Forem: Wahu Elizabeth

Getting Started with Python for Data Analytics as a beginner.

Wahu Elizabeth — Tue, 19 May 2026 08:05:06 +0000

In today’s digital world, data is everywhere. Businesses, schools, hospitals and even social media platforms collect huge amounts of information every day. To understand and make use of this data, many professionals rely on one programming language: Python.

As a beginner learning Python, I’ve discovered that it is not just a coding language for software developers. It is also one of the most powerful tools for data analytics. From simple calculations to handling large datasets, Python makes working with data easier and more efficient.

What is Python?

Python is a high-level programming language created to be simple, readable, and beginner-friendly. Unlike some programming languages that are difficult to understand at first, Python uses clear syntax that feels almost like reading English.

For example, printing a message in Python is as simple as:

print("Hello, World!")

Because of its simplicity, Python is widely used by beginners, students, data analysts, software developers, and even artificial intelligence engineers.

Some of the basic concepts beginners usually learn in Python include:

Variables
Data types
Operators
Conditional statements
Loops
Functions
Data structures such as lists and dictionaries

These concepts form the foundation of programming and help learners understand how to solve problems using code.

Why Python is Popular in Data Analytics

Python has become one of the most popular languages in data analytics for several reasons.

1.It is Easy to Learn

Python’s syntax is simple and clean, making it easier for beginners to understand compared to many other programming languages. This allows learners to focus more on solving problems and understanding data instead of struggling with complicated code.

For example:

age = 20

if age >= 18:
    print("Adult")
else:
    print("Minor")

Even someone new to programming can quickly understand what the code is doing.

2.Powerful Libraries

One of Python’s biggest strengths is its libraries. Libraries are collections of ready-made tools that help programmers perform tasks faster.

Two important libraries used in data analytics are:

Pandas
Requests

Pandas is used for working with data. It helps analysts clean, organize, and analyze datasets efficiently.

Example:

import pandas as pd

data = {
    "Name": ["John", "Mary", "David"],
    "Score": [80, 90, 75]
}

df = pd.DataFrame(data)

print(df)

With just a few lines of code, Python can organize data into a table format.

Requests

The Requests library allows Python to get data from websites and APIs. This is useful when collecting live information from the internet.

Example:

import requests

response = requests.get("https://api.github.com")

print(response.status_code)

This makes Python very useful for web data collection and automation.

3.Handles Large Amounts of Data

Organizations work with huge datasets every day. Python can process thousands or even millions of records quickly, making it useful in industries such as finance, healthcare, marketing and education.

4.Strong Community Support

Python has a large global community. This means beginners can easily find tutorials, videos, articles and solutions online whenever they face challenges.

Python Concepts I've learnt so far

Variables and Data Types
Variables are used to store information in Python.

Example:

name = "Elizabeth"
age = 22
height = 5.6

Python supports different data types including:

Strings - "Elizabeth"
Integers - 22
Floats - 5.6
Booleans- True / False

These help programmers work with different kinds of information.

Operators
Operators perform calculations and comparisons.

Example:

x = 10
y = 5

print(x + y)
print(x > y)

Operators are important when analyzing numerical data.

Conditional Statements
Conditional statements help programs make decisions.

Example:

marks = 70

if marks >= 50:
    print("Pass")
else:
    print("Fail")

This logic is commonly used in real-world applications.

Loops
Loops repeat actions multiple times.

Example of a for loop:

for number in range(1, 6):
    print(number)

Loops are useful when working with large datasets because they automate repetitive tasks.

Functions
Functions allow programmers to organize code into reusable blocks.

Example:

def greet(name):
    return f"Hello {name}"

print(greet("Mary"))

Functions make programs cleaner and easier to manage.

Real-World Examples of Python in Data Analytics

Python is used in many industries around the world.

Business and Sales Analysis
Companies use Python to analyze customer purchases, sales performance, and market trends. Businesses can identify which products sell the most and predict future sales.

Social Media Analytics
Social media platforms generate huge amounts of data daily. Python helps analysts track engagement, followers, likes, and online trends.

