DEV Community: Seenivasan A

Power Bi -Reports

Seenivasan A — Fri, 03 Jul 2026 18:07:18 +0000

World GDP 2025 – Bubble Map Visualization

To better understand the global economy, I created a Map Visualization in Microsoft Power BI using the 2025 GDP by Country dataset. This visualization displays each country on a world map with bubbles representing its GDP value.

The bubble size is determined by the country's Gross Domestic Product (GDP) in 2025. Countries with larger GDP values appear with larger bubbles, while countries with smaller GDP values appear with smaller bubbles. This makes it easy to compare the economic size of different countries at a glance.

For example:

The United States has one of the largest bubbles because it has one of the world's highest GDP values.
China also appears with a large bubble due to its strong economy.
Countries with smaller economies are represented by smaller bubbles.

This visualization helps users:

Compare the GDP of countries around the world.
Identify the largest economies in 2025.
Understand the global distribution of economic activity.
Analyze regional economic differences using an interactive map.

Power BI Configuration

To create this map, I configured the visualization as follows:

To create this map visualization, I configured the fields as follows:

Location: City
Bubble Size: GDP (2025)

Power BI automatically identified the geographic locations of the cities and plotted them on the world map. The GDP (2025) field was assigned to the Bubble Size, so cities with higher GDP values are displayed with larger bubbles, while cities with lower GDP values appear with smaller bubbles.

Gold and Silver Percentage Analysis Using Ribbon Chart

After importing the Gold and Silver dataset into Power BI, I created a Ribbon Chart to compare the yearly percentage changes in gold and silver prices. This visualization helps identify which metal performed better over different years and how their percentage changes varied over time.

In this report:

The X-axis represents the Year.
The Y-axis represents the Percentage Change (%) in gold and silver prices.
The Dark Blue Ribbon represents the Gold Change Percentage.
The Light Blue Ribbon represents the Silver Change Percentage.

The Ribbon Chart clearly shows both positive and negative price movements. Values above zero indicate an increase in price, while values below zero indicate a decrease. During some years, gold recorded higher returns than silver, whereas in other years silver outperformed gold.

This type of visualization makes it easy to:

Compare yearly performance of gold and silver.
Identify years with the highest and lowest returns.
Observe market trends over time.
Analyze fluctuations in precious metal prices.
Compare multiple categories in a single visual.

To create this visualization in Power BI:

Load the Gold and Silver dataset.
Select the Ribbon Chart from the Visualizations pane.
Drag Year into the Axis field.
Drag Gold Change % and Silver Change % into the Values field.
Customize the chart by adding titles, labels, and colors for better readability.

Filters in Power BI

Seenivasan A — Wed, 01 Jul 2026 19:04:28 +0000

Filters in Microsoft Power BI help refine data in reports by displaying only the information that meets specific conditions. They allow users to focus on relevant data subsets, making visualizations clearer and analysis more effective. Filters can be applied at the visual, page, or report level to control how data appears across a report.

Types of Power BI Filters
Visual-level Filters: Apply only to a specific visual. They allow you to control the data shown in that particular chart or table without affecting others.
Page-level Filters: Apply to all visuals on a single report page. Any filter applied here updates every visual on that page, ensuring consistent analysis.
Report-level Filters: Apply across all pages in the report. These filters maintain uniform data conditions throughout the entire report.
Drill-through Filters: Allow navigation to another page with filtered context. They help in analyzing detailed data related to a selected data point.

1. Basic Filtering
Basic filtering is the simplest and most intuitive filter mode. It provides a scrollable and searchable list of values for a specific field in our data. We can quickly search for a value and select it or use checkboxes to select multiple values.

This mode is ideal for situations where we simply want to focus on one or more data points without needing to apply complex conditions. For example, we can filter by specific countries or product categories with ease.

2. Advance Filtering
Advanced filtering gives us greater control over how data is filtered by allowing us to define more specific criteria. Instead of just selecting from a list, we can apply rules such as greater than, less than, between a range of values or even exclude certain values.

