keep you motivated.

That’s all for today, guys! Keep pushing, and don't forget, you've got this 💪🏼. Let me know in the comments if you have any questions or need clarification on anything!

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Hey Friends 😁

It’s another day to learn something new.

Have you ever wondered what happens to all the sales you see inputted on computers at big malls or supermarkets? How do they manage and collect all that data? That’s where SQL comes in, especially SQL JOINS, which helps combine all the sales data made at different times into a single report.

Or think about a time when you got debited but didn’t receive the cash you withdrew from a POS shop in your street, and when you go to the bank, where do you think they retrieve your information from? The information comes from the backend, where relational database systems like SQL Server, Postgres, and MySQL come in handy.

But how do they join all those records into a single table? How do they remove duplicates, or sometimes, keep duplicates when combining data into one table?

That’s where SQL JOINS (like LEFT JOIN, RIGHT JOIN, INNER JOIN, and FULL JOIN) and SQL Operations like UNION and UNION ALL come into play.

SQL JOINS

SQL JOINS are used to combine data from two or more tables.

There are different types of JOINS that determine how the data from these tables are combined.

Here are some common types of SQL JOINS:

LEFT JOIN: This returns all the data from the left table and the matching data from the right table. If there’s no matching value in the right table, it returns NULL (which means no data).

RIGHT JOIN: This does the opposite. It returns all the data from the right table and the matching data from the left. If there’s no match in the left table, it returns NULL.

INNER JOIN: This only returns the rows where there is a match in both tables. If there’s no match, the row won’t be included.

FULL JOIN: This is a combination of LEFT JOIN and RIGHT JOIN. It returns all the data from both tables, and if there’s no match, it fills the missing data with NULL.

Example: How do you which is a Left Table and a Right Table?

Let’s say you have two tables in a shop in Lekki. One table lists all the employees, and the other table lists all the sales transactions they’ve made.

When you LEFT JOIN the employees table with the sales table, the left table is the employees table (because it’s the first one you’re selecting), and the right table is the sales table (the second one).

SELECT employees.employee_id, employees.name, sales.total_sales

FROM employees

LEFT JOIN sales

ON employees.employee_id = sales.employee_id;

In this example, we’re getting all the employees and their sales. If an employee hasn’t made any sales, the result will show NULL for their total sales, but the employee data will still show up.

So, the left table is the one you want all the data from, and the right table is the one you're trying to match data with.

SQL OPERATIONS

SQL operations allow you to combine results from two or more SELECT queries, treating the data like sets of numbers, which lets you do things like Union, Intersection, and Difference. Today, we’ll focus on UNION and UNION ALL.

UNION: This combines the results of two queries but removes any duplicate records. A duplicate is any row where all the values are the same in both queries.

Example: Using UNION to Remove Duplicates

Let’s say you have two shops, one in Lekki and one in Marina, and both shops have their own tables listing the sales they made.

SELECT product_name, total_sales FROM Lekki_sales

UNION

SELECT product_name, total_sales FROM Marina_sales;

This query combines the sales data from both shops, but if the same product was sold in both Lekki and Marina, it will only show the product once (because UNION removes duplicates).

You might want to remove duplicates when you only care about seeing unique products sold, without worrying about which shop sold them.

UNION ALL: This also combines the results of two queries, but it keeps the duplicates.

Example: Using UNION ALL to Keep Duplicates

If you want to see all the products sold, even if they were sold in both Lekki and Marina, you’d use UNION ALL.

SELECT product_name, total_sales FROM Lekki_sales

UNION ALL

SELECT product_name, total_sales FROM Marina_sales;

This will show all the products from both shops, even if some products were sold in both places. You’d use UNION ALL when you want to know the full list of products sold, including duplicates.

Wrapping Up

SQL is a powerful tool that makes handling large amounts of data much easier. With SQL JOINS, you can combine different tables, and with SQL OPERATIONS like UNION and UNION ALL, you can decide whether or not to keep duplicates when combining data from multiple sources.

I’m still learning too, so feel free to ask any questions or share any thoughts! It’s all about practice and getting comfortable with how these tools work.

Keep pushing forward, and don’t forget, you’ve got this!

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Hey Friends 👋🏼

How are we doing today?

Here’s another opportunity to learn about SQL.