Healthcare
Hospitals and researchers use Python to analyze patient records, monitor diseases, and improve healthcare decisions.

Finance
Banks and financial institutions use Python to detect fraud, analyze transactions, and predict market behavior.

Weather Forecasting
Meteorologists use Python to analyze weather patterns and improve forecasting systems.

These examples show how Python is helping organizations make smarter decisions using data.

Why Beginners Should Learn Python

Learning Python is a great choice for beginners for several reasons.

Beginner-Friendly

Python is one of the easiest programming languages to start with. The simple syntax helps learners build confidence quickly.

High Demand Skills

Python skills are highly demanded in careers such as:

Data analytics
Data science
Software development
Artificial intelligence
Cybersecurity

Learning Python can open many career opportunities.

Encourages Problem Solving

Programming teaches logical thinking and problem-solving skills. Python helps beginners learn how to break problems into smaller, manageable steps.

Versatile Language

Python is not limited to one field. It can be used in web development, automation, machine learning, mobile applications, and data analytics.

Great for Building Projects

Beginners can quickly create useful projects such as:

Simple calculators
Data dashboards
Expense trackers
Web scrapers
Data analysis projects

Building projects helps learners gain practical experience.

Some popular learning platforms include:

freeCodeCamp - https://www.freecodecamp.org/

Kaggle Learn -https://www.kaggle.com/learn

W3Schools Python Tutorial -https://www.w3schools.com/python/

Final Thoughts
Python has become one of the most important programming languages in the modern world. Its simplicity, readability, and powerful libraries make it especially valuable in data analytics.

As a beginner, learning concepts such as variables, loops, functions, conditional statements, requests, and pandas creates a strong foundation for future growth. While programming may seem challenging at first, consistent practice makes it easier over time.

Python is more than just a coding language — it is a tool that helps people solve real-world problems using data. Whether someone wants to become a data analyst, software developer, or simply improve their technical skills, learning Python is a great place to start.

Onward we go!!!

Understanding Subqueries vs CTEs in SQL (With Examples)

Wahu Elizabeth — Wed, 22 Apr 2026 10:34:02 +0000

When working with SQL, especially in data analysis, you’ll often need to break down complex queries into manageable pieces. Two powerful tools that help with this are subqueries and Common Table Expressions (CTEs).

This article explains what they are, how they differ, and when to use each—using clear examples.

What is a Subquery?

A subquery is simply a query nested inside another SQL query. It runs first and passes its result to the outer query.
Example:

SELECT name
FROM employees
WHERE salary > (
 SELECT AVG(salary)
 FROM employees
 );

What’s happening here?

The inner query calculates the average salary
The outer query returns employees earning above that average

Types of Subqueries

1. Scalar Subquery
Returns a single value.

 SELECT name
 FROM employees
 WHERE salary > (SELECT AVG(salary) FROM employees);

2. Row Subquery
Returns a single row.

 SELECT *
 FROM employees
 WHERE (department_id, salary) = (
 SELECT department_id, MAX(salary)
 FROM employees
);

3. Column Subquery
Returns a single column.

SELECT name
FROM employees
WHERE department_id IN (
SELECT id FROM departments
 );

4. Correlated Subquery
Depends on the outer query and runs repeatedly.

SELECT name
FROM employees e1
WHERE salary > (
  SELECT AVG(salary)
  FROM employees e2
  WHERE e1.department_id = e2.department_id
  );

When Should We Use Subqueries?
Subqueries are useful when:

You need a quick, one-time calculation.
The logic is simple and doesn’t need reuse.
You want to filter data based on aggregated values.

However, they can become hard to read and slow when nested deeply.

What are CTEs (Common Table Expressions)?