This mode is useful when we need to filter data based on numerical ranges or specific conditions. For example, we might want to exclude certain countries from the report or filter products with sales above a specific threshold.

3. Top-N Filtering
Top-N filtering is used when we want to focus on the highest or lowest N values in a dataset. This filter type allows us to quickly identify top performers or outliers. We can specify how many top or bottom items we want to display such as the top 5 countries by sales or the bottom 3 products by revenue.

Top-N filters are useful in identifying trends or analyzing the extremes within our data. For example, we might filter for the top 10 products in terms of sales or the lowest 5 regions in terms of performance.

How to add Power BI Filter to a Visual?
Step 1: Select the Visual
Step 2: Drag and Drop Fields
Step 3: Apply the Filter

How to add a Power BI Filter to a Page
Step 1: Create Multiple Visuals
Step 2: Use Visual-Level Filters
Step 3: Apply Page-Level Filters
Step 4: Select Filter Values

How to add Power BI Filter to a Report
Step 1: Create a Visual
Step 2: Populate the Visual
Step 3: Duplicate the Page
Step 4: Apply the Report-Level Filter

Step 5: Applying selected filter values across the entire report
Step 6: View the Filtered Report

Software Development Life Cycle (SDLC)

Seenivasan A — Tue, 30 Jun 2026 16:42:04 +0000

The Software Development Life Cycle (SDLC) is a structured process used to plan, design, develop, test, deploy, and maintain software. It ensures a systematic workflow and helps align software development with business goals and user requirements.

Provides a clear and organized framework for managing development phases
Helps in early detection of defects, reducing overall cost and time
Ensures high-quality software delivery that meets user expectations

Stage 1: Planning & Feasibility Analysis
This stage determines whether the project is technically, financially, and operationally feasible.

Activities: Feasibility analysis, cost estimation, scheduling, resource planning
Output: Project Plan, Feasibility Report
Key Roles: Project Managers, Senior Engineers, Stakeholders

Stage 2: Requirement Specification (SRS)
In this stage, detailed functional and non-functional requirements are documented clearly and approved by stakeholders.

Activities: Requirement gathering, validation, documentation
Output: Software Requirement Specification (SRS)
Key Roles: Business Analysts, Product Owners

Stage 3: System Design
In this stage, the approved requirements are transformed into a technical blueprint for implementation.

High-Level Design (HLD): Defines system architecture, technology stack, database design, and major modules.
Low-Level Design (LLD): Specifies component logic, APIs, data structures, and workflows.
Output: Design Document Specification (DDS)

Stage 4: Development (Coding)
Developers build the software based on the approved design.

Activities: Coding, code reviews, unit testing, version control management
Tools: IDEs, version control systems, debuggers
Output: Source code, executable application
Key Roles: Frontend, Backend, Full Stack Developers

Stage 5: Testing
Testing ensures the software meets requirements and is free from defects before release.

Types of Testing include:

Unit Testing: Verifies individual components
Integration Testing: Ensures modules work together
System Testing: Validates the complete system
User Acceptance Testing (UAT): Confirms business requirements are met
Output: Test cases, defect reports, and quality metrics.

Stage 6: Deployment
The tested software is released to users.

Activities: Production setup, deployment, smoke testing
Modern Approach: Continuous Integration and Continuous Deployment (CI/CD) pipelines for faster and reliable releases
Output: Live application
Key Roles: DevOps Engineers, Release Managers

Stage 7: Maintenance
Post-deployment support ensures long-term usability.

Activities: Bug fixes, performance tuning, updates, feature enhancements
Output: Patches, updates, new versions
Key Roles: Support Engineers, Developers

References:
https://www.geeksforgeeks.org/software-engineering/software-development-life-cycle-sdlc/

Getting Started with Power BI: Creating Your First Interactive Dashboard

Seenivasan A — Mon, 29 Jun 2026 18:49:47 +0000

This blog explains the basic steps involved in creating a Power BI dashboard, including loading data, creating visualizations, using filters, and displaying important business metrics.

Loading Data into Power BI

The first step in Power BI is importing data. Power BI supports many data sources such as Excel, CSV files, SQL Server, and cloud databases.