What is an SQL View?

An SQL View is a virtual table created based on the result of a SQL query. A view behaves like a table in SQL, allowing you to select, update, insert, and delete data (with some limitations).

Let’s break down the limitations:

A view cannot store data itself; instead, it dynamically retrieves data from one or more underlying tables whenever the view is queried.

The data in a view is not stored physically. It is generated dynamically each time the view is accessed.

A view is defined using a SELECT statement, which can join multiple tables, filter rows, and select specific columns.

Benefits of Using a View

1. Security:

A view can restrict access to certain data in a table by exposing only specific columns or rows to the user.

For example, you can create a view that shows only certain fields of a sensitive table while hiding the rest from the user.

2. Simplicity:

Views simplify complex queries. Instead of writing a complex query repeatedly, you can create a view and use it as a simple table.

Example: Suppose you have a complex query joining multiple tables to generate a sales report. Instead of rewriting the query every time, you can create a view to handle it, making future reporting simpler.

Useful in applications where complex logic needs to be reused:

For example, if you frequently need to pull customer data filtered by purchase history and geographic location, you can create a view that handles these conditions, allowing you to reuse this logic easily.

Note: As your main table is updated, so is your view. If you delete or modify the main table, you won’t be able to access or run commands on the view.

How to Add a Column to a SQL View

1. If you haven’t created the view yet:

Add the new column(s) to the table you want, and then create the view.

2. If the view already exists:

You can delete the view, add the new column to the table, and recreate the view.

However, to avoid the hassle of deleting and recreating the view, you can use this shortcut:

Go to SQL View Information, right-click on the table, and click on "Design." A new page will open, showing the existing columns. Simply check the new column you want to add.

How to Check Which Tables are Joined in a SQL View

When a view is created by joining multiple tables, and you send the output to someone, the person may want to know which tables were used to generate the report.

Here’s how to find out:

1. Right-click on the view.

2. Click on "Design." A screen will open, showing all the tables that were joined to create the view.

3. If you lose your original query, you can copy the SQL information from the design screen and paste it back into your SQL Studio.

How to Export Data from SQL Server to Microsoft Excel

Before exporting, it’s important to ensure your data quality. This means the data you have in SQL Server should be exactly what you intend to export to Excel.

Here’s how to export:

1. Open Microsoft Excel.

2. Click on the "Data" tab.

3. Click "Get Data."

4. You’ll see options like “From Excel Workbook,” “From CSV,” “From JSON,” and others. Since you’re pulling data from SQL, select “From Database.”

5. Choose "SQL Server Database."

6. You’ll need to fill in certain details like the server name and database. While the database field is optional, it’s important to specify which database you’re working with.

Tip: SQL Server has a default database called master, but it’s a good idea to create your own unique database. This helps you keep track of your data and avoid confusion.

My Experience with SQL Databases

I once saved some of my tables to the default master database in SQL by mistake. This caused a lot of confusion when I tried to execute commands later, and I kept encountering errors for weeks. Eventually, I got help from a fellow data analyst, who advised me to start over and create a unique database from scratch.

Lesson learned: Always make sure to save your tables in a unique database that you create, so you don’t lose track of where your data is saved. If you're using master, be sure to stay consistent, but it's better to have a clearly named database for your projects.

Conclusion:

Learning SQL and data analysis can be challenging, especially in the beginning. It can be time-consuming and frustrating, and there are times when you may not feel like showing up. But consistency is key. The results, once you master these tools, are truly rewarding.

Currently, I’m working on data that I am supposed to submit as an assignment project using Microsoft Excel and SQL, but due to light issues and other factors, I haven’t fully mastered it yet. However, I remind myself to keep showing up, because the journey is worth it!

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Hey Friends👋🏼

Hope you all are doing well and making progress in your learning journeys!

Today, I’m going to talk about something really practical and useful when working with databases–SQL’s (Case when) statement. If you have read from my previous posts you already know that SQL helps you make sense of your data, in the same way you’d organise things in real life, like categorising books or clothes based on type or season.

By the end of today’s post, you’ll know how to use the (case when) statement, how to calculate date differences using [Date diff], how to update tables using conditions, and how to import data into your database. Trust me, these are skills you’ll keep coming back to as you work with SQL!

