Dr Emmanuel Ogungbemi

Head of Data Engineering | I help you improve your productivity with data and AI

From being a data analyst to leading the development of data pipelines, my journey in data engineering and analytics has been both challenging and rewarding.

When I began my career in data, I faced the daunting task of turning vast amounts of raw data into actionable insights.

The problem? Many organisations struggle with fragmented data and inefficient processes.

Here's how I tackled these challenges and built a successful career in data:

1. Crafted Data Solutions: Designed and implemented a Common Data Platform, ensuring seamless data integration and access.
2. Led Innovative Projects: At NATS, I contributed to the world's first AI system for air traffic control, utilising digital twinning and machine learning.
3. Developed Future Talent: Shaped data science and engineering education at Multiverse by developing comprehensive curricula for degree-level apprenticeships.
4. Consulted on High-Impact Projects: Provided AI and data science consultancy for industry giants like BP, Sword and Chaucer (now BIP UK), driving data-driven decision-making and innovation.

Now, I'd like to talk about you.

Are you aspiring to become a successful data analyst/data scientist/data engineer but need help figuring out where to start?

Here's a roadmap to guide you:

1. Master the Fundamentals:
- Learn key programming languages like Python and SQL.
- Understand data structures, algorithms, and database management.

2. Gain Practical Experience:
- Work on real-world projects to apply your skills.
- Use tools like Power BI and Tableau for data visualisation.

3. Join the Community:
- Participate in data science forums and attend industry events.
- Network with professionals to exchange knowledge and opportunities.

4. Continuous Learning:
- Stay updated with the latest trends and technologies in data analytics.
- Take advanced courses to deepen your expertise.

To help you get started, I've created a comprehensive Data Analyst Roadmap that outlines the essential steps and resources you need to succeed.

Join my email list to register your interest in my free Data Analyst Roadmap.

You have the potential to excel in the data industry, and I'm here to support your journey.

Let's connect and share our experiences in data engineering and analytics!

Head of Data Engineering

Excited to share an essential resource: Linear Regression!

This comprehensive guide delves into the fundamentals and advanced techniques of linear regression. 

Perfect for students, data scientists, and anyone interested in understanding this crucial statistical method, this document provides detailed explanations and practical examples.

Key topics include:
- Introduction to Linear Regression: Understanding the basics and importance of linear regression in data analysis.
- Mathematical Foundations: A detailed explanation of the linear regression model, including equations and assumptions.
- Data Visualization: The Importance of Visualizing Data and Interpreting Linear Regression Results.
- Model Evaluation: Techniques for evaluating the performance of linear regression models.
- Practical Applications: Real-world examples and applications of linear regression in various fields.


Enhance your understanding of linear regression and apply this knowledge to your data analysis projects with this detailed guide!

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The award of this certificate is in recognition of my participation in the SEEP 2019 conference held between 18 - 21 November 2019 at the University of Sharjah, United Arab Emirates where I delivered two (2) papers.

Certificate of Participation

The certificate is a result of my participation in the 5th International Conference on Material Science & Smart Materials where I presented a poster titled "Characterisation of Solar Materials and Electronic Devices using Cadmium Telluride (CdTe) and Zinc Sulphite (ZnS) as a Case Studies"

Certificate

The certificate is a result of my participation in the SEEP 2018 conference where I presented a paper titled "Effect of Membrane Thickness on the Performance of PEM Fuel Cell"

Certificate of National Service

I received this letter due to my invaluable contribution to the successful delivery of the first historic comprehensive training programme for the Volunteer Cadets in Nigeria, the OYES Volunteer Cadets' Training Course.  

Letter of Appreciation

The certificate is a recognition of my service to the club as the Vice-President (Business) in 2010.

Certificate of Fellowship

The award is a recognition of my service to the student society as General Secretary - 2007/2008, Vice-President - 2008/2009, President - 2009/2010 and National Vice-President (West) - 2010/2011.

Certificate of Service

This certificate is due to my participation in the 40th annual general meeting and conference of the society.

Certificate of Attendance

I received this certificate as a result of my participation in the international essay competition organised by the World Bank. The title is "How does youth unemployment affect you? How can you tackle youth unemployment through youth-led solutions"

Certificate of Merit

Marketing: Copywriting for Social Media

Developing CI/CD Solutions with Azure DevOps

Python Projects

Microsoft Certified: Azure Data Engineer Associate

Microsoft Certified: Azure AI Fundamentals

Microsoft Certified: Azure Data Fundamentals

Microsoft Certified: Azure Fundamentals

Data Engineering Foundations

[PCAP-31-03] PCAP – Certified Associate in Python Programming

Agile at Work: Building Your Agile Team

Scrum: The Basics

Stay Lean with Kanban

Machine Learning Essentials for Seismic Interpretation

Starting a Memorable Conversation

Artificial Intelligence Foundations: Neural Networks

Diversity, Inclusion, and Belonging

Machine Learning and AI Foundations: Classification Modeling

Body Language for Leaders

Industrial/Oilfield Health, Safety & Environmental Management Appreciation

Introduction to Auditing and Evaluating Behaviour Change (IEMA approved training)

International Association of Engineers

Institution of Chemical Engineers (IChemE)

The Institution of Engineering and Technology

I help you break into data science and AI with practical tips, real-world insights, and the latest trends.

Emmanuel is dedicated to helping you break into data science and AI with practical tips, real-world insights, and the latest trends.

Emmanuel Ogungbemi | Substack

We are delighted to be back for the second edition of ML Club Live!

This event is all about getting hands-on with GenAI, with some coding examples and also some discussions of interesting papers.

As always, this is community-driven, so if you want to come and speak about anything related to ML or AI, even for 10 minutes, please reach out to Emmanuel (bunmieogungbemi@gmail.com) or Andy (andrewpmcmahon629@gmail.com).

ML Club - July 2024 - Hands on with GenAI, Tue, Jul 2, 2024, 5:30 PM | Meetup

I coordinate Real Projects Talk for AFBE-UK Scotland

Organising Data Science and Engineering events
- To help newbies get into data science
- Organise boot camps to support learning and development
- Support working professionals by providing mentorship and advice
- https://www.meetup.com/ml-club/

Linear Regression

Excited to share an essential resource: Python Machine Learning!

This comprehensive guide delves into the fundamentals and advanced techniques of machine learning using Python. 