A CTE is a temporary result set defined at the start of a query using the WITH keyword. It acts like a named query you can reference later.
Example:

 WITH avg_salary AS (
   SELECT AVG(salary) AS avg_sal
   FROM employees
 )
SELECT name
FROM employees, avg_salary
WHERE employees.salary > avg_salary.avg_sal;

Types and Use Cases of CTEs

1. Simple CTE
Used to simplify complex queries.

WITH high_earners AS (
     SELECT name, salary
     FROM employees
     WHERE salary > 50000
  )
SELECT * FROM high_earners;

2. Recursive CTE
Used for hierarchical data (e.g., organizational charts).

WITH RECURSIVE employee_hierarchy AS (
  SELECT id, name, manager_id
  FROM employees
  WHERE manager_id IS NULL
  UNION ALL
  SELECT e.id, e.name, e.manager_id
  FROM employees e
  INNER JOIN employee_hierarchy eh
  ON e.manager_id = eh.id
  )
  SELECT * FROM employee_hierarchy;

3. Multiple CTEs
You can define multiple CTEs in one query.

WITH dept_avg AS (
    SELECT department_id, AVG(salary) avg_sal
    FROM employees
    GROUP BY department_id
),
filtered AS (
    SELECT e.name, e.salary, e.department_id
    FROM employees e
    JOIN dept_avg d
    ON e.department_id = d.department_id
    WHERE e.salary > d.avg_sal
    )
    SELECT * FROM filtered;

When Should You Use CTEs?

CTEs are ideal when:

Your query is complex and needs structure
You want to reuse logic
You need better readability and debugging
You’re working with hierarchical data (recursion)

Subqueries vs CTEs: Key Comparison

Which One Should You Use?

Use subqueries when:

The query is simple
You only need a quick filter or calculation

Use CTEs when:

The query is complex
You want cleaner, more maintainable code
You need recursion or multiple steps

Final Thoughts
Both subqueries and CTEs are essential SQL tools. The choice between them often comes down to clarity vs simplicity.

If your query is growing complicated or hard to read, that’s usually a sign you should switch to a CTE.

Still learning...

From Scratch to Queries: Building a School Database with SQL – My Nairobi Academy Project.

Wahu Elizabeth — Tue, 14 Apr 2026 15:06:44 +0000

Transitioning from viewing data as simple lists to managing it within a relational database is a milestone for any developer. This week, I took on the task of building a foundational system for** Nairobi Academy**. The goal was to move beyond theory and implement a functional database that tracks students, courses, and enrollments.

Here is a breakdown of the SQL fundamentals used to bring this project to life.

Understanding the Language: DDL vs. DML
Before writing a single line of code, it is essential to distinguish between the two primary "modes" of SQL:

DDL (Data Definition Language): Think of this as the blueprint phase. DDL commands define the structure of the database. When you create a table or change its columns, you are using DDL.

DML (Data Manipulation Language): This is the action phase. Once the structure exists, DML allows you to interact with the actual data—adding new students, updating grades, or removing records.

The Difference: DDL changes the container (the table), while DML changes the content (the rows).

Building the Framework: CREATE, INSERT, UPDATE, and DELETE
In the Nairobi Academy project, these four commands formed the core lifecycle of our data:

CREATE: I used this DDL command to establish our tables. For example, creating the Students table required defining data types like INT for IDs and VARCHAR for names.

INSERT: Once the tables were ready, I populated them with student records and course details. This is where the database actually starts to look like a school roster.

UPDATE: Data is rarely static. When a student changed their contact information or a course name was adjusted, UPDATE allowed me to modify existing rows without deleting them.

DELETE: This was used to remove records that were no longer relevant, such as a student who withdrew from a specific semester.

Precision with the WHERE Clause
A database with thousands of rows is useless if you can't find exactly what you need. The WHERE clause is the ultimate filter. During the project, I utilized several operators to refine my queries:

= and >: Used for direct matches or finding students above a certain age.

BETWEEN: Perfect for finding enrollments within a specific date range.

IN: Allowed me to filter students belonging to a specific list of departments (e.g., 'Science', 'Arts').

LIKE: A powerful tool for pattern matching, such as finding all students whose names start with "J" using LIKE 'J%'.

Transformation with CASE WHEN
One of the most interesting parts of the assignment was using the CASE WHEN statement. This functions like "if-then" logic within a query. Instead of just pulling raw numbers, I used it to transform data on the fly.