Let’s get started

Breaking Down the Meaning of SQL (Case When)

The (case when) statement in SQL is used to return a specific value based on certain conditions. It's similar to how you make decisions in everyday life. For example, if it's raining, you take an umbrella, and if it's sunny, you enjoy the sunshine. In SQL, this decision-making process helps you categorize data directly in your queries.

Example:

Let’s say you have a table of products, and each product belongs to a category like Electronics, Furniture, or Clothing. You can categorise them based on their category ID using the CASE WHEN statement.

If the category_id is 1, the result will be "Electronics".

If the category_id is 2, it will be "Furniture".

If the category_id is 3, it will be "Clothing".

For any other category, it will return "Other".

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What is (date diff)and How It Is Used in SQL

The (date diff) function in SQL calculates the difference between two dates. You can use it to determine how many days, months, or years are between two points in time.

Example:

If you want to calculate how many days there are between January 1, 2023, and January 10, 2023, the result would be 9 days. This is useful when tracking deadlines or comparing timelines.

You must ensure that you provide three important parameters:

1. The unit of time to measure (e.g., day, month, year).

2. The start date (the beginning point).

3. The end date (the point you're comparing to the start date).

This is similar to sorting things in your life into bins based on their type, making it easier to manage and analyse.

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How to Update a Table Using CASE WHEN

You can use the CASE WHEN statement to update values in your database tables dynamically. Imagine you have a product table and you want to update the stock status of products based on how many items are available.

Example:

You might want to update the stock status like this:

If the product has more than 50 items, label it as "In Stock."

If it has between 1 and 50 items, label it as "Limited Stock."

If it has 0 items, label it as "Out of Stock."

This allows you to dynamically adjust the status of products without manually updating each one. This is especially helpful when managing a large database.

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Conclusion

We’ve covered a lot today! To summarise:

SQL (case when) allows you to categorise or group your data based on specific conditions, making your queries much more dynamic.

(date diff) helps calculate the difference between two dates, a useful tool when tracking timelines or deadlines.

You can use (case when) to update values in your tables dynamically, like updating stock levels in a prod

We’ve covered a lot today! To summarise:

SQL (case when) allows you to categorise or group your data based on specific conditions, making your queries much more dynamic.

(date diff) helps calculate the difference between two dates, a useful tool when tracking timelines or deadlines.

You can use (case when) to update values in your tables dynamically, like updating stock levels in a product database.

We also went over how to import data into SQL, particularly from CSV files, and the steps you need to follow, including potential challenges with data types.

At first, learning SQL can feel overwhelming, and I get that. When I started, it seemed like a mountain to climb, especially in the first two weeks when everything felt complicated. But I kept showing up, practising, and little by little, it started to make sense.

So, if you’re feeling stuck or frustrated, keep pushing through. One day it will click, and you’ll realise that showing up is half the battle won. And even when things get tough, remind yourself that you’re improving every day. It might feel slow, but progress is happening. Just stick with it.

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Hey Friends🤗

How are you doing? I hope your learning journey is going well! For me, it's been an exciting, challenging road, and I’m glad to share what I’m learning along the way. Recently, I finished learning the basics of Microsoft Excel and SQL, though I’m still working on mastering them. My goal is to become proficient with Microsoft Excel, SQL, and Power BI.

Yes, I mentioned Power BI! That’s the tool I’m currently working with, and I’m applying it in a project that combines all three tools: Excel, SQL, and Power BI. This project involves two data tables—Customer Data and Sales Data and I'm using Excel to create summaries, SQL to write queries, and Power BI for creating visualisations. I'll be walking you through this project journey soon, so stay tuned!

What is Power BI?

Power BI is a powerful tool from Microsoft that helps you visualise data. It connects to various data sources, including Excel and SQL databases, allowing you to analyse data and create reports or dashboards. These visualisations make it easier to understand data trends and insights, which is essential for a data analyst.

As you get Started With Power BI, here are Some Tips I was taught to Always bear in mind as Beginner:

As you start with Power BI, here are some key points to keep in mind:

1. Balance Your Visual Elements: When designing reports, aim for a balanced look by carefully choosing textures, colours, and shapes. For example, if you’re using bold colours, balance them with softer tones to avoid overwhelming your audience.