Perfect for students, data scientists, and anyone looking to enhance their understanding of machine learning, this document provides detailed explanations and practical examples.

Key topics include:
- Getting Started with Python Machine Learning: Introduction to machine learning and Python, essential libraries, and the basics of data handling.
- Classification with Real-world Examples: Using the Iris dataset, visualization techniques, building and evaluating classification models, and cross-validation.
- Clustering: Finding related posts, preprocessing text data, using bag-of-words and KMeans clustering.
- Topic Modeling: Latent Dirichlet Allocation (LDA), building and evaluating topic models, and applying them to real-world datasets.
- Regression and Recommendations: Building recommendation systems, using regression models, and improving recommendations with advanced techniques.
- Computer Vision and Pattern Recognition: Introduction to image processing, pattern recognition, and building classifiers for image data.
- Dimensionality Reduction: Techniques for feature selection and extraction, principal component analysis (PCA), and other dimensionality reduction methods.
- Handling Big Data: Strategies for processing large datasets, using tools like Jug and Amazon Web Services (AWS).


Enhance your machine-learning skills and apply this knowledge to real-world projects with this detailed guide!

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Building Machine Learning Systems with Python

I am excited to share an essential resource: Exam Ref 70-761, Querying Data with Transact-SQL by Itzik Ben-Gan!

This comprehensive guide is perfect for anyone preparing for the Microsoft Exam 70-761, covering key topics in T-SQL querying and programming constructs. 

Whether you're a developer, DBA, or data analyst, this book provides the knowledge and practice you need to succeed.

Key topics include:
- Creating Transact-SQL SELECT Queries: Understanding logical query processing, filtering data, sorting data, and combining sets with set operators.
- Querying Multiple Tables Using Joins: Cross joins, inner joins, outer joins, and composite joins.
- Implementing Functions and Aggregating Data: Type conversion functions, date and time functions, character functions, CASE expressions, and aggregate functions.
- Modifying Data: Inserting, updating, deleting data, merging data, and using the OUTPUT option.
- Querying Data with Advanced T-SQL Components: Subqueries, APPLY operator, table expressions, pivoting, and window functions.
- Programming Databases Using Transact-SQL: Creating views, user-defined functions, stored procedures, error handling, transactions, and handling NULLs.


Enhance your T-SQL skills and prepare effectively for your certification with this detailed guide!

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Querying Data with Transact-SQL

Understanding the key terms in data analysis is essential for anyone working with data. 

This concise guide breaks down fundamental concepts, making it easier for you to navigate the world of data. 

Whether you're a beginner or looking to refresh your knowledge, this resource is perfect for you.

Key terms include:
- Data Profiling: Examining and summarizing data characteristics
- Data Interpretation: Making sense of data and deriving insights
- Data Manipulation: Transforming data for analysis
- Exploratory Data Analysis (EDA): Visualizing data to uncover patterns
- Data Mining: Discovering patterns in large datasets
- Data Storytelling: Conveying insights in a compelling narrative
- Data Visualization: Graphical representation for better understanding
- Data Validation: Ensuring data accuracy and reliability

Master these terms and enhance your data analysis skills!

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Data Analysis Vocabulary

Excited to share an essential resource: SQL: Comprehensive Guide and Examples!

This detailed guide covers a wide range of SQL topics, from getting started with basic queries to advanced techniques. 

Perfect for students, professionals, and anyone looking to deepen their SQL knowledge, this document provides valuable insights and practical examples.

Key topics include:
- Getting Started with SQL: Overview, Syntax, and Examples.
- ALTER TABLE: Modify columns, add/drop constraints, and alter table structure.
- AND & OR Operators: Combining conditions in SQL queries.
- Cascading Delete: Maintaining data integrity with cascading deletes.
- CASE Statements: Implementing conditional logic in queries.
- Clean Code in SQL: Best Practices for Writing Readable and Maintainable SQL Code.
- Common Table Expressions (CTEs): Simplifying complex queries with CTEs.
- CREATE DATABASE and CREATE TABLE: Setting up databases and tables.
- Data Types: Understanding different data types in SQL.
- DELETE Statements: Removing data from tables.
- JOINs: Combining data from multiple tables with various types of joins.
- Subqueries: Using subqueries in SELECT, FROM, and WHERE clauses.
- Transactions: Managing data consistency with transactions.
- Indexes: Improving query performance with indexing.
- Stored Procedures: Creating and using stored procedures.


Enhance your SQL skills and master database management with this comprehensive guide!

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Learning SQL

Excited to share an invaluable resource: Comprehensive Python Cheatsheet by Jure Šorn!

This extensive cheat sheet covers the fundamental concepts and advanced techniques of Python programming. 

Perfect for beginners and experienced developers, this document provides a detailed reference for a wide range of Python functionalities.

Key topics include:
- Collections: Lists, dictionaries, sets, tuples, ranges, and more.
- Types: Strings, regular expressions, numbers, and datetime.
- Syntax: Arguments, inline functions, closures, decorators, and classes.
- System Operations: Exit, print, input, command-line arguments, file handling, and OS commands.
- Data Handling: JSON, Pickle, CSV, SQLite, bytes, and memory views.
- Advanced Topics: Threading, operators, metaprogramming, evaluation, and coroutines.
- Libraries: Progress bars, plotting, tables, web scraping, profiling, NumPy, and more.


Enhance your Python programming skills and keep this comprehensive cheatsheet as a quick reference guide!

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Comprehensive Python Cheatsheet

I am excited to share an invaluable resource: Basic SQL!

This comprehensive guide provides a deep dive into SQL fundamentals, covering everything from data definition and constraints to basic retrieval queries and set operations. 

Perfect for students, professionals, and anyone looking to strengthen their SQL skills, this document offers detailed explanations and practical examples.

Key topics include:
- SQL Data Definition and Data Types: Creating tables, specifying data types, and understanding schemas.
- Specifying Constraints in SQL: Primary keys, unique keys, foreign keys, and check constraints.
- Basic Retrieval Queries in SQL: Using SELECT statements, WHERE clauses, and basic data retrieval techniques.
- Set Operations in SQL: UNION, EXCEPT, and INTERSECT operations.


Enhance your database management skills and master SQL with this detailed guide!

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Basic SQL

I am excited to share an essential resource: Scalable Machine Learning with Apache Spark!