For example, I used CASE WHEN to categorize student marks:

If a score was above 80, the query returned 'Distinction'.

If it was between 60 and 79, it returned 'Pass'.

This allows the database to provide meaningful insights rather than just raw data points.

Reflection: Challenges and Wins
This week was an eye-opener. The most challenging aspect was ensuring "Referential Integrity"—making sure I didn't delete a student who was still enrolled in a course, which would cause a conflict in the database.

The most interesting part? Seeing how a few lines of SQL can transform a massive pile of disorganized information into a structured, searchable system. Building the Nairobi Academy project made the abstract concept of "data" feel tangible and manageable.

Next step: Mastering Joins to connect these tables even more deeply.

I really enjoyed this.

My First Experience Using Excel for Real-World Data Analysis.

Wahu Elizabeth — Thu, 26 Mar 2026 16:13:39 +0000

Before starting this project, I honestly knew almost nothing about Excel. I had heard about it, seen people use it, but I never really understood how powerful it is. As just a girl trying to figure things out, I was honestly a bit nervous and overwhelmed at first. But at the same time, I was open to learning and ready to take on the challenge. Like many beginners, I saw it as just a tool for storing data in rows and columns. However, through hands-on practice, I quickly realized that Excel is far more powerful—it is a complete data analysis tool used by professionals across industries.

This article shares how Excel is applied in real-world data analysis, based on my experience building a product performance dashboard.

What is Excel?
Microsoft Excel is a tool used to organize, clean, analyze, and present data. It works using rows and columns, and it has features like formulas, PivotTables, charts, and slicers that help turn raw data into meaningful information. Even in 2026, Excel remains one of the most widely used tools for data analysis due to its accessibility and versatility

At first, it looked complicated—but as I started using it, I slowly began to understand how everything connects.

🌍 Real-World Use

I learned that Excel is used almost everywhere—in business, marketing, finance, and even e-commerce. For example, a company like Jumia can use Excel to analyze:

Product prices
Discounts
Customer reviews
Ratings

This helps them make decisions like:
Which products to promote
Whether to increase or reduce prices
Which products need improvement

Seeing this made me realise that Excel is not just about numbers—it actually helps businesses grow.

🛠️ What I Learned (As a Beginner)

Even though I started from zero, I managed to learn and use several important Excel tools:

🔹 Data Cleaning
This was one of the hardest parts at first, but also the most important.
I used:
Text to Columns
Find & Replace
Functions like LEFT(), VALUE(), and ABS()
This helped me:
Remove “KSh” and commas
Convert text into numbers
Fix negative review values
Extract ratings from text

🔹 Data Enrichment
I created new columns using formulas:
Discount Amount
Rating Category (Poor, Average, Excellent)
Discount Category (Low, Medium, High)
At first, formulas looked scary—but once I understood them, they became really useful.

🔹 PivotTables
This was honestly one of the most powerful tools I learned. It made analyzing data much faster and easier.
I used PivotTables to:
Find averages
Identify top products
Group data into categories

🔹 Charts & Visuals
I created:
Bar charts for top products
Donut charts for categories
Scatter plots to see relationships
This is where everything started to make sense visually.

🔹 Slicers & Interactivity
This part felt really cool 😄
I added slicers so the dashboard can filter data instantly.
It made everything interactive and more professional.

🔹 Conditional Formatting
I used colors to highlight:
High discounts (green)
Low ratings (red)
This made important insights easy to spot.

💡 Personal Reflection

This experience completely changed how I see data.
At the beginning, I was confused and honestly a bit stressed because everything felt new. But as I kept practicing, I started understanding patterns and relationships.
Now I can look at data and think:
Why is this happening?
What does this mean?
What decision can be made from this?

For example, I noticed that:
Medium discounts sometimes have better ratings
High discounts don’t always mean more reviews

That really surprised me.

I’m still a beginner, and I know I have a lot to learn but now I feel more confident. This is just the start of my journey into data analysis, and I’m excited to keep improving.