2. Use Patterns to Show Similarities: Apply consistent patterns in chart types, colours, or other design elements to highlight similar information. This can help viewers quickly grasp connections in your data.

3. Proportion Matters: Use proportions wisely to represent data fairly. For example, if one section of a bar chart is significantly smaller, it should reflect an equally smaller value, so the size difference accurately represents the data.

4. Know Your Audience: Ask yourself what your audience is trying to learn from the data. Tailor your visuals to address their questions.

5. Be Critical: Question your data as you work with it. Think about any missing data or insights that could alter the interpretation. This helps avoid bias in your reports.

6. Choose the Right Chart: Different data types require different chart types. For example, use bar charts to compare categories or pie charts for percentages. Selecting the right chart ensures your data is easy to understand.

7. Focus on the User’s Needs: Think about how your audience will use the data and adapt your visuals accordingly. For instance, an interactive dashboard might be useful for a manager who needs to drill down into details.

8. Provide Clear Connections: Include legends, titles, and descriptions to guide viewers through your data, helping them interpret and act on it effectively.

9. Simplify the Design: Remove any unnecessary details. Simple visuals are easier to understand, so avoid clutter to keep your report focused.

10. Tell a Story: Go beyond just showing data. Create a narrative that leads your audience from the beginning of your analysis to the end, guiding them to your insights.

11. Balance Design with Function: Make sure your design is both appealing and functional. The goal is clarity and simplicity—maximising impact and minimising noise.

Note: Remember, while beautiful designs are good, functionality is even better. Imagine a dashboard with fancy graphics but lacking essential information; it looks good but doesn't serve the purpose. A simpler, more straightforward layout with relevant data points will be more useful.

Final Thoughts

This journey with Power BI has been incredible so far, and I’m looking forward to putting my skills to use. In my next post, I’ll write about the different tabs in Power BI to give you a closer look at its interface and features.

Also, keep an eye out as I’ll soon be sharing the story of my project with Customer and Sales Data, where I used Power BI to clean, analyse, and create visualisations. I can’t wait to share the insights and lessons from this project with you. Stay tuned, friends, and thank you for being a part of this journey with me!

Favour_Fejiro

Hey friends!

Today, I.will be explaining the different tabs in Power BI and sharing a quick guide on what each one does. As a beginner in data analysis, understanding the tools in Power BI can feel overwhelming, so I’m breaking it down to make it simple and easy to follow.

Explaining Power BI Tabs and Their Functions

1. Get Data Tab

The Get Data Tab is where you bring data into Power BI, allowing you to connect data from various sources like Excel, SQL, or even online sources. This is usually the first step before you start any analysis or visualisation.

Common Data Sources: You’ll find common options like Excel, SQL Server, Web, and more in the dropdown list. Choosing a data source opens up options to configure the data connection.

Tip: If you want to manually enter data, click on "Enter Data" under this tab. This opens a dialog box (a pop-up window) where you can type in data directly, making it helpful for small datasets or test data.

2. Transform Data Tab

The Transform Data Tab is where you clean, shape, and prepare your data. Clicking on Transform Data takes you to the Power Query Editor.

Power Query Editor: This is like a workspace where you can preview, edit, and refine your data. It shows the data in table format, letting you make adjustments easily before visualising it.

In Power Query Editor, you’ll see options like Load, Transform, and Cancel when previewing your data. Remember, the Transform Data tab will only become active after selecting data.

Some key transformations you can apply:

Removing duplicates: Removes any repeated entries to ensure clean data.

Changing data types: Adjusts data format (e.g., changing text to numbers).

Filtering data: Allows you to remove unwanted rows based on specific conditions.

Splitting columns: Divides data in one column into multiple columns.

Merging columns: Combines data from different columns.

Example: Let’s say you import a table of customer information, but the date of birth is displayed as text. You can use Power Query Editor to change it to a date format, which will make it easier to calculate ages or group customers by age range.

Steps After Transformation: Once you’re done, always click Close & Apply to save changes and load the cleaned data into Power BI for visualisation.

3. New Visuals Tab

The New Visuals Tab is where you add visual elements to your report or dashboard, turning your data into charts, graphs, and more.

Types of Visualisations: When you click the dropdown, you’ll see various chart types like bar charts, line charts, pie charts, and more. Each type helps in representing data differently. For instance:

Bar charts for comparing categories.

