What 𝗠𝗟 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 are commonly asked in 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀?

These are fair game in interviews at 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀, 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴 & 𝗹𝗮𝗿𝗴𝗲 𝘁𝗲𝗰𝗵.

𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
- Supervised vs. Unsupervised Learning
- Overfitting and Underfitting
- Cross-validation
- Bias-Variance Tradeoff
- Accuracy vs Interpretability
- Accuracy vs Latency

𝗠𝗟 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines
- K-Nearest Neighbors
- Naive Bayes
- Linear Regression
- Ridge and Lasso Regression
- K-Means Clustering
- Hierarchical Clustering
- PCA

𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗦𝘁𝗲𝗽𝘀
- EDA
- Data Cleaning (e.g. missing value imputation)
- Data Preprocessing (e.g. scaling)
- Feature Engineering (e.g. aggregation)
- Feature Selection (e.g. variable importance)
- Model Training (e.g. gradient descent)
- Model Evaluation (e.g. AUC vs Accuracy)
- Model Productionization

𝗛𝘆𝗽𝗲𝗿𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗧𝘂𝗻𝗶𝗻𝗴
- Grid Search
- Random Search
- Bayesian Optimization

𝗠𝗟 𝗖𝗮𝘀𝗲𝘀
- [Capital One] Detect credit card fraudsters
- [Amazon] Forecast monthly sales
- [Airbnb] Estimate lifetime value of a guest

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Relatable? 😂

When you start making good money, do this:

1. Buy fewer clothes, but wear the highest quality.
2. Eat premium food, not junk.
3. Hire a helper for household chores. Buy back your time.
4. Upgrade your mattress. Sleep changes everything.
5. Invest in experiences, not just stuff.
6. Upgrade your financial adviser. The one who got you here won’t get you to the next level.
7. Surround yourself with high-value people.

Small shifts. Big impact.

How much Statistics must I know to become a Data Scientist?

This is one of the most common questions

Here are the must-know Statistics concepts every Data Scientist should know:

𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆

↗ Bayes' Theorem & conditional probability
↗ Permutations & combinations
↗ Card & die roll problem-solving

𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀

↗ Mean, median, mode
↗ Standard deviation and variance
↗  Bernoulli's, Binomial, Normal, Uniform, Exponential distributions

𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀

↗ A/B experimentation
↗ T-test, Z-test, Chi-squared tests
↗ Type 1 & 2 errors
↗ Sampling techniques & biases
↗ Confidence intervals & p-values
↗ Central Limit Theorem
↗ Causal inference techniques

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴

↗ Logistic & Linear regression
↗ Decision trees & random forests
↗ Clustering models
↗ Feature engineering
↗ Feature selection methods
↗ Model testing & validation
↗ Time series analysis

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10 great Python packages for Data Science not known to many:

1️⃣ CleanLab

Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.

2️⃣ LazyPredict

A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.

3️⃣ Lux

A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.

4️⃣ PyForest

A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.

5️⃣ PivotTableJS

PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥

6️⃣ Drawdata

Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.

7️⃣ black

The Uncompromising Code Formatter

8️⃣ PyCaret

An open-source, low-code machine learning library in Python that automates the machine learning workflow.

9️⃣ PyTorch-Lightning by LightningAI

Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.

🔟 Streamlit

A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment.

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Data Science Interview Questions

Question 1 : How would you approach building a recommendation system for personalized content on Facebook? Consider factors like scalability and user privacy.

   - Answer: Building a recommendation system for personalized content on Facebook would involve collaborative filtering or content-based methods. Scalability can be achieved using distributed computing, and user privacy can be preserved through techniques like federated learning.


Question 2 : Describe a situation where you had to navigate conflicting opinions within your team. How did you facilitate resolution and maintain team cohesion?

   - Answer: In navigating conflicting opinions within a team, I facilitated resolution through open communication, active listening, and finding common ground. Prioritizing team cohesion was key to achieving consensus.


Question 3 : How would you enhance the security of user data on Facebook, considering the evolving landscape of cybersecurity threats?

   - Answer: Enhancing the security of user data on Facebook involves implementing robust encryption mechanisms, access controls, and regular security audits. Ensuring compliance with privacy regulations and proactive threat monitoring are essential.

Question 4 : Design a real-time notification system for Facebook, ensuring timely delivery of notifications to users across various platforms.

   - Answer: Designing a real-time notification system for Facebook requires technologies like WebSocket for real-time communication and push notifications. Ensuring scalability and reliability through distributed systems is crucial for timely delivery.