To load data:

Open Power BI Desktop.
Select Get Data from the Home tab.
Choose a data source (Excel, CSV, SQL Server, etc.).
Select the required table.
Click Load.

After loading the data, all columns appear in the Fields pane and are ready for analysis.

Creating Basic Visualizations

Power BI provides many built-in charts that help users understand data quickly.

Some commonly used visualizations include:

Bar Chart
Column Chart
Pie Chart
Line Chart
Table
Matrix

For example, if a sales dataset contains Product and Sales Amount, users can create a bar chart by dragging Product to the Axis field and Sales Amount to the Values field. Power BI automatically creates a chart showing the sales of each product.

Interactive charts make it easy to compare values and identify business trends.

Using Card Visuals

Card visuals display important numerical information in a simple format. They are commonly used in dashboards to show Key Performance Indicators (KPIs).

Examples include:

Total Sales
Total Orders
Average Sales
Total Customers
Count of Products

For example:

Sum displays the total sales value.
Average calculates the average sales.
Count shows the total number of records.

Card visuals provide a quick overview of important business metrics.

Applying Filters and Slicers

Filters help users display only the required data.

For example, users can filter:

A specific year
A product category
A region
A customer segment

Power BI also provides Slicers, which are interactive filters placed directly on the report page. When a user selects a value in a slicer, all connected charts update automatically.

This feature makes dashboards interactive and easier to explore.

Customizing Visualizations

Power BI allows users to customize every visual through the Visualizations pane and the Format pane.

Users can:

Change chart colors
Edit chart titles
Modify axis labels
Add data labels
Adjust fonts
Change background colors
Resize and align visuals

These customization options help create professional and attractive dashboards.

Building an Interactive Dashboard

A beginner dashboard usually contains:

A Bar Chart showing sales by product
A Pie Chart showing category distribution
Cards displaying Total Sales, Average Sales, and Total Orders
A Slicer for filtering data by year or region

This combination provides both summary information and detailed analysis on a single page.

Conclusion

Microsoft Power BI is a powerful and user-friendly Business Intelligence tool that helps users transform raw data into meaningful insights. By learning how to load data, create visualizations, use card visuals, apply filters, and customize reports, beginners can build interactive dashboards that support better decision-making.

Learning these basic features is the first step toward becoming a skilled Data Analyst or Business Intelligence professional.

Power BI

Seenivasan A — Thu, 25 Jun 2026 17:56:18 +0000

In today's data-driven world, organizations collect large amounts of data every day. However, raw data alone cannot help businesses make better decisions. This is where Microsoft Power BI becomes an important tool. Power BI is a Business Intelligence (BI) and data visualization platform that helps users convert raw data into meaningful reports and interactive dashboards.

Power BI is widely used by Data Analysts, Business Analysts, and organizations to monitor business performance and identify trends. Its easy-to-use interface allows users to connect multiple data sources, transform data, create visualizations, and share reports with others.

What is Power BI?

Microsoft Power BI is a Business Intelligence tool developed by Microsoft. It enables users to connect to different data sources such as Excel, SQL Server, CSV files, and cloud services. Users can clean, analyze, and visualize data without writing complex code.

Power BI consists of three main components:

Power BI Desktop – Used to create reports and dashboards.
Power BI Service – A cloud platform used to publish and share reports.
Power BI Mobile – Allows users to view reports on mobile devices.

Key Features of Power BI

Power BI offers several powerful features that make data analysis easier:

Interactive dashboards
Drag-and-drop report creation
Real-time data monitoring
Data transformation using Power Query
DAX (Data Analysis Expressions) for calculations
Multiple data source connectivity
Secure report sharing

These features help organizations analyze business data quickly and make informed decisions.

Data Visualization in Power BI

One of the biggest advantages of Power BI is its rich collection of visualizations. Instead of reading large tables, users can understand information through charts and graphs.

Some commonly used visualizations include:

Bar Chart
Line Chart
Pie Chart
Donut Chart
Table and Matrix
Map Visualization
KPI Cards
Slicers for filtering data

Choosing the correct visualization helps users understand trends and patterns more effectively.