Pie charts for showing proportions.

Line charts for showing trends over time.

Adding visuals is as simple as selecting one from the list. You can then customise the look, size, and details of each visual.

4. Refresh Tab

The Refresh Tab is essential when working with live or updated data sources. It helps keep your visuals up-to-date with the latest data.

When to Use It: If the data source has been updated (for example, new sales data in your Excel file), clicking Refresh will reload the data, updating your visuals automatically.

5. Insert Tab

The Insert Tab lets you add various elements to your reports, like text boxes, images, and shapes. This is great for customising and enhancing the readability of your dashboard.

Examples:

Text boxes to add explanations or titles.

Images to include logos or relevant pictures.

Shapes to separate sections or highlight important information.

6. View Tab

The View Tab is all about the design and layout of your report. It allows you to choose themes, gridlines, and page size to make the report visually appealing and user-friendly.

Themes: Pick a colour scheme that fits your brand or report purpose.

Gridlines: Useful for aligning visuals neatly.

Page Size: Choose the best layout for your report based on your audience’s viewing preferences.

Final Thoughts

Understanding Power BI’s tabs is the first step to creating effective data visualisations. Each tab plays a specific role, from importing data to transforming, visualising, and designing it. Mastering these basics helps you confidently explore and analyse data.

As a beginner, remember that consistent practice is essential to gaining confidence. The more you work with Power BI, the more comfortable you’ll become with each of these tabs. Keep experimenting, and don’t be afraid to make mistakes—every step brings you closer to mastering Power BI!

Stay tuned for my next post, Happy learning, and thanks for following along on my data analysis journey!

Favour_Fejiro

Hello Friends!

I hope you’re all doing well! Today, I’ll continue with a breakdown of different tabs in Power BI, explaining each one in a simple way that anyone can understand. Let’s get started!

Choose Columns Tab

The Choose Columns tab lets you select specific columns you want to keep in your data view while hiding the ones you don’t need. This feature helps you

focus on important information, especially in larger datasets. Here are two ways to use it:

1. Click on Choose Columns to display a window that lets you select columns.

2. While displaying your table, place your cursor over the table you want to modify, then click on the Choose Columns tab to make your selections.

Example: If you have columns for customer name, purchase amount, and address but only need the purchase amount, use this tab to keep only that column visible.

Remove Columns Tab

When you click on the dropdown under Remove Columns, you have two options:

Remove Columns: Deletes the columns you select. This is useful if certain columns contain irrelevant or empty data.

Remove Other Columns: Keep only the columns you highlight and remove the rest.

Keep Rows Tab

This tab helps you retain only the rows that meet your criteria, which can be useful for cleaning up your data.

Remove Rows Tab

The Remove Rows dropdown offers options to delete rows based on their position or content. This is helpful for handling rows that might contain unnecessary or empty data.

Ways you can transform Rows: Transpose, Reverse, and Count

1. Transpose rows: Converts rows into columns (and vice versa), which is helpful for restructuring data.

2. Reverse Rows: Reverses the order of rows, useful for prioritising data, like showing the most recent records first.

3. Count Rows: Displays the number of rows, helping you check data volume or validate record counts.

Sort Tab

The Sort tab organises your data in either ascending or descending order, helping you spot trends or outliers.

Example: Sorting sales data from highest to lowest allows you to quickly identify your best-selling products.

Split Column Tab

The Split Column tab allows you to divide a column into multiple columns based on a specific separator, like a comma or space.

Example: If you have a “Full Name” column, you can split it into separate “First Name” and “Last Name” columns for easier analysis.

Understanding Data Types

Every column has a data type that indicates the kind of data it contains, like numbers, text, or dates. Selecting the right data type ensures Power BI interprets and processes your data accurately.

Identifying Data Types:

After loading your data, always verify each column’s data type. For example, ensure date columns are set as “Date” and not as text to avoid errors in your analysis. You can determine the data type by examining each column. When you import data from any source, previewing it during the transformation stage is crucial to check for data consistency.

What's Data Consistency?

Data consistency means that each column has a uniform data format, allowing Power BI to interpret it correctly.

Tip: Review each column for unexpected values or formatting issues to ensure accurate results.

