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.me/sqlproject
ENJOY LEARNING ๐๐
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.me/sqlproject
ENJOY LEARNING ๐๐
๐5
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
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
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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
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like for more ๐
๐4โค1
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.
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.
Like for more ๐
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.
Like for more ๐
๐13โค8
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
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content ๐๐
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
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content ๐๐
๐5โค1
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Resume key words for data scientist role explained in points:
1. Data Analysis:
- Proficient in extracting, cleaning, and analyzing data to derive insights.
- Skilled in using statistical methods and machine learning algorithms for data analysis.
- Experience with tools such as Python, R, or SQL for data manipulation and analysis.
2. Machine Learning:
- Strong understanding of machine learning techniques such as regression, classification, clustering, and neural networks.
- Experience in model development, evaluation, and deployment.
- Familiarity with libraries like TensorFlow, scikit-learn, or PyTorch for implementing machine learning models.
3. Data Visualization:
- Ability to present complex data in a clear and understandable manner through visualizations.
- Proficiency in tools like Matplotlib, Seaborn, or Tableau for creating insightful graphs and charts.
- Understanding of best practices in data visualization for effective communication of findings.
4. Big Data:
- Experience working with large datasets using technologies like Hadoop, Spark, or Apache Flink.
- Knowledge of distributed computing principles and tools for processing and analyzing big data.
- Ability to optimize algorithms and processes for scalability and performance.
5. Problem-Solving:
- Strong analytical and problem-solving skills to tackle complex data-related challenges.
- Ability to formulate hypotheses, design experiments, and iterate on solutions.
- Aptitude for identifying opportunities for leveraging data to drive business outcomes and decision-making.
Resume key words for a data analyst role
1. SQL (Structured Query Language):
- SQL is a programming language used for managing and querying relational databases.
- Data analysts often use SQL to extract, manipulate, and analyze data stored in databases, making it a fundamental skill for the role.
2. Python/R:
- Python and R are popular programming languages used for data analysis and statistical computing.
- Proficiency in Python or R allows data analysts to perform various tasks such as data cleaning, modeling, visualization, and machine learning.
3. Data Visualization:
- Data visualization involves presenting data in graphical or visual formats to communicate insights effectively.
- Data analysts use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to create visualizations that help stakeholders understand complex data patterns and trends.
4. Statistical Analysis:
- Statistical analysis involves applying statistical methods to analyze and interpret data.
- Data analysts use statistical techniques to uncover relationships, trends, and patterns in data, providing valuable insights for decision-making.
5. Data-driven Decision Making:
- Data-driven decision making is the process of making decisions based on data analysis and evidence rather than intuition or gut feelings.
- Data analysts play a crucial role in helping organizations make informed decisions by analyzing data and providing actionable insights that drive business strategies and operations.
Data Science Interview Resources
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1. Data Analysis:
- Proficient in extracting, cleaning, and analyzing data to derive insights.
- Skilled in using statistical methods and machine learning algorithms for data analysis.
- Experience with tools such as Python, R, or SQL for data manipulation and analysis.
2. Machine Learning:
- Strong understanding of machine learning techniques such as regression, classification, clustering, and neural networks.
- Experience in model development, evaluation, and deployment.
- Familiarity with libraries like TensorFlow, scikit-learn, or PyTorch for implementing machine learning models.
3. Data Visualization:
- Ability to present complex data in a clear and understandable manner through visualizations.
- Proficiency in tools like Matplotlib, Seaborn, or Tableau for creating insightful graphs and charts.
- Understanding of best practices in data visualization for effective communication of findings.
4. Big Data:
- Experience working with large datasets using technologies like Hadoop, Spark, or Apache Flink.
- Knowledge of distributed computing principles and tools for processing and analyzing big data.
- Ability to optimize algorithms and processes for scalability and performance.
5. Problem-Solving:
- Strong analytical and problem-solving skills to tackle complex data-related challenges.
