Stop obsessing over Python and SQL skills.
Here are 5 non-technical skills that make exceptional data analysts:
- Business Acumen
Understand the industry you're in. Know your company's goals, challenges, and KPIs. Your analyses should drive business decisions, not just process data.
- Storytelling
Data without context is just noise. Learn to craft compelling narratives around your insights. Use analogies, visuals, and clear language to make complex data accessible.
- Stakeholder Management
Navigate office politics and build relationships. Know how to manage expectations, handle difficult personalities, and align your work with stakeholders' priorities.
- Problem-Solving
Develop ability for identifying the real problem behind the data request. Often, the question asked isn’t the one that truly needs solving. It’s your job as a data analyst to dig deeper, challenge assumptions, and uncover the actual business challenge.
Technical skills may get you started, but it’s the soft skills that truly advance your career. These are the skills that turn a good analyst into an essential part of the team.
The best data analysts aren't just number crunchers - they guide the strategy that drives the business forward.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊
Here are 5 non-technical skills that make exceptional data analysts:
- Business Acumen
Understand the industry you're in. Know your company's goals, challenges, and KPIs. Your analyses should drive business decisions, not just process data.
- Storytelling
Data without context is just noise. Learn to craft compelling narratives around your insights. Use analogies, visuals, and clear language to make complex data accessible.
- Stakeholder Management
Navigate office politics and build relationships. Know how to manage expectations, handle difficult personalities, and align your work with stakeholders' priorities.
- Problem-Solving
Develop ability for identifying the real problem behind the data request. Often, the question asked isn’t the one that truly needs solving. It’s your job as a data analyst to dig deeper, challenge assumptions, and uncover the actual business challenge.
Technical skills may get you started, but it’s the soft skills that truly advance your career. These are the skills that turn a good analyst into an essential part of the team.
The best data analysts aren't just number crunchers - they guide the strategy that drives the business forward.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊
👏2❤1
🔥 20 Data Engineering Interview Questions
1. What is Data Engineering?
Data engineering is the design, construction, testing, and maintenance of systems that collect, manage, and convert raw data into usable information for data scientists and business analysts.
2. What are the key responsibilities of a Data Engineer?
Building and maintaining data pipelines, ETL processes, data warehousing solutions, and ensuring data quality, availability, and security.
3. What is ETL?
Extract, Transform, Load - A data integration process that extracts data from various sources, transforms it into a consistent format, and loads it into a data warehouse.
4. What is a Data Warehouse?
A central repository for storing structured, filtered data that has already been processed for a specific purpose.
5. What is a Data Lake?
A storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data.
6. What are the differences between Data Warehouse and Data Lake?
- Structure: Data Warehouse stores structured data; Data Lake stores structured, semi-structured, and unstructured data.
- Processing: Data Warehouse processes data before storage; Data Lake processes data on demand.
- Purpose: Data Warehouse for reporting and analytics; Data Lake for exploration and discovery.
7. What is a Data Pipeline?
A series of steps that move data from source systems to a destination, cleaning and transforming it along the way.
8. What are the common tools used by Data Engineers?
Hadoop, Spark, Kafka, AWS S3, AWS Glue, Azure Data Factory, Google Cloud Dataflow, SQL, Python, Scala, and various database technologies (SQL and NoSQL).
9. What is Apache Spark?
A fast, in-memory data processing engine used for large-scale data processing and analytics.
10. What is Apache Kafka?
A distributed streaming platform that enables real-time data pipelines and streaming applications.
11. What is Hadoop?
A framework for distributed storage and processing of large datasets across clusters of computers.
12. What is the difference between Batch Processing and Stream Processing?
- Batch: Processes data in bulk at scheduled intervals.
- Stream: Processes data continuously in real-time.
13. Explain the concept of schema-on-read and schema-on-write.
- Schema-on-write: Data is validated and transformed before being written into a data warehouse.
- Schema-on-read: Data is stored as is and the schema is applied when the data is read.
14. What are some popular cloud platforms for data engineering?
- Amazon Web Services (AWS)
- Microsoft Azure
- Google Cloud Platform (GCP)
15. What is an API and why is it important in Data Engineering?
Application Programming Interface - Enables different software systems to communicate and exchange data. Crucial for integrating data from various sources.
16. How do you ensure data quality in a data pipeline?
Implementing data validation rules, monitoring data for anomalies, and setting up alerting mechanisms.
17. What is data modeling?
The process of creating a visual representation of data and its relationships within a system.
18. What are some common data modeling techniques?
- Entity-Relationship (ER) modeling
- Dimensional modeling (Star Schema, Snowflake Schema)
19. Explain Star Schema and Snowflake Schema.
- Star Schema: A simple data warehouse schema with a central fact table and surrounding dimension tables.
