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 😊
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🔥 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
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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.
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📊 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.
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