Advanced Questions Asked by Big 4

📊 Excel Questions
1. How do you use Excel to forecast future trends based on historical data? Describe a scenario where you built a forecasting model.
2. Can you explain how you would automate repetitive tasks in Excel using VBA (Visual Basic for Applications)? Provide an example of a complex macro you created.
3. Describe a time when you had to merge and analyze data from multiple Excel workbooks. How did you ensure data integrity and accuracy?

🗄 SQL Questions
1. How would you design a database schema for a new e-commerce platform to efficiently handle large volumes of transactions and user data?
2. Describe a complex SQL query you wrote to solve a business problem. What was the problem, and how did your query help resolve it?
3. How do you ensure data integrity and consistency in a multi-user database environment? Explain the techniques and tools you use.

🐍 Python Questions
1. How would you use Python to automate data extraction from various APIs and combine the data for analysis? Provide an example.
2. Describe a machine learning project you worked on using Python. What was the objective, and how did you approach the data preprocessing, model selection, and evaluation?
3. Explain how you would use Python to detect and handle anomalies in a dataset. What techniques and libraries would you employ?

📈 Power BI Questions
1. How do you create interactive dashboards in Power BI that can dynamically update based on user inputs? Provide an example of a dashboard you built.
2. Describe a scenario where you used Power BI to integrate data from non-traditional sources (e.g., web scraping, APIs). How did you handle the data transformation and visualization?
3. How do you ensure the performance and scalability of Power BI reports when dealing with large datasets? Describe the techniques and best practices you follow.


💡 Tips for Success:
Understand the business context: Tailor your answers to show how your technical skills solve real business problems.
Provide specific examples: Highlight your past experiences with concrete examples.
Stay updated: Continuously learn and adapt to new tools and methodologies.

Hope it helps :)

20 essential Python libraries for data science:

🔹 pandas: Data manipulation and analysis. Essential for handling DataFrames.
🔹 numpy: Numerical computing. Perfect for working with arrays and mathematical functions.
🔹 scikit-learn: Machine learning. Comprehensive tools for predictive data analysis.
🔹 matplotlib: Data visualization. Great for creating static, animated, and interactive plots.
🔹 seaborn: Statistical data visualization. Makes complex plots easy and beautiful.
Data Science
🔹 scipy: Scientific computing. Provides algorithms for optimization, integration, and more.
🔹 statsmodels: Statistical modeling. Ideal for conducting statistical tests and data exploration.
🔹 tensorflow: Deep learning. End-to-end open-source platform for machine learning.
🔹 keras: High-level neural networks API. Simplifies building and training deep learning models.
🔹 pytorch: Deep learning. A flexible and easy-to-use deep learning library.
🔹 mlflow: Machine learning lifecycle. Manages the machine learning lifecycle, including experimentation, reproducibility, and deployment.
🔹 pydantic: Data validation. Provides data validation and settings management using Python type annotations.
🔹 xgboost: Gradient boosting. An optimized distributed gradient boosting library.
🔹 lightgbm: Gradient boosting. A fast, distributed, high-performance gradient boosting framework.

🔍 Best Data Analytics Roles Based on Your Graduation Background!

Thinking about a career in Data Analytics but unsure which role fits your background? Check out these top job roles based on your degree:

🚀 For Mathematics/Statistics Graduates:
🔹 Data Analyst
🔹 Statistical Analyst
🔹 Quantitative Analyst
🔹 Risk Analyst

🚀 For Computer Science/IT Graduates:
🔹 Data Scientist
🔹 Business Intelligence Developer
🔹 Data Engineer
🔹 Data Architect

🚀 For Economics/Finance Graduates:
🔹 Financial Analyst
🔹 Market Research Analyst
🔹 Economic Consultant
🔹 Data Journalist

🚀 For Business/Management Graduates:
🔹 Business Analyst
🔹 Operations Research Analyst
🔹 Marketing Analytics Manager
🔹 Supply Chain Analyst

🚀 For Engineering Graduates:
🔹 Data Scientist
🔹 Industrial Engineer
🔹 Operations Research Analyst
🔹 Quality Engineer

🚀 For Social Science Graduates:
🔹 Data Analyst
🔹 Research Assistant
🔹 Social Media Analyst
🔹 Public Health Analyst

🚀 For Biology/Healthcare Graduates:
🔹 Clinical Data Analyst
🔹 Biostatistician
🔹 Research Coordinator
🔹 Healthcare Consultant

✅ Pro Tip:

Some of these roles may require additional certifications or upskilling in SQL, Python, Power BI, Tableau, or Machine Learning to stand out in the job market.

Like if it helps ❤️

Python Libraries & Frameworks

COMMON TERMINOLOGIES IN PYTHON - PART 1

Have you ever gotten into a discussion with a programmer before? Did you find some of the Terminologies mentioned strange or you didn't fully understand them?

In this series, we would be looking at the common Terminologies in python.

It is important to know these Terminologies to be able to professionally/properly explain your codes to people and/or to be able to understand what people say in an instant when these codes are mentioned. Below are a few:

IDLE (Integrated Development and Learning Environment) - this is an environment that allows you to easily write Python code. IDLE can be used to execute a single statements and create, modify, and execute Python scripts.

Python Shell - This is the interactive environment that allows you to type in python code and execute them immediately

System Python - This is the version of python that comes with your operating system

Prompt - usually represented by the symbol ">>>" and it simply means that python is waiting for you to give it some instructions

REPL (Read-Evaluate-Print-Loop) - this refers to the sequence of events in your interactive window in form of a loop (python reads the code inputted>the code is evaluated>output is printed)

Argument - this is a value that is passed to a function when called eg print("Hello World")... "Hello World" is the argument that is being passed.

Function - this is a code that takes some input, known as arguments, processes that input and produces an output called a return value. E.g print("Hello World")... print is the function

Return Value - this is the value that a function returns to the calling script or function when it completes its task (in other words, Output). E.g.
>>> print("Hello World")
Hello World
Where Hello World is your return value.

Note: A return value can be any of these variable types: handle, integer, object, or string

Script - This is a file where you store your python code in a text file and execute all of the code with a single command

Script files - this is a file containing a group of python scripts

React ♥️ for more

How a SQL query gets executed internally - Lets see step by step!

We all know SQL, but most of us do not understand the internals of it.

Let me take an example to explain this better.

Select p.plan_name, count(plan_id) as total_count
From plans p
Join subscriptions s on s.plan_id=p.plan_id
Where p.plan_name !=’premium’
Group by p.plan_name
Having count(plan_id) > 100
Order by p.plan_name
Limit 10;

Step 01: Get the table data required to run the sql query
Operations: FROM, JOIN (From plans p, Join subscriptions s)

Step 02: Filter the data rows
Operations: WHERE (where p.plan_name=’premium’)

Step 03: Group the data
Operations: GROUP (group by p.plan_name)

Step 04: Filter the grouped data
Operations: HAVING (having count(plan_id) > 100)

Step 05: Select the data columns
Operations: SELECT (select p.plan_name, count(p.plan_id)

Step 06: Order the data
Operations: ORDER BY (order by p.plan_name)

Step 07: Limit the data rows
Operations: LIMIT (limit 100)

Knowing the Internals really help.