Data Transformation with Power Query

Before creating reports, datasets often need cleaning and preparation. Power BI provides Power Query Editor, which helps users:

Remove duplicate records
Handle missing values
Change data types
Merge multiple tables
Split or combine columns
Filter unnecessary data

Power Query reduces manual work and prepares data for accurate analysis.

DAX in Power BI

Power BI uses Data Analysis Expressions (DAX) to perform calculations and create custom measures.

Example:

Total Sales = SUM(Sales[Amount])

This formula calculates the total sales amount from the Sales table.

DAX allows analysts to create calculated columns, measures, and business metrics for better reporting.

Why Should We Learn Power BI?

Power BI has become one of the most popular Business Intelligence tools because it is easy to learn and widely used in industries such as finance, healthcare, retail, manufacturing, and education.

Benefits of learning Power BI include:

Better data visualization skills
Faster business reporting
Interactive dashboards
Easy integration with Microsoft Excel
High demand in Data Analyst job roles

For students and beginners, learning Power BI can improve analytical skills and increase career opportunities.

Conclusion

Microsoft Power BI is a powerful Business Intelligence tool that transforms raw data into meaningful insights through interactive dashboards and visual reports. Its features such as Power Query, DAX, and real-time visualization help businesses make better decisions based on data.

As organizations continue to rely on data-driven strategies, Power BI has become an essential skill for aspiring Data Analysts and Business Intelligence professionals. Learning Power BI is an excellent step toward building a successful career in data analytics.

Python-Sets

Seenivasan A — Thu, 04 Jun 2026 17:47:14 +0000

Set is used to store a collection of items with the following properties.

No duplicate elements. If you try to insert the same item again, it is ignored because sets store only unique values.
An unordered collection. When we access all items, they are accessed without any specific order and we cannot access items using indexes as we do in lists.
Internally use hashing that makes set efficient for search, insert and delete operations. It gives a major advantage over a list for problems with these operations.
Mutable, meaning we can add or remove elements after their creation, the individual elements within the set cannot be changed directly.

s = {10, 50, 20}
print(s)
print(type(s))

Output
{10, 50, 20}

Method |Shortcut |Description

add() Adds an element to the set
clear() Removes all the elements from the set
copy() Returns a copy of the set
difference() - Returns a set containing the difference between two or more sets
difference_update() -= Removes the items in this set that are also included in another, specified set
discard() Remove the specified item
intersection() & Returns a set, that is the intersection of two other sets
intersection_update() &= Removes the items in this set that are not present in other, specified set(s)
isdisjoint() Returns True if NO items of this set is present in another set
issubset() <= Returns True if all items of this set is present in another set
< Returns True if all items of this set is present in another, larger set
issuperset() >= Returns True if all items of another set is present in this set
> Returns True if all items of another, smaller set is present in this set
pop() Removes an element from the set
remove() Removes the specified element
symmetric_difference() ^ Returns a set with the symmetric differences of two sets
symmetric_difference_update() ^= Inserts the symmetric differences from this set and another
union() | Return a set containing the union of sets
update() |= Update the set with the union of this set and others

Check unique and Immutable
Sets cannot have duplicate values. While you cannot modify the individual elements directly, you can still add or remove elements from the set.

# a set cannot have duplicate values
s = {"Geeks", "for", "Geeks"}
print(s)

# values of a set cannot be changed
s[1] = "Hello"
print(s)

Output

{'Geeks', 'for'}
TypeError: 'set' object does not support item assignment

Python frozenset

frozenset is an immutable version of a set.
Like sets, it contains unique, unordered, unchangeable elements.
Unlike sets, elements cannot be added or removed from a frozenset.

a = frozenset([1, 2, 3, 4])
b = frozenset([3, 4, 5, 6])

c = a.copy()
print(c)

print(a.union(b))
print(a.intersection(b))
print(a.difference(b))
print(a.symmetric_difference(b))

Output
frozenset({1, 2, 3, 4})
frozenset({1, 2, 3, 4, 5, 6})
frozenset({3, 4})
frozenset({1, 2})
frozenset({1, 2, 5, 6})