This comprehensive guide is perfect for anyone looking to build and scale machine-learning models using Apache Spark. 

Covering everything from data processing pipelines to distributed hyperparameter tuning, this document provides valuable insights and practical knowledge for data scientists and engineers.

Key topics include:
- Data Processing Pipelines: Creating and managing efficient data workflows with Spark.
- Machine Learning Models: Building, Tuning, and Deploying ML Models with Spark ML.
- Model Tracking and Deployment: Using MLflow to track, version, and deploy machine learning models.
- Hyperparameter Tuning: Performing distributed hyperparameter tuning with Hyperopt.
- Scaling Inference: Scaling the inference of single-node models with Spark.


Enhance your machine learning capabilities and streamline your data engineering processes with this detailed guide!

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Scalable Machine Learning with Apache Spark

Excited to share an essential resource: SQL Fundamentals!

This comprehensive guide covers the foundational concepts of SQL, making it perfect for students, professionals, and anyone looking to deepen their understanding of SQL and database management. 

From basic queries to advanced techniques, this document provides a thorough introduction to SQL.

Key topics include:

Introduction to SQL SELECT Statements: Learn how to retrieve data from databases using basic SELECT statements.
Basic SELECT Statement Syntax: Understand the structure and components of a basic SELECT statement, including SELECT, FROM, and WHERE clauses.
Writing SQL Statements: Guidelines for constructing clear and readable SQL statements.
Selecting All Columns and Rows: Techniques for retrieving all data from tables.
Selecting Specific Columns: How to display specific columns of a table.
Column Heading Defaults and Aliases: Customizing column headings for better readability.
Arithmetic Expressions and Operator Precedence: Using arithmetic operators and understanding their precedence in expressions.
Handling Null Values: Defining and working with null values in SQL.
Concatenation and Literal Character Strings: Combining columns and adding literal strings in SQL queries.
Eliminating Duplicate Rows: Using the DISTINCT keyword to remove duplicate rows in query results.
SQL and SQL*Plus Interaction: Overview of SQL*Plus commands and their differences from SQL statements.
Displaying Table Structure: Using the DESCRIBE command to view table structures.
Enhance your SQL skills and master database management with this detailed guide!

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Writing Basic SQL Statements

I am excited to share an insightful resource: Improving Python and Spark Performance and Interoperability with Apache Arrow by Julien Le Dem and Li Jin!

This comprehensive guide delves into optimizing Python and Spark interoperability using Apache Arrow. 

Perfect for data scientists, engineers, and anyone working with big data, this document provides a deep dive into enhancing performance and efficiency in data processing.

Key topics include:

Current Limitations of PySpark UDFs: Understanding the constraints and performance issues with existing user-defined functions.
Overview of Apache Arrow: Introduction to Arrow, its goals, and benefits for in-memory columnar storage.
Performance Improvements: Real-world examples and benchmarks showcasing significant speedups in PySpark integration.
Vectorized UDFs: Leveraging pandas DataFrames for efficient data processing and reducing overhead.
Group UDFs: Techniques for improving group-based operations and data normalization.
Future Roadmap: Upcoming enhancements and support for Arrow in the Apache ecosystem.


Enhance your data processing workflows and achieve higher performance with this detailed guide on Apache Arrow and PySpark!

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Improving Python and Spark Performance and interoperabi

Excited to share an invaluable resource: Docker Tutorial by Anthony Baire!

This comprehensive tutorial covers everything you need to know about Docker, from the basics of managing containers to advanced security and ecosystem considerations. 

Whether you're new to Docker or looking to enhance your skills, this guide provides clear and detailed insights into effective containerization practices.

Key topics include:
- Introduction to Docker and its components
- Managing Docker containers: creation, interaction, and lifecycle
- Handling inputs/outputs and data volumes
- Building and managing Docker images
- Security considerations and best practices
- Exploring the Docker ecosystem and future trends


Unlock the full potential of Docker for your projects with this detailed tutorial!

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Docker Tutorial

Pre-Interview Checklist for Big Data Engineer Roles.

1. SQL Essentials:
- SELECT statements, including WHERE, ORDER BY, GROUP BY, HAVING
- Basic Joins: INNER, LEFT, RIGHT, and FULL
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries, Common Table Expressions (WITH clause)
- CASE statements, advanced JOIN techniques, and window functions (OVER, PARTITION BY, ROW_NUMBER, RANK)

2. Python Programming:
- Basic syntax, control structures, data structures (lists, dictionaries)
- Pandas & NumPy for data manipulation: DataFrames, Series, groupby

3. Hadoop Ecosystem Proficiency:
- Understanding HDFS architecture, replication, and block management.
- Mastery of MapReduce for distributed data processing.
- Familiarity with YARN for resource management and job scheduling.

4. Hive Skills:
- Writing efficient HiveQL queries for data retrieval and manipulation.
- Optimizing table performance with partitioning and bucketing.
- Working with ORC, Parquet, and Avro file formats.

5. Apache Spark:
- Spark architecture
- RDD, DataFrame, Datasets, and Spark SQL
- Spark optimization techniques
- Spark Streaming

6. Apache HBase:
- Designing effective row keys and understanding HBase’s data model.
- Performing CRUD operations and integrating HBase with other big data tools.

7. Apache Kafka:
- Deep understanding of Kafka architecture, including producers, consumers, and brokers.
- Implementing reliable message queuing systems and managing data streams.
- Integrating Kafka with ETL pipelines.

8.Apache Airflow:
- Designing and managing DAGs for workflow scheduling.
- Handling task dependencies and monitoring workflow execution.

9. Data Warehousing and Data Modeling:
- Concepts of OLAP vs. OLTP
- Star and Snowflake schema designs
- ETL processes: Extract, Transform, and load
- Data lake vs. data warehouse
- Balancing normalization and denormalization in data models.

