Many data scientists don't know how to push ML models to production. Here's the recipe ๐
๐๐ฒ๐ ๐๐ป๐ด๐ฟ๐ฒ๐ฑ๐ถ๐ฒ๐ป๐๐
๐น ๐ง๐ฟ๐ฎ๐ถ๐ป / ๐ง๐ฒ๐๐ ๐๐ฎ๐๐ฎ๐๐ฒ๐ - Ensure Test is representative of Online data
๐น ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ - Generate features in real-time
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ข๐ฏ๐ท๐ฒ๐ฐ๐ - Trained SkLearn or Tensorflow Model
๐น ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ผ๐ฑ๐ฒ ๐ฅ๐ฒ๐ฝ๐ผ - Save model project code to Github
๐น ๐๐ฃ๐ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ - Use FastAPI or Flask to build a model API
๐น ๐๐ผ๐ฐ๐ธ๐ฒ๐ฟ - Containerize the ML model API
๐น ๐ฅ๐ฒ๐บ๐ผ๐๐ฒ ๐ฆ๐ฒ๐ฟ๐๐ฒ๐ฟ - Choose a cloud service; e.g. AWS sagemaker
๐น ๐จ๐ป๐ถ๐ ๐ง๐ฒ๐๐๐ - Test inputs & outputs of functions and APIs
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด - Evidently AI, a simple, open-source for ML monitoring
๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐ฑ๐๐ฟ๐ฒ
๐ฆ๐๐ฒ๐ฝ ๐ญ - ๐๐ฎ๐๐ฎ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป & ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด
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.
๐ฆ๐๐ฒ๐ฝ ๐ฎ - ๐ ๐ผ๐ฑ๐ฒ๐น ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐
Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.
๐ฆ๐๐ฒ๐ฝ ๐ฏ - ๐๐ฃ๐ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐ & ๐๐ผ๐ป๐๐ฎ๐ถ๐ป๐ฒ๐ฟ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป
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
๐ฆ๐๐ฒ๐ฝ ๐ฐ - ๐ง๐ฒ๐๐๐ถ๐ป๐ด & ๐๐ฒ๐ฝ๐น๐ผ๐๐บ๐ฒ๐ป๐
Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.
๐ฆ๐๐ฒ๐ฝ ๐ฑ - ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด
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.
Data Science Resources
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
๐๐ฒ๐ ๐๐ป๐ด๐ฟ๐ฒ๐ฑ๐ถ๐ฒ๐ป๐๐
๐น ๐ง๐ฟ๐ฎ๐ถ๐ป / ๐ง๐ฒ๐๐ ๐๐ฎ๐๐ฎ๐๐ฒ๐ - Ensure Test is representative of Online data
๐น ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ - Generate features in real-time
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ข๐ฏ๐ท๐ฒ๐ฐ๐ - Trained SkLearn or Tensorflow Model
๐น ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ผ๐ฑ๐ฒ ๐ฅ๐ฒ๐ฝ๐ผ - Save model project code to Github
๐น ๐๐ฃ๐ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ - Use FastAPI or Flask to build a model API
๐น ๐๐ผ๐ฐ๐ธ๐ฒ๐ฟ - Containerize the ML model API
๐น ๐ฅ๐ฒ๐บ๐ผ๐๐ฒ ๐ฆ๐ฒ๐ฟ๐๐ฒ๐ฟ - Choose a cloud service; e.g. AWS sagemaker
๐น ๐จ๐ป๐ถ๐ ๐ง๐ฒ๐๐๐ - Test inputs & outputs of functions and APIs
๐น ๐ ๐ผ๐ฑ๐ฒ๐น ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด - Evidently AI, a simple, open-source for ML monitoring
๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐ฑ๐๐ฟ๐ฒ
๐ฆ๐๐ฒ๐ฝ ๐ญ - ๐๐ฎ๐๐ฎ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป & ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด
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.
๐ฆ๐๐ฒ๐ฝ ๐ฎ - ๐ ๐ผ๐ฑ๐ฒ๐น ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐
Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.
๐ฆ๐๐ฒ๐ฝ ๐ฏ - ๐๐ฃ๐ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐ & ๐๐ผ๐ป๐๐ฎ๐ถ๐ป๐ฒ๐ฟ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป
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
๐ฆ๐๐ฒ๐ฝ ๐ฐ - ๐ง๐ฒ๐๐๐ถ๐ป๐ด & ๐๐ฒ๐ฝ๐น๐ผ๐๐บ๐ฒ๐ป๐
Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.
๐ฆ๐๐ฒ๐ฝ ๐ฑ - ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด
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.
Data Science Resources
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
๐12โค1
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 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.iss.one/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
Like for more ๐
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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The Average Salary Of a Data Scientist is 14LPA
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We help you master the required skills.
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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
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like for more ๐
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
Like for more ๐
๐13โค2
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
Like if you need similar content ๐๐
๐6โค1
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
๐๐
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content ๐๐
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
๐3โค2