Method |Shortcut |Description

copy() Returns a shallow copy
difference() - Returns a new frozenset with the difference
intersection() & Returns a new frozenset with the intersection
isdisjoint() Returns True if there is NO intersection between two frozensets
issubset() <= / < Returns True if this frozenset is a (proper) subset of another
issuperset() >= / > Returns True if this frozenset is a (proper) superset of another
symmetric_difference() ^ Returns a new frozenset with the symmetric differences
union() | Returns a new frozenset containing the union

Internal working of Set

Python sets are implemented using a hash table, similar to dictionaries, where the set elements are stored as keys with dummy values. If multiple elements map to the same index, Python handles the collision by storing them in the same bucket.

The diagram below illustrates how sets internally manage collisions to support efficient insertion, deletion, and lookup operations.

Each index in the hash table represents a possible hash value where elements are stored based on their computed hash key.
When two elements map to the same index, they are linked together using a linked list, forming a chain at that position.
This chaining mechanism helps efficiently handle collisions, allowing multiple elements to exist at the same hash index without overwriting each other.

References
https://www.geeksforgeeks.org/python/sets-in-python/
https://www.w3schools.com/python/python_sets.asp

Python-List,Tuples

Seenivasan A — Wed, 03 Jun 2026 17:25:48 +0000

List

A List is an ordered collection of items that can store different types of data such as numbers, strings, or even other lists. Lists are mutable, which means their contents can be modified after creation.

Lists are one of 4 built-in data types in Python used to store collections of data, the other 3 are Tuple, Set, and Dictionary, all with different qualities and usage.
List is a built-in data structure used to store an ordered collection of items. They are dynamic, resizable and capable of storing multiple data types.
Mutable: list elements can be changed, updated, added, or removed after the list is created.
Ordered: elements maintain the order in which they are inserted.
Index-based: elements are accessed using their position, starting from index 0.

a = [1, 2, 3]
print(a)

b = ["apple", "banana"]
print(b)

a = list((1, 2, 3, 'apple', 4.5))  
print(a)

b = list("GFG")
print(b)

Output
[1, 2, 3]
['apple', 'banana']
[1, 2, 3, 'apple', 4.5]
['G', 'F', 'G']

Method Description

append() Adds an element at the end of the list
clear() Removes all the elements from the list
copy() Returns a copy of the list
count() Returns the number of elements with the specified value
extend() Add the elements of a list (or any iterable), to the end of the current list
index() Returns the index of the first element with the specified value
insert() Adds an element at the specified position
pop() Removes the element at the specified position
remove() Removes the item with the specified value
reverse() Reverses the order of the list
sort() Sorts the list

Use Lists When:

Data needs frequent updates.
Elements need to be added or removed.
Working with dynamic datasets.

Tuples

A Tuple is also an ordered collection of items, but unlike lists, tuples are immutable. Once a tuple is created, its values cannot be changed.

Tuples are similar to lists, but unlike lists, they cannot be changed after their creation.
Can hold elements of different data types.
These are ordered, heterogeneous and immutable

Tuples can store elements of different data types, such as integers, strings, lists and dictionaries, within a single structure.

Tuples can be concatenated using the + operator. This operation combines two or more tuples to create a new tuple

Method Description

count() Returns the number of times a specified value occurs in a tuple
index() Searches the tuple for a specified value and returns the position of where it was found

tup = ()
print(tup)

# Using String
tup = ('Geeks', 'For')
print(tup)

# Using List
li = [1, 2, 4, 5, 6]
print(tuple(li))

# Using Built-in Function
tup = tuple('Geeks')
print(tup)

Output
()
('Geeks', 'For')
(1, 2, 4, 5, 6)
('G', 'e', 'e', 'k', 's')

Slicing of Tuple
Slicing a tuple means creating a new tuple from a subset of elements of the original tuple. The slicing syntax is tuple[start:stop:step].

tup = tuple('GEEKSFORGEEKS')
print(tup[1:])
print(tup[::-1])
print(tup[4:9])

Output
('E', 'E', 'K', 'S', 'F', 'O', 'R', 'G', 'E', 'E', 'K', 'S')
('S', 'K', 'E', 'E', 'G', 'R', 'O', 'F', 'S', 'K', 'E', 'E', 'G')
('S', 'F', 'O', 'R', 'G')

Use Tuples When:
Data should remain unchanged.
Storing fixed information.
Better performance is required.

