The Data Science skill no one talks about...

Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
    1. a dataset, and
    2. a clearly defined metric to optimize for, e.g. accuracy

But it doesn’t.

It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.

Let’s go through an example.

Example

Imagine you are a data scientist at Uber. And your product lead tells you:

    👩‍💼: “We want to decrease user churn by 5% this quarter”

We say that a user churns when she decides to stop using Uber.

But why?

There are different reasons why a user would stop using Uber. For example:

   1.  “Lyft is offering better prices for that geo” (pricing problem)
   2. “Car waiting times are too long” (supply problem)
   3. “The Android version of the app is very slow” (client-app performance problem)

You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view.

Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?

This is when you pull out your great data science skills and EXPLORE THE DATA 🔎.

You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.

For example…

Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem)

One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:

    The A group. No user in this group will receive any discount.

    The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.

You could add more groups (e.g. C, D, E…) to test different pricing points.

In a nutshell

    1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem

📊 Data Science Essentials: What Every Data Enthusiast Should Know!

1️⃣ Understand Your Data
Always start with data exploration. Check for missing values, outliers, and overall distribution to avoid misleading insights.

2️⃣ Data Cleaning Matters
Noisy data leads to inaccurate predictions. Standardize formats, remove duplicates, and handle missing data effectively.

3️⃣ Use Descriptive & Inferential Statistics
Mean, median, mode, variance, standard deviation, correlation, hypothesis testing—these form the backbone of data interpretation.

4️⃣ Master Data Visualization
Bar charts, histograms, scatter plots, and heatmaps make insights more accessible and actionable.

5️⃣ Learn SQL for Efficient Data Extraction
Write optimized queries (SELECT, JOIN, GROUP BY, WHERE) to retrieve relevant data from databases.

6️⃣ Build Strong Programming Skills
Python (Pandas, NumPy, Scikit-learn) and R are essential for data manipulation and analysis.

7️⃣ Understand Machine Learning Basics
Know key algorithms—linear regression, decision trees, random forests, and clustering—to develop predictive models.

8️⃣ Learn Dashboarding & Storytelling
Power BI and Tableau help convert raw data into actionable insights for stakeholders.

🔥 Pro Tip: Always cross-check your results with different techniques to ensure accuracy!

Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

DOUBLE TAP ❤️ IF YOU FOUND THIS HELPFUL!

A-Z of Data Science Part-1

A-Z of Data Science Part-2

Master Power BI with this Cheat Sheet🔥

If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision!

📝 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗕𝗮𝘀𝗶𝗰𝘀:

DAX Functions:

- SUMX: Sum of values based on a condition.
- FILTER: Filter data based on a given condition.
- RELATED: Retrieve a related column from another table.
- CALCULATE: Perform dynamic calculations.
- EARLIER: Access a column from a higher context.
- CROSSJOIN: Create a Cartesian product of two tables.
- UNION: Combine the results from multiple tables.
- RANKX: Rank data within a column.
- DISTINCT: Filter unique rows.

Data Modeling:

- Relationships: Create, manage, and modify relationships.
- Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year).
- Calculated Columns: Create calculated columns to extend data.
- Measures: Write powerful measures to analyze data effectively.

Data Visualization:

- Charts: Bar charts, line charts, pie charts, and more.
- Table & Matrix: Display tabular data and matrix visuals.
- Slicers: Create interactive filters.
- Tooltips: Enhance visual interactivity with tooltips.
- Map: Display geographical data effectively.

✨ 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗧𝗶𝗽𝘀:

✅ Use DAX for efficient data analysis.

✅ Optimize data models for performance.

✅ Utilize drill-through and drill-down for deeper insights.

✅ Leverage bookmarks for enhanced navigation.

✅ Annotate your reports with comments for clarity.

Like this post if you need more content like this 👍❤️

Complete 3-months roadmap to learn Artificial Intelligence (AI) 👇👇

### Month 1: Fundamentals of AI and Python

Week 1: Introduction to AI
- Key Concepts: What is AI? Categories (Narrow AI, General AI, Super AI), Applications of AI.
- Reading: Research papers and articles on AI.
- Task: Watch introductory AI videos (e.g., Andrew Ng's "What is AI?" on Coursera).

Week 2: Python for AI
- Skills: Basics of Python programming (variables, loops, conditionals, functions, OOP).
- Resources: Python tutorials (W3Schools, Real Python).
- Task: Write simple Python scripts.

Week 3: Libraries for AI
- Key Libraries: NumPy, Pandas, Matplotlib, Scikit-learn.
- Task: Install libraries and practice data manipulation and visualization.
- Resources: Documentation and tutorials on these libraries.

Week 4: Linear Algebra and Probability
- Key Topics: Matrices, Vectors, Eigenvalues, Probability theory.
- Resources: Khan Academy (Linear Algebra), MIT OCW.
- Task: Solve basic linear algebra problems and write Python functions to implement them.

---

### Month 2: Core AI Techniques & Machine Learning

Week 5: Machine Learning Basics
- Key Concepts: Supervised, Unsupervised learning, Model evaluation metrics.
- Algorithms: Linear Regression, Logistic Regression.
- Task: Build basic models using Scikit-learn.
- Resources: Coursera’s Machine Learning by Andrew Ng, Kaggle datasets.

Week 6: Decision Trees, Random Forests, and KNN
- Key Concepts: Decision Trees, Random Forests, K-Nearest Neighbors (KNN).
- Task: Implement these algorithms and analyze their performance.
- Resources: Hands-on Machine Learning with Scikit-learn.