10. Cloud Computing for Data Engineering:
- Benefits of cloud services (AWS, Azure, and Google Cloud)
- Data storage solutions: S3, Azure Blob Storage, and Google Cloud Storage
- Cloud-based data analytics tools: BigQuery, Redshift, and Snowflake
- Cost management and optimization strategies

If I had to start learning #dataengineering all over again

(I will follow this plan, mostly in order)

1. Learn SQL
— Aggregations with GROUP BY
— Joins (INNER, LEFT, FULL OUTER)
— Window functions and Common table expressions

2. Learn about data modeling
— read about data normalization and 3rd normal form
— read about fact, dimension, and aggregate tables
— read about efficient table designs such as cumulative

3. Learn Python
— Learn loops, if statements
— Try out some really solid libraries like Pandas, numpy, scikit-learn
— Will not waste time on Machine Learning

4. Learn about data quality
— how do you write a good data check?
— how can you implement the write-audit-publish pattern in your pipelines?

5. Learn about distributed compute, and job orchestration
— Read about MapReduce 
— learn about partitioning, skew, and spilling to disk
— try a scheduler like Airflow

If you follow this, you’ll feel way more confident in interviews
#DataEngineering

Understanding Git: Your Ultimate Version Control System 🚀

Ever wondered what makes Git so essential?

Let’s break it down for you:

1/ What is Git?
↳ Git is a version control system that tracks code changes.
↳ It’s a tool used by developers to collaborate on projects.
↳ With Git, you can revert to previous versions of your project anytime.

2/ Git vs. GitHub
↳ Git is NOT the same as GitHub.
↳ GitHub is a hosting service that makes use of Git.
↳ GitHub allows developers to collaborate globally.

3/ Why Git Matters
↳ Over 70% of developers use Git!
↳ It tracks every change made to your project.
↳ It allows multiple people to work on the same project from anywhere.

4/ Key Git Features
↳ Every time a file is changed, Git tracks it.
↳ You can see the entire history of changes (commits).
↳ Git allows you to revert back to earlier versions easily.

5/ Git Branching
↳ Create separate branches to work on different features.
↳ Merge branches when the feature is complete.
↳ Switch between branches without disrupting the main project.

6/ Git Commands to Know
↳ $ git init: Initializes a new Git repository.
↳ $ git add <filename>: Stages files for commit.
↳ $ git commit -m “message”: Commits changes with a message.

7/ Pushing to GitHub
↳ After committing changes locally, push them to GitHub.
↳ $ git push origin master: Sends your code to the master branch on GitHub.
↳ GitHub hosts your project so others can contribute or review it.

Git makes collaboration easier and code management more efficient. 
- Mastering Git is essential for every developer!
________________________________________
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🔔 Follow Emmanuel Ogungbemi for more insights on Git and development tools.

25 Most Common Interview Questions for Data Analyst Positions 🚀

I will hire you if you can answer these questions.

Here are 25 key questions to help you prepare:

Fundamentals:
1.	What is data analytics, and why is it important for businesses?
2.	Explain the difference between structured and unstructured data.
3.	What are the key steps in the data analysis process?
4.	How do you handle missing or corrupted data in a dataset?
5.	What are the key differences between quantitative and qualitative data?

Data Tools & Techniques:
6.	What is the role of SQL in data analysis?
7.	What are some key Excel functions used in data analysis?
8.	Explain the use of JOINs in SQL.
9.	What is data normalization in databases, and why is it important?
10.	How would you explain the concept of a relational database to someone new?

Data Visualization:
11.	Why is data visualization important in data analysis?
↳ Helps present complex data clearly to stakeholders.
12.	What are some popular data visualization tools?
↳ Mention Tableau, Power BI, and Excel as key tools.
13.	When would you use a bar chart vs. a line chart?
↳ Bar charts for comparing categories, line charts for trends over time.
14.	What are some best practices for creating a dashboard?
↳ Focus on clarity, simplicity, and relevance to the audience.
15.	Explain the use of heatmaps in data visualization.
↳ Heatmaps represent data using colour gradients to show patterns or density.

Statistical Concepts:
16.	What is the difference between correlation and causation?
↳ Correlation shows a relationship, while causation implies one variable causes the other.
17.	Explain the concept of standard deviation.
↳ Measures the amount of variation or dispersion in a dataset.
18.	What is the p-value in hypothesis testing?
↳ The p-value helps determine the significance of your results.
19.	How do you determine if a dataset is normally distributed?
↳ Use histograms, Q-Q plots, and skewness/kurtosis measures.
20.	What is A/B testing, and how is it used in decision-making?

Practical Implementation:
21.	How do you clean and prepare data for analysis?
↳ Explain steps like handling missing data, removing duplicates, and outlier treatment.
22.	What are the challenges of working with large datasets?
↳ Mention issues like performance, storage, and data quality.
23.	How do you handle outliers in your data?
↳ Depending on the context, you can remove, cap, or transform outliers.
24.	Explain a situation where you identified a problem and solved it using data.
↳ Be prepared to walk through a real-life example with clear steps.
25.	How do you ensure data accuracy and integrity throughout your analysis?
↳ Double-check data entry, validate sources and perform consistency checks.

________________________________________

Found these questions helpful?
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💬 Comment

So more data analysts can prepare for interviews!

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Good luck with the interviews! 💼

You’ve been missing out! 

See What Every Data Expert Knows But Isn't Telling You!

You need to get a team of specialists and make sure the entire data life cycle is covered

- Data Engineers: Are you just storing data, or are you transforming it?
- Analytics Engineers: Is your data modelling just busy work, or are you gaining breakthrough insights?
- Data Analysts: Are your reports ending up in a drawer, or are they driving real action?

Each role is a critical piece of the puzzle in the data-to-decision process. 

Understanding this can dramatically boost your career and business strategy!

🌟 Don’t let your competitors get ahead! 

Is your team aligned with these best practices?

cc: https://rpst.in/lBuOU

Pyspark can be HARD

But learning these concepts made it easier for you.

𝟭. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗣𝘆𝘀𝗽𝗮𝗿𝗸:
- Spark Architecture & Apache Spark Overview
- Setting up the PySpark Environment
- SparkContext and SparkSession

𝟮. 𝗥𝗗𝗗𝘀 (𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀):
- Creating RDDs
- Transformations and Actions (Narrow vs Wide)
- RDD Operations (map, filter, reduce, etc.)