Lists and Tuples are fundamental data structures in Python. Lists provide flexibility because they can be modified, while Tuples provide safety and better performance because they cannot be changed. Choosing the right data structure depends on the application's requirements. Understanding these concepts helps programmers write more efficient and organized Python code.

References
https://www.w3schools.com/python/python_lists.asp
https://www.geeksforgeeks.org/python/python-lists/
https://www.geeksforgeeks.org/python/python-tuples/
https://www.w3schools.com/python/python_tuples.asp

AI in Data Analytics

Seenivasan A — Mon, 25 May 2026 19:02:59 +0000

Why AI is Used in Data Analytics

AI is used because it saves time and reduces manual work. Data Analysts often spend many hours cleaning data and writing code. AI tools help complete these tasks faster and more accurately.

Benefits of AI include:

Faster analysis
Better accuracy
Easy automation
Quick visualization
Faster code generation

AI also helps beginners understand coding concepts more easily.

Recently, I attended a webinar conducted by Brand Monk Academy about how Artificial Intelligence (AI) is used in Data Analytics. The session explained how Data Analysts use AI tools to make their work easier, faster, and more efficient.

The webinar mainly focused on four important topics:

Data Cleaning
Data Visualization
Automation
Data Generation

These concepts are very important in modern Data Analytics because companies work with huge amounts of data every day.

Data Cleaning

Data cleaning is the process of correcting or removing unwanted data from a dataset. Before analyzing data, it is important to make sure the data is accurate and organized properly.

The speakers explained that datasets often contain:

Missing values
Duplicate records
Incorrect information
Empty rows

Cleaning the data helps improve accuracy and makes analysis easier. AI tools can now help automate this process and reduce manual work.

Data Visualization

Data visualization means presenting data using charts, graphs, and dashboards. It helps people understand information more clearly.

The webinar showed different visualization methods such as:

Bar charts
Pie charts
Line graphs

Visualization helps businesses identify trends and make better decisions. AI tools can also suggest suitable charts automatically based on the data.

Automation

Automation is one of the biggest advantages of AI in Data Analytics. Instead of doing repetitive tasks manually, analysts can use automation tools and code to complete tasks quickly.

The speakers also explained how Excel Developer Options and VBA macros are used for automation.

Some automated tasks include:

Data formatting
Removing duplicates
Creating reports
Performing calculations automatically

Automation saves time and improves productivity.

The speakers also explained how Excel Developer Options and VBA macros are used for automation.

Sub RemoveDuplicates()

    Range("A1:A10").RemoveDuplicates Columns:=1, Header:=xlNo

    MsgBox "Duplicate values removed successfully!"

End Sub

After the code, continue with this explanation:

This VBA macro removes duplicate values from Excel data automatically and displays a completion message. VBA helps Data Analysts automate repetitive tasks and improve productivity while working with large datasets.

Google Colab and Gemini AI

One of the most interesting topics in the webinar was using Google Colab and Gemini AI for Data Analytics.

Google Colab allows users to write and run Python code online without installing software. Gemini AI helps users generate code, create datasets, analyze data, and visualize information using simple prompts.

The speakers demonstrated how AI can:

Generate Python code
Create sample datasets
Analyze CSV files
Generate charts and graphs
Explain coding errors
Automate repetitive tasks

For example, users can simply ask Gemini AI to:

“Create a bar chart for sales data”

and AI can generate the required code automatically.

This makes learning Data Analytics easier for beginners and improves productivity for professionals.

Will AI Replace Jobs?
One important discussion in the webinar was whether AI will replace Data Analyst jobs.

The speakers explained that AI may automate some repetitive tasks, but it will mainly improve jobs rather than completely replace them. Human thinking, decision-making, creativity, and business understanding are still very important.