Final Thoughts

I’ve learned not to count the hours I spend practicing but to focus on the productive results those hours bring. Instead of tracking time, I measure my progress, understanding, and every skill I develop as a beginner in data analysis. The true satisfaction I get comes from applying what I’ve learned and seeing it add real value to my learning journey.

Favour_Fejiro

Hello Friends 🤗

Hope you’re all doing great! Today, I’ll walk you through some tabs and important concepts in Power BI that have helped me understand how to view, clean, and manage data better. Let’s get started!

Column Distribution

The Column Distribution feature in Power BI helps you see all the unique values in each column, giving you an overview of the types of entries you have. This can be particularly useful for spotting any unusual characters or duplicates.

Example: If you’re working with a dataset that includes customer names, Column Distribution will show how many unique names there are, helping you quickly spot any repetition or errors.

Column Profile

The Column Profile works on each column individually, showing a detailed breakdown of the column’s contents. It provides information on data quality, including how many values are missing and the percentage of valid data in each column.

Tip 1: To access the Column Profile, go to the View menu and select Column Profile. This tool tells you if there are any missing values in a column and gives a percentage breakdown. For example, if a column shows 100% valid data, your data in that column is fully cleaned. As a data analyst, achieving 100% in each column is the goal for complete data cleanliness.

Tip 2: You can select multiple rows easily:

Click on the first row, hold down the CTRL key, and continue selecting other rows.

Alternatively, click the first row, hold down the SHIFT key, and click the last row you want to highlight, which selects everything in between.

Tip 3: When you upload a file to Power BI (e.g., from Excel or SQL), the data is independent of its source. This means you can freely make changes in Power BI without affecting the original file.

Removing NULL Columns

To remove empty (NULL) cells in a column, go to the Transform stage and click Fill. You’ll see two options: Fill Up and Fill Down.

Fill Up: This fills any empty cells with the value from the cell directly below.

Fill Down: This fills any empty cells with the value from the cell directly above.

Example: Imagine you have a column with a customer’s order history where some cells are blank. Using Fill Down would copy the last known value into the empty cells until it encounters the next non-empty cell.

Understanding Headers

A Header in Power BI (or in any data context) is the top row of a table that describes the data in each column. Headers are like labels–they give context to the data below.

Promoting Headers

Promoting headers means taking a row of data and setting it as the column names (headers) for a table. This is helpful if the headers weren’t set up properly when you imported your data.

How to Promote a Header:

1. Go to the Home menu.

2. Under the Transform tab, you’ll find an option called Use First Row as Headers. Select it to turn the top row into the header row for your data.

Final Thoughts

Even after a few months of learning data analysis, I still stumble upon something new almost every day. It feels a bit scary at times and can make me feel like I haven’t learned much. But I know that becoming proficient takes time. I’m committed to this journey, and with consistent effort, I’ll soon get the hang of everything. Each step forward, no matter how small, brings me closer to my goal!

Check out my GitHub portfolio to check out the progress of my Data Analysis project on my repository :https://github.com/FavourFejiro24

thanni

@Favour_Fejiro It's been a while. How's your learning going?

Favour_Fejiro

@thanni "#p858

Good evening Sir Thanni,

It's going well for me, but I haven't started any hands-on practice projects yet. I currently have one of my data analysis tools not running on my laptop. On a positive note, I plan to practice with SQL and Microsoft Excel, which are running smoothly on my laptop. I will be sharing my learning summary starting next week.

Favour_Fejiro

Hi friends! 🤗

It’s been a minute, how have you all been?

Lately, I’ve been dealing with some technical issues using Power BI on my laptop, which has made hands-on practice a bit challenging. But that hasn’t stopped me from learning! Alongside SQL and Microsoft Excel, I’m diving deeper into Power BI.

Recently, I came across a Twitter post that asked a common data analysis interview question: "How do you create a table using DAX in Power BI?" That got me curious and led me to research more about DAX and how to create simple tables because mastering these tools prepares us for the job market😁

Why Is This Important?

Tables are foundational to data analysis. Mastering how to create and customize them in Power BI enables you to organize data, perform calculations, and effectively visualize insights. This is a crucial skill for presenting findings in a structured, meaningful way.

Using DAX Templates in Power BI

There’s a useful resource called the Power BI Ultimate Template. It includes definitions, columns, hierarchies, and structures that you can use to create tables.