𝟯. 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗔𝗣𝗜𝘀:
- DataFrame operations (select, filter, groupBy, etc.)
- Reading, writing data & inferring schemas
- Spark File Layout
- How to refer columns in DF and DS

𝟰. 𝗦𝗽𝗮𝗿𝗸 𝗦𝗤𝗟 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲𝘀 :
- Running SQL Queries on DataFrames
- Registering DataFrames as Temporary Tables or Views
- SQL Functions and UDFs
- Working with various formats (CSV, JSON, Parquet, Avro)
- Hive Tables and External Databases (JDBC)

𝟱. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀:
- Broadcast and Accumulator Variables
- Caching, Persistence, and Memory Management
- Data Skew Handling
- Partition Pruning and Shuffle Optimization

𝟲. 𝗣𝘆𝘀𝗽𝗮𝗿𝗸 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴:
- Structured Streaming Basics
- Windowing Operations
- Integrating with Kafka for Real-time Data Processing
- Fault Tolerance and Checkpointing

Data skills will always be relevant.

So learning skills focused on data will last a lifetime.

Here are 3 career options to consider in data.

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁:
- SQL
- Python
- Excel
- Power BI / Tableau
- Statistical Analysis
- Data Warehousing

𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴:
- SQL
- Python
- Hadoop
- Hive
- HBase
- Kafka
- Airflow
- PySpark
- CI/CD
- Data Warehousing
- Data modeling
- AWS/Azure/GCP

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁:
- SQL
- Python/R
- Artificial intelligence
- Statistics & Probability
- Machine Learning
- Deep Learning
- Data Wrangling
- Mathematics (Linear Algebra, Calculus)

If you've read so far, do SHARE the post.

An aspiring Data Engineer aspirant asked me a question.

Why is PySpark so important to learn?

I showed them this list:

- PySpark + DataFrame API = Data Manipulation
- PySpark + RDD = Distributed Datasets
- PySpark + filter() = Data Filtering
- PySpark + join() = Data Integration
- PySpark + groupBy() = Data Aggregation
- PySpark + orderBy() = Data Sorting
- PySpark + union() = Combining Datasets
- PySpark + withColumn() = Data Transformation
- PySpark + select() = Column Selection
- PySpark + SQL Queries = SQL Integration
- PySpark + createOrReplaceTempView() = Virtual Tables
- PySpark + map() = Data Mapping
- PySpark + reduceByKey() = Data Reduction
- PySpark + partitionBy() = Data Partitioning
- PySpark + broadcast() = Data Broadcasting
- PySpark + accumulators = Shared Variables
- PySpark + Spark SQL = Structured Data
- PySpark + DataFrame Caching = Performance Optimization
- PySpark + Window Functions = Advanced Analytics
- PySpark + UDFs = Custom Functions
- PySpark + Machine Learning = Scalable Models
- PySpark + GraphX = Graph Processing
- PySpark + Streaming = Real-Time Processing
- PySpark + DataFrame Joins = Efficient Merging
- PySpark + MLlib = Machine Learning
- PySpark + Structured Streaming = Continuous Processing
- PySpark + Pipeline API = Workflow Automation
- PySpark + Delta Lake = Reliable Lakes
- PySpark + Databricks = Cloud Platform
- PySpark + ETL Pipelines = Data Extraction
- PySpark + Performance Tuning = Query Efficiency
- PySpark + Cluster Management = Distributed Computing

With consistency and effort, you will get there.

LIKE and SHARE, if you can relate to this post.

You're STILL a data scientist, even if...

- You prefer Excel over Pandas for quick data checks.
- Statistics formulas feel like a foreign language sometimes.
- Your machine learning model didn’t perform as expected.
- You rely on Google for the syntax of a common Python function.
- You forgot how to interpret a p-value during a meeting.
- Your Jupyter notebook is a mess of commented-out code.
- You still find deep learning architectures intimidating.
- You’re NOT a data scientist when...

You decide to quit.
SO DON'T QUIT! KEEP PUSHING FORWARD! 

💡 Repost to inspire others on their data science journey!

LinkedIn Growth Hacker✌️ ||Believe in Smart work💥 || Content Creator🖋️|| Believe in Opportunities💫|| Adding a little spark✨

LinkedIn Growth Hacker | Brand Builder | Marketer | ML Engineer | Public Speaker | Featured at Times Square

Most LinkedIn profiles get views. Few get growth.

I help creators, professionals, and brands turn their LinkedIn presence into a growth engine, attracting visibility, opportunities, and authority.

Many people have incredible skills or products, but their profiles don’t reflect their value. I change that.

I’ve scaled my own LinkedIn to 130,000+ followers, not with luck, trends, or gimmicks, but with a proven system of growth-focused content, strategic branding, and audience-first engagement.

Now, I help others do the same. My focus is on amplifying your authority, building a strong personal brand, and growing your audience, so you become the go-to name in your niche.

What I do:

✅ LinkedIn Growth Strategy 
✅ Content Creation & Branding 
✅ Brand Collaborations & Campaigns 
✅ Profile Optimization

If you want to scale your LinkedIn, grow your influence, and turn your brand into a visible authority, let’s connect.

➡️ DM me or email: v.toshniwal2828@gmail.com

Always open to meaningful conversations, collaborations, and opportunities in AI, ML, and tech-driven products.

Currently seeking Tech / Machine Learning Engineer opportunities.

Vidhi Toshniwal

I used to type everywhere.

On the bus.
At the cafe.
Even in random co-working spots.

My brain would move faster than my fingers. Mistakes. Backspaces. Endless edits.

Then I tried something different.

I started speaking instead of typing.

With Wispr Flow, everything I say turns into perfectly formatted text, emails, Slack messages, notes, even code. It works across every app I use.

➡️ 500ms latency
➡️ 85% zero-edit rate
➡️ Emoji commands & whisper mode
➡️ Works in 70+ apps

72% of my work today? Voice-only. My keyboard barely gets touched anymore.

And now, Android users can finally join in. Same speed, same accuracy, same magic.

Work anywhere. Speak naturally. Get more done.

My keyboard? Officially retired.

Try it yourself and see how much faster you can get things done.

https://wisprflow.ai

Wispr Flow

Wispr Flow 

My developer quoted me ₹5,00,000
for a simple internal app.

All I wanted was:

➡️ Track my clients’ LinkedIn post performance
➡️ See engagement data clearly
➡️ Monitor growth trends
➡️ Generate clean reports

Nothing complex.
Just clarity.

₹5 lakh.

For a minute, I convinced myself…
“Maybe that’s the cost of building software.”

Then, while scrolling LinkedIn, I discovered Replit.