- Ability to formulate hypotheses, design experiments, and iterate on solutions.
- Aptitude for identifying opportunities for leveraging data to drive business outcomes and decision-making.
Resume key words for a data analyst role
1. SQL (Structured Query Language):
- SQL is a programming language used for managing and querying relational databases.
- Data analysts often use SQL to extract, manipulate, and analyze data stored in databases, making it a fundamental skill for the role.
2. Python/R:
- Python and R are popular programming languages used for data analysis and statistical computing.
- Proficiency in Python or R allows data analysts to perform various tasks such as data cleaning, modeling, visualization, and machine learning.
3. Data Visualization:
- Data visualization involves presenting data in graphical or visual formats to communicate insights effectively.
- Data analysts use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to create visualizations that help stakeholders understand complex data patterns and trends.
4. Statistical Analysis:
- Statistical analysis involves applying statistical methods to analyze and interpret data.
- Data analysts use statistical techniques to uncover relationships, trends, and patterns in data, providing valuable insights for decision-making.
5. Data-driven Decision Making:
- Data-driven decision making is the process of making decisions based on data analysis and evidence rather than intuition or gut feelings.
- Data analysts play a crucial role in helping organizations make informed decisions by analyzing data and providing actionable insights that drive business strategies and operations.
Data Science Interview Resources
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
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๐13โค4
ML Interview Question โฌ๏ธ
โก๏ธ Logistic Regression
The interviewer asked to explain Logistic Regression along with its:
๐ท Cost function
๐ท Assumptions
๐ท Evaluation metrics
Here is the step by step approach to answer:
โ๏ธ Cost function: Point out how logistic regression uses log loss for classification.
โ๏ธ Assumptions: Explain LR assumes features are independent and they have a linear link.
โ๏ธ Evaluation metrics: Discuss accuracy, precision, and F1-score to measure performance.
Knowing every concept is important but more than that, it is important to convey our knowledge๐ฏ
Data Science Resources
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content ๐๐
โก๏ธ Logistic Regression
The interviewer asked to explain Logistic Regression along with its:
๐ท Cost function
๐ท Assumptions
๐ท Evaluation metrics
Here is the step by step approach to answer:
โ๏ธ Cost function: Point out how logistic regression uses log loss for classification.
โ๏ธ Assumptions: Explain LR assumes features are independent and they have a linear link.
โ๏ธ Evaluation metrics: Discuss accuracy, precision, and F1-score to measure performance.
Knowing every concept is important but more than that, it is important to convey our knowledge๐ฏ
Data Science Resources
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content ๐๐
๐10
๐ Top 10 Tools Data Scientists Love! ๐ง
In the ever-evolving world of data science, staying updated with the right tools is crucial to solving complex problems and deriving meaningful insights.
๐ Hereโs a quick breakdown of the most popular tools:
1. Python ๐: The go-to language for data science, favored for its versatility and powerful libraries.
2. SQL ๐ ๏ธ: Essential for querying databases and manipulating data.
3. Jupyter Notebooks ๐: An interactive environment that makes data analysis and visualization a breeze.
4. TensorFlow/PyTorch ๐ค: Leading frameworks for deep learning and neural networks.
5. Tableau ๐: A user-friendly tool for creating stunning visualizations and dashboards.
6. Git & GitHub ๐ป: Version control systems that every data scientist should master.
7. Hadoop & Spark ๐ฅ: Big data frameworks that help process massive datasets efficiently.
8. Scikit-learn ๐งฌ: A powerful library for machine learning in Python.
9. R ๐: A statistical programming language that is still a favorite among many analysts.
10. Docker ๐: A must-have for containerization and deploying applications.
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content ๐๐
In the ever-evolving world of data science, staying updated with the right tools is crucial to solving complex problems and deriving meaningful insights.