- Snowflake Schema: An extension of the star schema where dimension tables are further normalized into sub-dimensions.
20. What are some challenges in Data Engineering?
- Handling large volumes of data
- Ensuring data quality and consistency
- Integrating data from diverse sources
- Managing data security and compliance
- Keeping up with evolving technologies
❤️ React for more Interview Resources
1. What is Data Engineering?
Data engineering is the design, construction, testing, and maintenance of systems that collect, manage, and convert raw data into usable information for data scientists and business analysts.
2. What are the key responsibilities of a Data Engineer?
Building and maintaining data pipelines, ETL processes, data warehousing solutions, and ensuring data quality, availability, and security.
3. What is ETL?
Extract, Transform, Load - A data integration process that extracts data from various sources, transforms it into a consistent format, and loads it into a data warehouse.
4. What is a Data Warehouse?
A central repository for storing structured, filtered data that has already been processed for a specific purpose.
5. What is a Data Lake?
A storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data.
6. What are the differences between Data Warehouse and Data Lake?
- Structure: Data Warehouse stores structured data; Data Lake stores structured, semi-structured, and unstructured data.
- Processing: Data Warehouse processes data before storage; Data Lake processes data on demand.
- Purpose: Data Warehouse for reporting and analytics; Data Lake for exploration and discovery.
7. What is a Data Pipeline?
A series of steps that move data from source systems to a destination, cleaning and transforming it along the way.
8. What are the common tools used by Data Engineers?
Hadoop, Spark, Kafka, AWS S3, AWS Glue, Azure Data Factory, Google Cloud Dataflow, SQL, Python, Scala, and various database technologies (SQL and NoSQL).
9. What is Apache Spark?
A fast, in-memory data processing engine used for large-scale data processing and analytics.
10. What is Apache Kafka?
A distributed streaming platform that enables real-time data pipelines and streaming applications.
11. What is Hadoop?
A framework for distributed storage and processing of large datasets across clusters of computers.
12. What is the difference between Batch Processing and Stream Processing?
- Batch: Processes data in bulk at scheduled intervals.
- Stream: Processes data continuously in real-time.
13. Explain the concept of schema-on-read and schema-on-write.
- Schema-on-write: Data is validated and transformed before being written into a data warehouse.
- Schema-on-read: Data is stored as is and the schema is applied when the data is read.
14. What are some popular cloud platforms for data engineering?
- Amazon Web Services (AWS)
- Microsoft Azure
- Google Cloud Platform (GCP)
15. What is an API and why is it important in Data Engineering?
Application Programming Interface - Enables different software systems to communicate and exchange data. Crucial for integrating data from various sources.
16. How do you ensure data quality in a data pipeline?
Implementing data validation rules, monitoring data for anomalies, and setting up alerting mechanisms.
17. What is data modeling?
The process of creating a visual representation of data and its relationships within a system.
18. What are some common data modeling techniques?
- Entity-Relationship (ER) modeling
- Dimensional modeling (Star Schema, Snowflake Schema)
19. Explain Star Schema and Snowflake Schema.
- Star Schema: A simple data warehouse schema with a central fact table and surrounding dimension tables.
- Snowflake Schema: An extension of the star schema where dimension tables are further normalized into sub-dimensions.
20. What are some challenges in Data Engineering?
- Handling large volumes of data
- Ensuring data quality and consistency
- Integrating data from diverse sources
- Managing data security and compliance
- Keeping up with evolving technologies
❤️ React for more Interview Resources
❤14
Prompt Engineering in itself does not warrant a separate job.
Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts 😅. Also a lot of these prompts don't work for any other LLMs apart from ChatGPT.
You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc.
The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.
Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts 😅. Also a lot of these prompts don't work for any other LLMs apart from ChatGPT.
You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc.
The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.
❤2👏2
📊 Data Science Summarized: The Core Pillars of Success! 🚀
✅ 1️⃣ Statistics:
The backbone of data analysis and decision-making.
Used for hypothesis testing, distributions, and drawing actionable insights.
✅ 2️⃣ Mathematics:
Critical for building models and understanding algorithms.
Focus on:
Linear Algebra
Calculus
Probability & Statistics
✅ 3️⃣ Python:
The most widely used language in data science.
Essential libraries include:
Pandas
NumPy
Scikit-Learn
TensorFlow
✅ 4️⃣ Machine Learning:
Use algorithms to uncover patterns and make predictions.
Key types:
Regression
Classification
Clustering
✅ 5️⃣ Domain Knowledge:
Context matters.
Understand your industry to build relevant, useful, and accurate models.