I decided to try it.

That decision saved me ₹5,00,000.

No servers.

I described what I wanted.

And I had a working, full-stack app.

Not a mockup.
Not slides.

A real app with logic, data storage, authentication, ready to use.

That’s when I understood something powerful:

Replit isn’t just for experimenting.

It supports the full journey:

👉 Start — Turn an idea into something real, fast.
👉 Build — Everything in one place.
👉 Scale — Production-ready, secure, built to grow.

Biggest realization?

We don’t always need a full tech team to validate an idea.
Sometimes we just need the right platform.

₹5 lakh saved.
One idea shipped.

If you’re sitting on an idea because “it’s too expensive”,

Maybe it’s not.

Maybe you just haven’t started yet.

➡️ https://lnkd.in/gg9cPEwR

P.S: I have already saved 5 Lakh Rs.  
Now it's your turn 😎

Hello LinkiFam 👋
This side Vidhi,Thanks for visiting my profile .
Currently pursuing 12th grade and preparing for IIT-JEE entrance exam📖 .
I believe, learning with experience that last forever so I help every deserving person to get there where they ought to be🔥.
Technology 👩‍💻excites me and I'm always in the awe of the change it drives in world🌍

China just dropped the game changer in AI

Kimi is now the #1 most-used model on OpenClaw (via OpenRouter)
and the overall #1 on OpenRouter.

It has Claude Opus4.6 performance,
but 8x cheaper!!!

People using Kimi on OpenClaw are already saving hundreds of dollars!
This isn’t hype. Builders don’t follow trends, they follow results.

To support this growth, Kimi Code has gone token-based:

➡️ Pay for actual usage
➡️ Transparent scaling
➡️ No bloated subscriptions

Getting started is simple:

1️⃣ Go to kimi.com/code, pick a plan, grab your API key
2️⃣ Login to openclaw.ai, install, paste your key
3️⃣ Keep the default model kimi-coding/k2p5
4️⃣ Choose hooks (markdown, command logging, context continuity) if you want

Restart your gateway, hatch your bot, and you’re live.

Builders don’t wait. They build.

And the community has already made their choice:
Kimi ✖️ OpenClaw.

Try Kimi x OpenClaw before everyone catches on: http://kimi.com

Happy building 🚀

Kimi K2.5

A mentor once told me,

“Invest in yourself early. The returns compound for life.”

At that time, I didn’t fully understand it.
Now I do.

We spend years working hard…
But hesitate when it comes to upgrading our own skills.

Here’s the truth:

𝙊𝙥𝙥𝙤𝙧𝙩𝙪𝙣𝙞𝙩𝙞𝙚𝙨 𝙙𝙤𝙣’𝙩 𝙬𝙖𝙞𝙩.
𝙎𝙠𝙞𝙡𝙡𝙨 𝙘𝙧𝙚𝙖𝙩𝙚 𝙩𝙝𝙚𝙢.

This Holi season, instead of just adding color to your clothes…
Add value to your career. 🎨

Right now, Coursera Plus Annual is available at ₹7,499 (instead of ₹13,999) until 9th March.

Access to 10,000+ programs.
Top universities.
Industry experts.
One subscription.

We always say “I’ll start learning soon.”

Soon becomes next month.
Next month becomes next year.

And suddenly we’re behind.

Building your future is important.
But building the right skills is what makes that future powerful.

This could be the smartest ₹7,499 you invest this year.

If growth is on your mind, don’t ignore this.

Here’s the link:
https://lnkd.in/gZ9P499A

Let’s upgrade ourselves 🚀

Google just reset the AI career ladder.

One certificate.
Built for real work.

𝗧𝗵𝗶𝘀 𝗶𝘀𝗻’𝘁 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗔𝗜 𝗰𝗼𝘂𝗿𝘀𝗲.
𝗜𝘁’𝘀 𝗮 𝗰𝗮𝗿𝗲𝗲𝗿 𝘀𝗮𝗳𝗲𝘁𝘆 𝗻𝗲𝘁.

That gap?
It’s widening every single month.

𝗧𝗛𝗜𝗦 𝗜𝗦 𝗧𝗛𝗘 𝗗𝗜𝗙𝗙𝗘𝗥𝗘𝗡𝗖𝗘.

𝗚𝗢𝗢𝗚𝗟𝗘 𝗔𝗜 𝗣𝗥𝗢𝗙𝗘𝗦𝗦𝗜𝗢𝗡𝗔𝗟 𝗖𝗘𝗥𝗧𝗜𝗙𝗜𝗖𝗔𝗧𝗘

➡️ 6 short, practical courses
➡️ 20+ hands-on labs
➡️ A final project where you build a real AI solution

You’ll work with tools used in real jobs:

– Google AI tools
– Gemini
– NotebookLM
– AI Studio

Why this matters (a lot):
AI isn’t “nice to have” anymore.
It’s becoming baseline skill expectation.

This certificate:

✓ Is built from real job-market data
✓ Teaches how to apply AI, not just talk about it
✓ Ends with a Google-backed certificate employers actually trust

If you’re a professional in 2026 and
AI is still “optional” for you, the market has already decided otherwise.

Learn how to build with AI, not compete against it.

👉 Start here: https://lnkd.in/gRUcR9QP

AI won’t take your job.
But someone who knows how to use it might.

#ai

I kept seeing people talk about AI agents everywhere, but I didn’t really get the hype.

Then I watched Abacus AI Deep Agent build an app in a few minutes.

Frontend, backend, database, logic, it just put everything together while I was watching.

That’s when it finally made sense why people are so excited about this.

👉 http://deepagent.abacus.ai

Here’s what stood out while using it:

➡️ End-to-end apps from one prompt

You describe what you want, and it sets up the UI, backend, database, and forms.
It also does mobile apps now -  just explain the idea, and it builds something you can publish.

➡️ Websites and SaaS in one flow
Landing pages, logins, payments, and data storage are all handled together.

➡️ Complex products from simple explanations
You explain how it should work, and DeepAgent figures out the structure.

➡️ These are things people would actually use

CRMs, internal tools, Notion-style systems, and HR software.

➡️ Micro-SaaS feels a lot more doable

Small tools and niche ideas don’t feel like huge projects anymore.

➡️ Apps and games show up quickly
Sudoku, simulations, and interactive tools are ready to use.