AI works as a supporting tool for analysts by helping them:

Work faster
Reduce errors
Improve productivity
Focus on important decisions

Therefore, learning AI tools can help Data Analysts improve their skills and grow in their careers.

Conclusion

The webinar provided a simple understanding of how AI is transforming Data Analytics. Concepts like data cleaning, visualization, automation, and AI-assisted coding are becoming essential in modern workplaces.

Learning tools like Google Colab and Gemini AI can help students and professionals improve their technical knowledge and work more efficiently in the future.

Certificate

CRACKING CODING INTERVIEW

Seenivasan A — Sun, 24 May 2026 17:59:51 +0000

Technical interviews at top companies like Google, Amazon, and Meta are often centered around coding and algorithm-based problem-solving. Many students feel nervous about these interviews because they are very different from regular academic exams. However, understanding the interview process can make preparation easier and more effective.
In most software engineering interviews, candidates are given one or two coding problems and are expected to explain their thought process while solving them. Interviewers are not only checking whether the final answer is correct, but also evaluating how candidates think, communicate, and approach difficult problems.

What Interviewers Evaluate

Interviewers generally focus on five important areas:

1. Analytical Skills

- This is one of the most important aspects of coding interviews. Interviewers observe how candidates analyze a problem, identify patterns, and choose efficient solutions.
- For example, if a candidate is asked to find duplicate elements in an array, a brute-force solution may work, but an optimized approach using a hash set demonstrates stronger problem-solving ability.

Example

nums = [1, 2, 3, 2, 4, 5, 1]
seen = set()

for num in nums:
    if num in seen:
        print("Duplicate:", num)
    else:
        seen.add(num)

Output

Duplicate: 2
Duplicate: 1

2. Coding Skills

Writing clean and readable code is very important. Interviewers expect proper variable names, structured logic, and fewer syntax mistakes.

Good coding style includes:

Meaningful variable names
Proper indentation
Error handling
Optimized logic

Even small mistakes are acceptable if the candidate explains their logic clearly.

3. Computer Science Fundamentals

Strong knowledge of data structures and algorithms is extremely useful in interviews. Concepts like arrays, linked lists, stacks, queues, trees, graphs, sorting, and searching are commonly asked.

For example, Binary Search is a popular interview topic because it demonstrates understanding of efficient searching techniques.

f(n)=log2n

Binary Search reduces the search space repeatedly, making it much faster than linear search.

Important Data Structures and Algorithms Topics for Coding Interviews

4. Experience and Projects

Interviewers also evaluate the projects candidates have worked on. Real-world projects demonstrate practical knowledge and problem-solving ability.

For example, developing a Doctor Appointment Booking System with features like:

Appointment scheduling
Patient management
Database handling
Authentication systems

shows strong practical exposure to development concepts.

5. Communication Skills and Culture Fit

Candidates who explain their ideas clearly usually perform better. Interviewers prefer people who can collaborate well with teams and communicate effectively.

During interviews:

Think aloud
Explain your approach
Discuss alternative solutions
Ask clarifying questions when necessary

Good communication often creates a positive impression.

Why Do Companies Use Coding Interviews?

Many candidates question why companies focus heavily on algorithms and whiteboard coding. There are several reasons behind this approach.

Problem-Solving Ability

Companies believe that candidates who solve difficult algorithmic problems are usually strong logical thinkers. Smart problem solvers often adapt quickly to new technologies and challenges.

Understanding Fundamentals

Basic knowledge of computer science concepts helps developers choose better solutions in real-world applications. For example, knowing when to use a hash map or binary search tree can improve software performance significantly.

Whiteboard Coding Encourages Discussion

Although whiteboard coding feels artificial, it helps interviewers understand how candidates think. It also encourages communication and explanation rather than relying completely on syntax or IDE suggestions.

Important Reality About Interviews

One important thing to understand is that interview evaluation is relative. Interviewers compare your performance with other candidates who solved the same question.

Getting a difficult question does not mean failure. If a problem is hard for you, it is likely difficult for everyone else too.