What are hierarchies and structures?

Hierarchies: Think of hierarchies like steps on a ladder. They help you organize data from big to small. For example:

Start with the Year, then go to the Month, and finally the Day.

Or in a sales example: Start with the Region (big area), then the City, and finally the Store (smallest level).

Structures: Structures define how data is arranged or related in a table. Think of it as the foundation that helps organize the table logically. For instance, a structure might include relationships between product categories and sales figures.

With the DAX template, you can either:

1. Use it as it is, or

2. Create your perfect table

What is a perfect table?

A perfect table is one customized to meet your specific needs. For example, if you’re analyzing monthly sales, your table might include columns for Year, Month, Total Sales, and Growth Rate. It’s all about tailoring the data to suit your analysis goals.

Why Learn How to Create a Simple Table in Power BI?

Tables are the foundation of data analysis. Knowing how to create and customize them allows you to organize data, perform calculations, and visualize insights effectively. It’s a crucial skill for presenting your findings in a structured and meaningful way.

Note: All I have explained above is the complex part of creating a table using the DAX template, which doesn't work on the Power BI desktop. However, you can go online to get the Power BI template that works with it.

How to Create a Simple Date Table in Power BI

1. Go to the ‘New Table’ tab to create a table and name it Date.

2. Use the CALENDAR AUTO() function. This function scans your model for date columns and generates a date table containing all the referenced years and dates.

Note: The initial table will only contain dates, so you’ll need to add more columns to enrich it.

3. Use DAX (Data Analysis Expressions) to add columns. DAX lets you write expressions to customize your table, making it easy to copy and reuse.

What is UI?

The UI (User Interface) refers to the visual part of Power BI where you interact with tools and features, like creating columns without writing code. However, DAX is often preferred because it’s more flexible and efficient.

Adding Columns with DAX

The ADDCOLUMNS function helps you create new columns and add them to your table. Let’s imagine we want to add columns for Year, Month, Month Number, and Quarter to our Date table.

Example: Adding Columns with DAX

To extract the Year:

ADDCOLUMNS(CALENDAR AUTO(), "Year", YEAR([Date]))

To format the Month Name:

FORMAT([Date], "mmmm") (This returns names like January, February, etc.)

To extract the Month Number:

MONTH([Date]) (This returns numbers from 1 to 12.)

To add the Quarter:

FORMAT([Date], "\qq") (This formats dates into quarters like Q1, Q2, etc.)

Your table would now look like the above.

TIP: Why CALENDAR AUTO Can Create Too Many Dates

As I already highlighted The CALENDAR AUTO() function scans your model for all date columns and generates a table with the full range of dates found in your dataset. However, this can result in unnecessary dates being included, such as gaps between years or irrelevant time periods.

Why Large Date Tables Are Not Ideal

Large date tables slow down performance and make navigation cumbersome. For most analyses, you only need specific dates and years relevant to your dataset. For instance, if you’re analyzing sales from 2020 to 2023, you don’t need dates from 2010 or 2030.

How to Optimize Your Date Table:

Focus on creating a table that only includes:
Relevant dates for your analysis.
Specific time periods (e.g., fiscal years).

This keeps your data model efficient and easy to manage.

Final Thoughts

Although I’m having technical challenges with Power BI, I’m enjoying this learning journey, and I am excited to share that I’ve qualified for the mentorship phase of the boot camp I joined to learn Data Analysis! So, I’ll sharing my hands-on practices, insights from my mentor, and more about my experiences.

For now, feel free to check out my projects on GitHub. I’ll continue updating my progress and sharing the tips I learn.

So besties 🤗, How’s are your learning journeys going? Let’s connect in the comments!

P.S. So, the interview question was about creating a table using DAX template, and my explanation at the beginning of this post on DAX template may sound straightforward, Well, DAX is like the big boss level of creating tables in Power BI. And guess what? You can’t even use regular Power BI Desktop for it. Nope, for the complex stuff like hierarchy structures, you need the DAX template. (Pro tip: Open Chrome, search for “DAX template,” and there you go!)

Also, fun fact: this question was for an intermediate data analyst, but I thought, "Why not give it a try?" Then I started searching for videos on YouTube on how to create a simple table using DAX functions, not the DAX template. Both are two different things. So there your have it 😁