➡️ AI tools built for specific roles

Founder helpers, research tools, and domain-specific apps.

➡️ Reports, decks, and research

Charts, visuals, and structured info without extra setup.

➡️ Spreadsheets turned into dashboards

Upload a file and get metrics and explanations right away.

➡️ ChatLLM is built in

So text, PDFs, images, videos, and code all live in one place.

For founders, this makes trying ideas a lot faster.
For teams, it changes how work gets split up.

Definitely worth keeping an eye on.
👉 Abacus AI Deep Agent
http://deepagent.abacus.ai

I thought people were exaggerating when they said “AI agents change everything.”

Then I watched one quietly build an app from scratch.

In a few minutes, it had the frontend, backend, database, and logic in place.
Nothing fancy on the surface - it just… worked.

This is Abacus AI Deep Agent 
An AI agent that handles execution the way a small engineering team would.

👉 http://deepagent.abacus.ai

Here’s what really stood out:

➡️ FULL-STACK APPS FROM ONE PROMPT
UI, backend, databases, and forms created together in one flow.
And now mobile apps too. Describe what you want, and Deep Agent builds a full app you can publish.

➡️ LAUNCH AN ONLINE STARTUP IN MINUTES
Landing pages, login systems, payments, and data storage are handled together instead of pieced apart.

➡️ COMPLEX SOFTWARE & SAAS USING SIMPLE TEXT
You explain how it should look and work.
Deep Agent figures out the structure and builds it.

CRMs, internal tools, Notion-style systems, HR software built for real usage.

➡️ MICRO-SAAS IDEAS THAT FEEL POSSIBLE
Resume tools, niche utilities, and lead magnets ideas that used to feel heavy now feel worth trying.

➡️ APPS & GAMES GENERATED QUICKLY
Sudoku, Minecraft-style games, Conway’s Game of Life, and interactive tools - ready to use as soon as they’re created.

➡️ BUILD YOUR OWN CHATGPT-STYLE APPS
Founder assistants, workout planners, diet tools, research helpers, domain-specific AI tools.

➡️ REPORTS, DECKS & PRESENTATIONS
Charts, visuals, and structured insights put together in minutes.

➡️ SPREADSHEETS → INTERACTIVE DASHBOARDS
Upload Excel or CSV files and get metrics, explanations, and visuals automatically.

➡️ IMAGE & VIDEO GENERATION BUILT IN
Work with text, PDFs, images, videos, and code in the same place.

➡️ TURN RESEARCH PAPERS INTO AUDIO
Deep Agent turns long papers into audio conversations you can actually listen to.

➡️ DEEP AGENT BROWSER EXTENSION
Describe a workflow, and it executes each step automatically with no manual setup.

For founders, this changes how fast ideas can be tested.
For teams, it changes how work gets distributed.

Worth keeping an eye on.
👉 Abacus AI Deep Agent
http://deepagent.abacus.ai

Cancel your AI subscriptions.

I know that sounds dramatic. But I actually did it after 2 years of paying for almost every AI tool out there.

I was opening ChatGPT for writing, research, and summaries.
Gemini for images.
Kling for videos.
Another tool for vibe-coding apps.
Another one for presentations.

Nothing was wrong with the tools. The problem was fragmentation.

Then I moved everything into ChatLLM by Abacus hi AI. - 

One place for all major AI models
GPT-5.2, Claude 4.6, Gemini 3 Pro, Grok 4.1 Fast, DeepSeek, LLaMA, same workspace, same context.

Images and videos without switching products
Nano Banana Pro, FLUX, Seedream, Ideogram for images.
Sora, VEO, Kling, Runway, Luma, Grok Imagine for videos.

Full-stack apps, websites, and SaaS from prompts
Type an idea and see it live. Layout, colors, copy, and logic all editable.

Presentations that don’t feel empty
Simple inputs turn into structured decks with research, charts, and flow.

An assistant who keeps working in the background
Tasks, searches, and workflows progress even when I’m not active.

PDFs become interactive
Upload documents, ask questions, and extract notes instead of reading line by line.

AI text that sounds human
Generate, humanize, choose a tone, and use it as-is.

Real-time web research
Answers come with sources, visuals, and context.

Deep Agent for real outputs
Apps, reports, dashboards, workflows, micro-SaaS.

A code editor that actually supports building
Write, run, debug, and improve without chaos.
At some point, it stopped feeling like using tools.
It felt like working inside one system.

That’s why I cancelled everything else.  Try it now, it’s only $10 a month - http://chatllm.abacus.ai

Building a 24/7 financial assistant used to sound like something only Wall Street firms could afford.

Now anyone can do it in minutes.

I recently built a simple Autonomous Financial Intelligence System using Kimi Claw.

Just a smart AI agent that:

➡️ Tracks financial data
➡️ Generates automated reports
➡️ Sends insights on schedule
➡️ Remembers preferences over time

Instead of constantly checking dashboards and market updates… the system brings insights to me.

A few things I love about it:

✓ Runs 24/7 in the cloud
✓ Generates weekly and daily financial reports
✓ Uses thousands of built-in skills for analysis
✓ Stores data and past insights with persistent memory

The carousel breaks down the exact steps I used to build it with Kimi Claw.

Now it's your turn.

Do check out
➡️ https://www.kimi.com/bot

Kimi AI 

Yaar koi mujhe batao how is Manyavar so good at this??

They just dropped a Wedding Music Video with Vijay and Rashmika and I am not okay. 

The video is SO cute, the chemistry is insane, and Amit Trivedi ka music? Chef's kiss.

But also, let's talk about the big brain energy here. Manyavar didn't make an ad. They made a wedding film that people actually want to watch and share. No voiceover or "buy now." Just a beautiful story about a wedding that almost  just happened.

They literally created a cultural moment.

The timing is also wild. When Vijay and Rashmika are the hottest topic on the internet, Manyavar already had this in the bag. That's not coincidence, that's someone on their team who really understands the pulse.

Bohot din baad kuch aisa dekha jisme brand feel hi nahi hua. 

And that's the biggest compliment you can give a marketing team.

Vedant Fashions Limited - Manyavar-Mohey 

#Manyavar #WeddingMusicVideo #VijayRashmika

Some ads don’t sell a product.
They sell a feeling. ❤️

Myntra’s Valentine’s campaign “Pyaar Baantte Chalo” did exactly that.