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Data Analyst vs Data Scientist 👆

Data Science Interview Questions

1: How would you preprocess and tokenize text data from tweets for sentiment analysis? Discuss potential challenges and solutions.

- Answer: Preprocessing and tokenizing text data for sentiment analysis involves tasks like lowercasing, removing stop words, and stemming or lemmatization. Handling challenges like handling emojis, slang, and noisy text is crucial. Tools like NLTK or spaCy can assist in these tasks.


2: Explain the collaborative filtering approach in building recommendation systems. How might Twitter use this to enhance user experience?

- Answer: Collaborative filtering recommends items based on user preferences and similarities. Techniques include user-based or item-based collaborative filtering and matrix factorization. Twitter could leverage user interactions to recommend tweets, users, or topics.


3: Write a Python or Scala function to count the frequency of hashtags in a given collection of tweets.

- Answer (Python):
   

     def count_hashtags(tweet_collection):
         hashtags_count = {}
         for tweet in tweet_collection:
             hashtags = [word for word in tweet.split() if word.startswith('#')]
             for hashtag in hashtags:
                 hashtags_count[hashtag] = hashtags_count.get(hashtag, 0) + 1
         return hashtags_count
     

4: How does graph analysis contribute to understanding user interactions and content propagation on Twitter? Provide a specific use case.

- Answer: Graph analysis on Twitter involves examining user interactions. For instance, identifying influential users or detecting communities based on retweet or mention networks. Algorithms like PageRank or Louvain Modularity can aid in these analyses.

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Python Roadmap 👆

If I Were to Start My Data Science Career from Scratch, Here's What I Would Do 👇

1️⃣ Master Advanced SQL

Foundations: Learn database structures, tables, and relationships.

Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.

Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.

JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.

Advanced Concepts: CTEs, window functions, and query optimization.

Metric Development: Build and report metrics effectively.


2️⃣ Study Statistics & A/B Testing

Descriptive Statistics: Know your mean, median, mode, and standard deviation.

Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.

Probability: Understand basic probability and Bayes' theorem.

Intro to ML: Start with linear regression, decision trees, and K-means clustering.

Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.

A/B Testing: Design experiments—hypothesis formation, sample size calculation, and sample biases.


3️⃣ Learn Python for Data

Data Manipulation: Use pandas for data cleaning and manipulation.

Data Visualization: Explore matplotlib and seaborn for creating visualizations.

Hypothesis Testing: Dive into scipy for statistical testing.

Basic Modeling: Practice building models with scikit-learn.


4️⃣ Develop Product Sense

Product Management Basics: Manage projects and understand the product life cycle.

Data-Driven Strategy: Leverage data to inform decisions and measure success.

Metrics in Business: Define and evaluate metrics that matter to the business.


5️⃣ Hone Soft Skills

Communication: Clearly explain data findings to technical and non-technical audiences.

Collaboration: Work effectively in teams.

Time Management: Prioritize and manage projects efficiently.

Self-Reflection: Regularly assess and improve your skills.


6️⃣ Bonus: Basic Data Engineering

Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.

ETL: Set up extraction jobs, manage dependencies, clean and validate data.

Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.

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Complete Data Science Roadmap 
👇👇 

1. Introduction to Data Science 
   - Overview and Importance 
   - Data Science Lifecycle 
   - Key Roles (Data Scientist, Analyst, Engineer) 

2. Mathematics and Statistics 
   - Probability and Distributions 
   - Descriptive/Inferential Statistics 
   - Hypothesis Testing 
   - Linear Algebra and Calculus Basics 

3. Programming Languages 
   - Python: NumPy, Pandas, Matplotlib 
   - R: dplyr, ggplot2 
   - SQL: Joins, Aggregations, CRUD 

4. Data Collection & Preprocessing 
   - Data Cleaning and Wrangling 
   - Handling Missing Data 
   - Feature Engineering 

5. Exploratory Data Analysis (EDA) 
   - Summary Statistics 
   - Data Visualization (Histograms, Box Plots, Correlation) 

6. Machine Learning 
   - Supervised (Linear/Logistic Regression, Decision Trees) 
   - Unsupervised (K-Means, PCA) 
   - Model Selection and Cross-Validation 