๐ Hereโs a quick breakdown of the most popular tools:
1. Python ๐: The go-to language for data science, favored for its versatility and powerful libraries.
2. SQL ๐ ๏ธ: Essential for querying databases and manipulating data.
3. Jupyter Notebooks ๐: An interactive environment that makes data analysis and visualization a breeze.
4. TensorFlow/PyTorch ๐ค: Leading frameworks for deep learning and neural networks.
5. Tableau ๐: A user-friendly tool for creating stunning visualizations and dashboards.
6. Git & GitHub ๐ป: Version control systems that every data scientist should master.
7. Hadoop & Spark ๐ฅ: Big data frameworks that help process massive datasets efficiently.
8. Scikit-learn ๐งฌ: A powerful library for machine learning in Python.
9. R ๐: A statistical programming language that is still a favorite among many analysts.
10. Docker ๐: A must-have for containerization and deploying applications.
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
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What ๐ ๐ ๐ฐ๐ผ๐ป๐ฐ๐ฒ๐ฝ๐๐ are commonly asked in ๐ฑ๐ฎ๐๐ฎ ๐๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ถ๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐?
These are fair game in interviews at ๐๐๐ฎ๐ฟ๐๐๐ฝ๐, ๐ฐ๐ผ๐ป๐๐๐น๐๐ถ๐ป๐ด & ๐น๐ฎ๐ฟ๐ด๐ฒ ๐๐ฒ๐ฐ๐ต.
๐๐๐ป๐ฑ๐ฎ๐บ๐ฒ๐ป๐๐ฎ๐น๐
- Supervised vs. Unsupervised Learning
- Overfitting and Underfitting
- Cross-validation
- Bias-Variance Tradeoff
- Accuracy vs Interpretability
- Accuracy vs Latency
๐ ๐ ๐๐น๐ด๐ผ๐ฟ๐ถ๐๐ต๐บ๐
- 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
๐ ๐ผ๐ฑ๐ฒ๐น๐ถ๐ป๐ด ๐ฆ๐๐ฒ๐ฝ๐
- 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
๐๐๐ฝ๐ฒ๐ฟ๐ฝ๐ฎ๐ฟ๐ฎ๐บ๐ฒ๐๐ฒ๐ฟ ๐ง๐๐ป๐ถ๐ป๐ด
- Grid Search
- Random Search
- Bayesian Optimization
๐ ๐ ๐๐ฎ๐๐ฒ๐
- [Capital One] Detect credit card fraudsters
- [Amazon] Forecast monthly sales
- [Airbnb] Estimate lifetime value of a guest
I have curated the best interview resources to crack Data Science Interviews
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These are fair game in interviews at ๐๐๐ฎ๐ฟ๐๐๐ฝ๐, ๐ฐ๐ผ๐ป๐๐๐น๐๐ถ๐ป๐ด & ๐น๐ฎ๐ฟ๐ด๐ฒ ๐๐ฒ๐ฐ๐ต.
๐๐๐ป๐ฑ๐ฎ๐บ๐ฒ๐ป๐๐ฎ๐น๐
- Supervised vs. Unsupervised Learning
- Overfitting and Underfitting
- Cross-validation
- Bias-Variance Tradeoff
- Accuracy vs Interpretability
- Accuracy vs Latency
๐ ๐ ๐๐น๐ด๐ผ๐ฟ๐ถ๐๐ต๐บ๐
- 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
๐ ๐ผ๐ฑ๐ฒ๐น๐ถ๐ป๐ด ๐ฆ๐๐ฒ๐ฝ๐
- 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
๐๐๐ฝ๐ฒ๐ฟ๐ฝ๐ฎ๐ฟ๐ฎ๐บ๐ฒ๐๐ฒ๐ฟ ๐ง๐๐ป๐ถ๐ป๐ด
- Grid Search
- Random Search
- Bayesian Optimization
๐ ๐ ๐๐ฎ๐๐ฒ๐
- [Capital One] Detect credit card fraudsters
- [Amazon] Forecast monthly sales
- [Airbnb] Estimate lifetime value of a guest
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content ๐๐
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