✅ 1️⃣ Statistics:
The backbone of data analysis and decision-making.
Used for hypothesis testing, distributions, and drawing actionable insights.
✅ 2️⃣ Mathematics:
Critical for building models and understanding algorithms.
Focus on:
Linear Algebra
Calculus
Probability & Statistics
✅ 3️⃣ Python:
The most widely used language in data science.
Essential libraries include:
Pandas
NumPy
Scikit-Learn
TensorFlow
✅ 4️⃣ Machine Learning:
Use algorithms to uncover patterns and make predictions.
Key types:
Regression
Classification
Clustering
✅ 5️⃣ Domain Knowledge:
Context matters.
Understand your industry to build relevant, useful, and accurate models.
❤3👍1
💻 How to Become a Data Engineer in 1 Year – Step by Step 📊🛠️
✅ Tip 1: Master SQL & Databases
- Learn SQL queries, joins, aggregations, and indexing
- Understand relational databases (PostgreSQL, MySQL)
- Explore NoSQL databases (MongoDB, Cassandra)
✅ Tip 2: Learn a Programming Language
- Python or Java are the most common
- Focus on data manipulation (pandas in Python)
- Automate ETL tasks
✅ Tip 3: Understand ETL Pipelines
- Extract → Transform → Load data efficiently
- Practice building pipelines using Python or tools like Apache Airflow
✅ Tip 4: Data Warehousing
- Learn about warehouses like Redshift, BigQuery, Snowflake
- Understand star schema, snowflake schema, and OLAP
✅ Tip 5: Data Modeling & Schema Design
- Learn to design efficient, scalable schemas
- Understand normalization and denormalization
✅ Tip 6: Big Data & Distributed Systems
- Basics of Hadoop & Spark
- Processing large datasets efficiently
✅ Tip 7: Cloud Platforms
- Familiarize with AWS, GCP, or Azure for storage & pipelines
- S3, Lambda, Glue, Dataproc, BigQuery, etc.
✅ Tip 8: Data Quality & Testing
- Implement checks for missing, duplicate, or inconsistent data
- Monitor pipelines for failures
✅ Tip 9: Real Projects
- Build end-to-end pipeline: API → ETL → Warehouse → Dashboard
- Work with streaming data (Kafka, Spark Streaming)
✅ Tip 10: Stay Updated & Practice
- Follow blogs, join communities, explore new tools
- Practice with Kaggle datasets and real-world scenarios
💬 Tap ❤️ for more!
✅ Tip 1: Master SQL & Databases
- Learn SQL queries, joins, aggregations, and indexing
- Understand relational databases (PostgreSQL, MySQL)
- Explore NoSQL databases (MongoDB, Cassandra)
✅ Tip 2: Learn a Programming Language
- Python or Java are the most common
- Focus on data manipulation (pandas in Python)
- Automate ETL tasks
✅ Tip 3: Understand ETL Pipelines
- Extract → Transform → Load data efficiently
- Practice building pipelines using Python or tools like Apache Airflow
✅ Tip 4: Data Warehousing
- Learn about warehouses like Redshift, BigQuery, Snowflake
- Understand star schema, snowflake schema, and OLAP
✅ Tip 5: Data Modeling & Schema Design
- Learn to design efficient, scalable schemas
- Understand normalization and denormalization
✅ Tip 6: Big Data & Distributed Systems
- Basics of Hadoop & Spark
- Processing large datasets efficiently
✅ Tip 7: Cloud Platforms
- Familiarize with AWS, GCP, or Azure for storage & pipelines
- S3, Lambda, Glue, Dataproc, BigQuery, etc.
✅ Tip 8: Data Quality & Testing
- Implement checks for missing, duplicate, or inconsistent data
- Monitor pipelines for failures
✅ Tip 9: Real Projects
- Build end-to-end pipeline: API → ETL → Warehouse → Dashboard
- Work with streaming data (Kafka, Spark Streaming)
✅ Tip 10: Stay Updated & Practice
- Follow blogs, join communities, explore new tools
- Practice with Kaggle datasets and real-world scenarios
💬 Tap ❤️ for more!
❤12
Descriptive Statistics and Exploratory Data Analysis.pdf
1 MB
Covers basic numerical and graphical summaries with practical examples, from University of Washington.
❤4
✅ 15 Data Engineering Interview Questions for Freshers 🛠️📊
These are core questions freshers face in 2025 interviews—per recent guides from DataCamp and GeeksforGeeks, ETL and pipelines remain staples, with added emphasis on cloud tools like AWS Glue for scalability. Your list nails the basics; practice explaining with real examples to shine!
1) What is Data Engineering?