On the surface, it’s about gifting.
But underneath, it’s pure marketing intelligence.

They didn’t shout discounts.
They didn’t push features first.
They led with emotion, love that’s simple, thoughtful, and meant to be shared.

And then… they slipped the product in naturally.

Here’s the smart part:
Valentine’s Day comes with pressure.
Forgot the gift? Running late? Panic mode.

Myntra M-Now quietly solves that anxiety with 30-minute delivery.

No urgency in the messaging.
Just reassurance.

That’s powerful marketing.

Because great ads don’t say:
“Buy this now.”

They say:
“We’ve got you. Go spread some love.”

Fashion became the language.
Speed became the support.
And love stayed at the center.

This Valentine’s, it’s not about how expensive the gift is.
It’s about the feeling behind it.

This is how brands win hearts ❤️

#myntra

When I was younger, I used to get irritated when my nani asked too many questions.

“How does this work?”
“Who decides this?”
“What happens if it fails?”

At the time, it felt unnecessary.
Today, it feels like wisdom.

Every AI system I’ve seen break in production broke for the same reason:
no one truly understood it.

It worked in demos.
It impressed in meetings.
But when something went wrong, there were no clear answers, only guesses.

The strongest teams I’ve worked with build differently.

They slow down first.
They analyze intent before writing logic.
They design how agents interact, not just what they output.
They build systems they can explain without slides.

That approach is exactly what Architect enables.

It follows the same discipline:
analyze → orchestrate → build.

Nothing hidden.
Nothing assumed.
Just glass-box transparency from the start.

Funny how nani’s questions ended up becoming a blueprint for how AI should be built.

I’ve been exploring Architect and seeing how it applies these principles to real workflows:
https://lnkd.in/g6grt-M6

I never thought that a task as simple as booking a househelp could teach me a business lesson.

Last week, I tried Urban Company for the first time.

Service done. Payment done. I thought that was it.

Then the app asked:
“Do you want to book the same professional next time?”

A tiny question… but a genius idea.

Because in service businesses, the hardest part isn’t getting a customer once.
It’s making them come back.

Urban Company quietly solves that problem. The next time you open the app, the professional you already trust appears as a preferred option.

Just comfort and familiarity.

Most companies chase the first sale.
The smart ones? They make the second sale effortless.

Small product decisions = big retention wins.

P.S: What’s one small feature in a product or service that made you come back again and again?

I was reading an article this morning, and one thing just stopped me, the new Tata Punch.ev has been revealed at a starting price of Rs 9.69 lakh (ex-showroom). And once I went down that rabbit hole, I couldn't stop.

Now I follow car launches quite closely, and the EV space especially has been fascinating to watch. But what Tata has done with the new Punch.ev genuinely surprised me.

The safety package alone is something else. Over 60 safety features, including Hill Ascent and Descent Control, Electronic Stability Program, Hill Hold Control, ISOFIX child seat anchors, and even an SOS calling function built right in. That's not just car safety, that's peace of mind at a price most people wouldn't expect.

Then there's the connected car experience through IRA.ev, which comes standard across all variants. We're talking about real-time charge status, remote diagnostics, geo-fencing, valet mode, and alerts for everything from an open door to an abrupt charge cut-off. And if that wasn't enough, you can control the car from your smartwatch, lock, unlock, switch the AC on, and precool the cabin before you even walk to the parking lot.

On the comfort side, a voice-assisted electric sunroof, cooled glovebox, 90-degree door opening, and 366L of boot space are the kind of everyday touches that make ownership genuinely enjoyable rather than just practical.

A lifetime battery warranty on the 40 kWh pack ties it all together. Tata isn't just selling a car here; they're selling safety and confidence.

The more I read, the more I felt this isn't just a good EV. It's a genuinely good car.

A B2B fintech brand just made me stop scrolling. That doesn't happen often.

Cashfree Payments dropped "One of Your Kind" and it is nothing like what you expect from a payments company.

No product pitches. No feature talk. Just the real moments behind building BigBasket, Third Wave Coffee, RedBus, Heads Up For Tails and more.

As someone who lives and breathes brand storytelling, this is how you build emotional equity in a space everyone treats as a commodity.

Every business builds its own way. The infrastructure behind it should understand that.

This is what intentional brand building looks like.

Shoutout to Akash S. and Reeju Datta for bringing this story to life. 👏

#CashfreePayments #OneOfYourKind

For over a decade, Cashfree Payments has powered payments for 1 million+ businesses across India.

Working closely with startups, D2C, SaaS, travel and growing businesses reveals something quickly.

No two successful businesses operate the same way - each is one of a kind. 
Some brands scale through flash sales.
Some run marketplaces with multiple sellers.
Some deal with COD orders and returns.
Some start selling globally from day one.

Every business solves problems in its own way. And their payments need to work the same way.

At Cashfree Payments, we build payments around how businesses actually operate and what they need. Whether you are processing your first hundred orders or scaling to millions of transactions.

Cashfree Payments. One of Your Kind.

bigbasket | | redBus | Heads Up For Tails | Third Wave Coffee | Prashanti Sarees

#OneOfYourKind

Today’s social media scroll came with a pleasant surprise.

I came across RADHA Diamond and Gold Jewellery, and for a moment it felt like the jewellery brand KISNA diamond and Gold Jewellery had introduced a new identity.

But the idea behind it was far more meaningful and unique.

For Women’s Day, the brand chose to rename itself, placing a feminine power at the forefront as a tribute to the strength, grace and wisdom.

It’s a subtle gesture, but one that speaks volumes.

Because sometimes, the most powerful tributes simply place women where they truly belong, at the heart of it all.

#InternationalWomensDay #HonouringHer #TodayIAmRadha #KISNADiamondAndGoldJewellery #CelebratingWomanhood

taplio

The Battle

Dr Emmanuel Ogungbemi

Vidhi Toshniwal

Followers

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Performance ScoreDr Emmanuel Ogungbemi's LinkedIn Performance

Performance ScoreVidhi Toshniwal's LinkedIn Performance

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taplio

The Battle

Dr Emmanuel Ogungbemi

Vidhi Toshniwal

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Dr Emmanuel Ogungbemi's LinkedIn Performance

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Performance ScoreDr Emmanuel Ogungbemi's LinkedIn Performance