Week 7: Neural Networks & Deep Learning
- Key Concepts: Artificial Neurons, Forward and Backpropagation, Activation Functions.
- Framework: TensorFlow, Keras.
- Task: Build a simple neural network for a classification problem.
- Resources: Fast.ai, Coursera Deep Learning Specialization by Andrew Ng.

Week 8: Convolutional Neural Networks (CNN)
- Key Concepts: Image classification, Convolution, Pooling.
- Task: Build a CNN using Keras/TensorFlow to classify images (e.g., CIFAR-10 dataset).
- Resources: CS231n Stanford Course, Fast.ai Computer Vision.

---

### Month 3: Advanced AI Techniques & Projects

Week 9: Natural Language Processing (NLP)
- Key Concepts: Tokenization, Embeddings, Sentiment Analysis.
- Task: Implement text classification using NLTK/Spacy or transformers.
- Resources: Hugging Face, Coursera NLP courses.

Week 10: Reinforcement Learning
- Key Concepts: Q-learning, Markov Decision Processes (MDP), Policy Gradients.
- Task: Solve a simple RL problem (e.g., OpenAI Gym).
- Resources: Sutton and Barto’s book on Reinforcement Learning, OpenAI Gym.

Week 11: AI Model Deployment
- Key Concepts: Model deployment using Flask/Streamlit, Model Serving.
- Task: Deploy a trained model using Flask API or Streamlit.
- Resources: Heroku deployment guides, Streamlit documentation.

Week 12: AI Capstone Project
- Task: Create a full-fledged AI project (e.g., Image recognition app, Sentiment analysis, or Chatbot).
- Presentation: Prepare and document your project.
- Goal: Deploy your AI model and share it on GitHub/Portfolio.

### Tools and Platforms:
- Python IDE: Jupyter, PyCharm, or VSCode.
- Datasets: Kaggle, UCI Machine Learning Repository.
- Version Control: GitHub or GitLab for managing code.

Free Books and Courses to Learn Artificial Intelligence👇👇

Introduction to AI for Business Free Course

Top Platforms for Building Data Science Portfolio

Artificial Intelligence: Foundations of Computational Agents Free Book

Learn Basics about AI Free Udemy Course

Amazing AI Reverse Image Search

By following this roadmap, you’ll gain a strong understanding of AI concepts and practical skills in Python, machine learning, and neural networks.

Join @free4unow_backup for more free courses

ENJOY LEARNING 👍👍

Data Science Interview Questions with Answers 👇

Q1: How would you analyze time series data to forecast production rates for a manufacturing unit? 

Ans: I'd use tools like Prophet for time series forecasting. After decomposing the data to identify trends and seasonality, I'd build a model to forecast production rates.


Q2: Describe a situation where you had to design a data warehousing solution for large-scale manufacturing data. 

Ans: For a project with multiple manufacturing units, I designed a star schema with a central fact table and surrounding dimension tables to allow for efficient querying.

Q3: How would you use data to identify bottlenecks in a production line? 

Ans:  I'd analyze production metrics, time logs, and machine efficiency data to identify stages in the production line with delays or reduced output, pinpointing potential bottlenecks.

Q4: How do you ensure data accuracy and consistency in a manufacturing environment with multiple data sources?

Ans: I'd implement data validation checks, use standardized data collection protocols across units, and set up regular data reconciliation processes to ensure accuracy and consistency.

𝗦𝗤𝗟 𝗝𝗼𝗶𝗻𝘀 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁 - 𝗙𝘂𝗹𝗹𝘆 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱

𝗪𝗵𝘆 𝗷𝗼𝗶𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?
Joins let you combine data from multiple tables to extract meaningful insights.
Every serious data analyst or backend dev should master these.

Let’s break them down with clarity:

𝗜𝗡𝗡𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns only the rows with matching keys in both tables
→ Think of it as intersection
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Customers who have placed at least one order

SELECT *
FROM Customers
INNER JOIN Orders
ON Customers.ID = Orders.CustomerID;

𝗟𝗘𝗙𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the left table + matching rows from the right
→ If no match, right side = NULL
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
List all customers, even if they’ve never ordered

SELECT *
FROM Customers
LEFT JOIN Orders
ON Customers.ID = Orders.CustomerID;

𝗥𝗜𝗚𝗛𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the right table + matching rows from the left
→ Rarely used, but similar logic
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
All orders, even from unknown or deleted customers

SELECT *
FROM Customers
RIGHT JOIN Orders
ON Customers.ID = Orders.CustomerID;

𝗙𝗨𝗟𝗟 𝗢𝗨𝗧𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns all records when there’s a match in either table
→ Unmatched rows = NULLs
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all customers and all orders, whether matched or not

SELECT *
FROM Customers
FULL OUTER JOIN Orders
ON Customers.ID = Orders.CustomerID;

𝗖𝗥𝗢𝗦𝗦 𝗝𝗢𝗜𝗡
→ Returns Cartesian product (all combinations)
→ Use with care. 1,000 x 1,000 rows = 1,000,000 results!
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all possible product and supplier pairings

SELECT *
FROM Products
CROSS JOIN Suppliers;

𝗦𝗘𝗟𝗙 𝗝𝗢𝗜𝗡
→ Join a table to itself
→ Used for hierarchical data like employees & managers
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Find each employee’s manager

SELECT A.Name AS Employee, B.Name AS Manager
FROM Employees A
JOIN Employees B
ON A.ManagerID = B.ID;

𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀
→ Always use aliases (A, B) to simplify joins
→ Use JOIN ON instead of WHERE for better clarity
→ Test each join with LIMIT first to avoid surprises

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