Answer: Data Engineering involves designing, building, and managing systems and pipelines that collect, store, and process large volumes of data efficiently.
2) What is ETL?
Answer: ETL stands for Extract, Transform, Load — a process to extract data from sources, transform it into usable formats, and load it into a data warehouse or database.
3) Difference between ETL and ELT?
Answer: ETL transforms data before loading it; ELT loads raw data first, then transforms it inside the destination system.
4) What are Data Lakes and Data Warehouses?
Answer:
⦁ Data Lake: Stores raw, unstructured or structured data at scale.
⦁ Data Warehouse: Stores processed, structured data optimized for analytics.
5) What is a pipeline in Data Engineering?
Answer: A series of automated steps that move and transform data from source to destination.
6) What tools are commonly used in Data Engineering?
Answer: Apache Spark, Hadoop, Airflow, Kafka, SQL, Python, AWS Glue, Google BigQuery, etc.
7) What is Apache Kafka used for?
Answer: Kafka is a distributed event streaming platform used for real-time data pipelines and streaming apps.
8) What is the role of a Data Engineer?
Answer: To build reliable data pipelines, ensure data quality, optimize storage, and support data analytics teams.
9) What is schema-on-read vs schema-on-write?
Answer:
⦁ Schema-on-write: Data is structured when written (used in data warehouses).
⦁ Schema-on-read: Data is structured only when read (used in data lakes).
10) What are partitions in big data?
Answer: Partitioning splits data into parts based on keys (like date) to improve query performance.
11) How do you ensure data quality?
Answer: Data validation, cleansing, monitoring pipelines, and using checks for duplicates, nulls, or inconsistencies.
12) What is Apache Airflow?
Answer: An open-source workflow scheduler to programmatically author, schedule, and monitor data pipelines.
13) What is the difference between batch processing and stream processing?
Answer:
⦁ Batch: Processing large data chunks at intervals.
⦁ Stream: Processing data continuously in real-time.
14) What is data lineage?
Answer: Tracking the origin, movement, and transformation history of data through the pipeline.
15) How do you optimize data pipelines?
Answer: By parallelizing tasks, minimizing data movement, caching intermediate results, and monitoring resource usage.
💬 React ❤️ for more!
These are core questions freshers face in 2025 interviews—per recent guides from DataCamp and GeeksforGeeks, ETL and pipelines remain staples, with added emphasis on cloud tools like AWS Glue for scalability. Your list nails the basics; practice explaining with real examples to shine!
1) What is Data Engineering?
Answer: Data Engineering involves designing, building, and managing systems and pipelines that collect, store, and process large volumes of data efficiently.
2) What is ETL?
Answer: ETL stands for Extract, Transform, Load — a process to extract data from sources, transform it into usable formats, and load it into a data warehouse or database.
3) Difference between ETL and ELT?
Answer: ETL transforms data before loading it; ELT loads raw data first, then transforms it inside the destination system.
4) What are Data Lakes and Data Warehouses?
Answer:
⦁ Data Lake: Stores raw, unstructured or structured data at scale.
⦁ Data Warehouse: Stores processed, structured data optimized for analytics.
5) What is a pipeline in Data Engineering?
Answer: A series of automated steps that move and transform data from source to destination.
6) What tools are commonly used in Data Engineering?
Answer: Apache Spark, Hadoop, Airflow, Kafka, SQL, Python, AWS Glue, Google BigQuery, etc.
7) What is Apache Kafka used for?
Answer: Kafka is a distributed event streaming platform used for real-time data pipelines and streaming apps.
8) What is the role of a Data Engineer?
Answer: To build reliable data pipelines, ensure data quality, optimize storage, and support data analytics teams.
9) What is schema-on-read vs schema-on-write?
Answer:
⦁ Schema-on-write: Data is structured when written (used in data warehouses).
⦁ Schema-on-read: Data is structured only when read (used in data lakes).
10) What are partitions in big data?
Answer: Partitioning splits data into parts based on keys (like date) to improve query performance.
11) How do you ensure data quality?
Answer: Data validation, cleansing, monitoring pipelines, and using checks for duplicates, nulls, or inconsistencies.
12) What is Apache Airflow?
Answer: An open-source workflow scheduler to programmatically author, schedule, and monitor data pipelines.
13) What is the difference between batch processing and stream processing?
Answer:
⦁ Batch: Processing large data chunks at intervals.
⦁ Stream: Processing data continuously in real-time.
14) What is data lineage?
Answer: Tracking the origin, movement, and transformation history of data through the pipeline.
15) How do you optimize data pipelines?
Answer: By parallelizing tasks, minimizing data movement, caching intermediate results, and monitoring resource usage.
💬 React ❤️ for more!
❤3