Interviewers mainly observe:

Your thinking process
Problem-solving approach
Optimization ability
Communication skills
Confidence under pressure

Coding interviews are not perfect, but they remain one of the most common methods companies use to evaluate software engineers. Success in these interviews depends not only on technical knowledge but also on communication, confidence, and structured thinking.
Consistent practice, strong fundamentals, and real project experience can greatly improve interview performance. Instead of fearing coding interviews, candidates should treat them as opportunities to demonstrate their problem-solving abilities and technical skills.

*Reference *

Strings and Loops in Python

Seenivasan A — Fri, 22 May 2026 12:58:30 +0000

String

Strings are sequence of characters written inside quotes. It can include letters, numbers, symbols and spaces. Python does not have a separate character type.
Strings are immutable, meaning their values cannot be changed after creation. Any modification to a string creates a new string instead of altering the original one.

Common String Methods

Python string methods is a collection of in-built Python functions that operates on strings.

upper()
lower()
find()
split()
strip()
format()
join()

Discussed problems

Reverse a String
Password Authentication
Parse csv "java","1","2","3".."6"
Convert uppercase to lowercase

Examples

Parse csv "java","1","2","3".."6"

data = input("Enter CSV data: ")
values = data.split(",")
total = 0
for i in values:
    i = i.strip('"')
    if i.isdigit():
        total += int(i)

print("Total =", total)

Input-"java","1","2","3","4","5","6"

Output-Total = 21

Loop

Loops are used to execute a block of code repeatedly until a condition is met or all items in a sequence are processed. The main types are For loops (iterating over sequences) and While loops (executing code based on a condition).
break is used to exit a for loop or a while loop, whereas continue is used to skip the current block, and return to the "for" or "while" statement.

While Loop Syntax

while condition:
statement

For Loop Syntax

for variable in range(start, stop, step):
statement

Discussed problems

Sum of (N) numbers
Multiplication table
Print even numbers

Examples
Sum of (N) numbers

def sum_of_numbers(n):
    i=1
    sum=0
    while i<=n:
        sum+=i
        i+=1
    print("The sum of first",n,"numbers is:",sum)

n=int(input("Enter the number of terms: "))
sum_of_numbers(n)

Input-> N=10

Output-> Sum=55

Algorithms Discussed

Merge Sort

Binary Search And Linear Search

References
https://www.geeksforgeeks.org/dsa/merge-sort/

https://tutorialhorizon.com/algorithms/linear-search-vs-binary-search/

GitLab

Seenivasan A — Fri, 15 May 2026 17:38:57 +0000

GitLab was found by Dmitriy Zaporozhets and Valery Sizov in October 2011. It was distributed under MIT license and the stable version of GitLab is 10.4 released in January 22, 2018.

Gitlab is a service that provides remote access to Git repositories.

These additional features include managing the sharing of code between different people, bug tracking, wiki space and other tools for 'social coding'.

Git: A local tool installed on your computer to track code changes.

GitHub / Bitbucket: Remote websites used to host and share your Git code.

GitLab: A complete, all-in-one platform that hosts your code and automates your entire development process.

Why GitLab?
GitLab is a github like service that organizations can use to provide internal management of git repositories. It is a self hosted Git-repository management system that keeps the user code private and can easily deploy the changes of the code.

GitLab is a user friendly web interface layer on top of Git, which increases the speed of working with Git.

GitLab - Git Commands

$ git --version
$ git status
$ git add *
$ git push origin branch-name
$ git pull origin NAME-OF-BRANCH -u
$git checkout branch-name
$ git merge master

Authentication
Id-(@username) in the profile.
Create personal access tokens. When you use 2FA, you can use these tokens to access the GitLab API.

To Be Discuss?

GitLab provides its own Continuous Integration (CI) system for managing the projects and provides user interface along with other features of GitLab.
GitLab - SSH Key Setup

References
https://www.tutorialspoint.com/gitlab/gitlab_introduction.htm
https://suresoft.dev/knowledge-hub/continuous-integration/gitlab-ci/
https://docs.gitlab.com/administration/get_started/