7. Advanced Machine Learning 
   - SVM, Random Forests, Boosting 
   - Neural Networks Basics 

8. Deep Learning 
   - Neural Networks Architecture 
   - CNNs for Image Data 
   - RNNs for Sequential Data 

9. Natural Language Processing (NLP) 
   - Text Preprocessing 
   - Sentiment Analysis 
   - Word Embeddings (Word2Vec) 

10. Data Visualization & Storytelling 
   - Dashboards (Tableau, Power BI) 
   - Telling Stories with Data 

11. Model Deployment 
   - Deploy with Flask or Django 
   - Monitoring and Retraining Models 

12. Big Data & Cloud 
   - Introduction to Hadoop, Spark 
   - Cloud Tools (AWS, Google Cloud) 

13. Data Engineering Basics 
   - ETL Pipelines 
   - Data Warehousing (Redshift, BigQuery) 

14. Ethics in Data Science 
   - Ethical Data Usage 
   - Bias in AI Models 

15. Tools for Data Science 
   - Jupyter, Git, Docker 

16. Career Path & Certifications 
   - Building a Data Science Portfolio 

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©How fresher can get a job as a data scientist?©

1. Education: Obtain a degree in a relevant field such as computer science, statistics, mathematics, or data science. Consider pursuing additional certifications or specialized courses in data science to enhance your skills.

2. Build a strong foundation: Develop a strong understanding of key concepts in data science such as statistics, machine learning, programming languages (such as Python or R), and data visualization.

3. Hands-on experience: Gain practical experience by working on projects, participating in hackathons, or internships. Building a portfolio of projects showcasing your data science skills can be beneficial when applying for jobs.

4. Networking: Attend industry events, conferences, and meetups to network with professionals in the field. Networking can help you learn about job opportunities and make valuable connections.

5. Apply for entry-level positions: Look for entry-level positions such as data analyst, research assistant, or junior data scientist roles to gain experience and start building your career in data science.

6. Prepare for interviews: Practice common data science interview questions, showcase your problem-solving skills, and be prepared to discuss your projects and experiences related to data science.

7. Continuous learning: Data science is a rapidly evolving field, so it's important to stay updated on the latest trends, tools, and techniques. Consider taking online courses, attending workshops, or joining professional organizations to continue learning and growing in the field.

Many data scientists don't know how to push ML models to production. Here's the recipe 👇

𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀

🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data
🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time
🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model
🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github
🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API
🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API
🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker
🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs
🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring

𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲

𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴

Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling.

𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁

Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.

𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻

Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment

𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁

Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.

𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴

Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data.

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Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science

Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.

1. Basic python and statistics

Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset

2. Advanced Statistics

Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset

3. Supervised Learning

a) Regression Problems

How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview

b) Classification problems

Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking

4. Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

5. Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Share with credits: https://t.iss.one/sqlproject

ENJOY LEARNING 👍👍

Data Science Learning Plan

Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)

Step 2: Python for Data Science (Basics and Libraries)

Step 3: Data Manipulation and Analysis (Pandas, NumPy)

Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)

Step 5: Databases and SQL for Data Retrieval

Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)

Step 7: Data Cleaning and Preprocessing

Step 8: Feature Engineering and Selection

Step 9: Model Evaluation and Tuning

Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)

Step 11: Working with Big Data (Hadoop, Spark)

Step 12: Building Data Science Projects and Portfolio

Data Science Resources
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Practice projects to consider:

1. Implement a basic search engine: Read a set of documents and build an index of keywords. Then, implement a search function that returns a list of documents that match the query.

2. Build a recommendation system: Read a set of user-item interactions and build a recommendation system that suggests items to users based on their past behavior.

3. Create a data analysis tool: Read a large dataset and implement a tool that performs various analyses, such as calculating summary statistics, visualizing distributions, and identifying patterns and correlations.

4. Implement a graph algorithm: Study a graph algorithm such as Dijkstra's shortest path algorithm, and implement it in Python. Then, test it on real-world graphs to see how it performs.

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A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

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Accenture Data Scientist Interview Questions!

1st round-

Technical Round

- 2 SQl questions based on playing around views and table, which could be solved by both subqueries and window functions.

- 2 Pandas questions , testing your knowledge on filtering , concatenation , joins and merge.

- 3-4 Machine Learning questions completely based on my Projects, starting from
Explaining the problem statements and then discussing the roadblocks of those projects and some cross questions.

2nd round-

- Couple of python questions agains on pandas and numpy and some hypothetical data.

- Machine Learning projects explanations and cross questions.

- Case Study and a quiz question.

3rd and Final round.

HR interview

Simple Scenerio Based Questions.

Data Science Resources
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https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y

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