Top 10 Websites for Data Science 👇
1. Flowing Data (https://flowingdata.com)
2. Data Simplifier (https://www.datasimplifier.com)
3. R-Bloggers (https://www.r-bloggers.com)
4. Edwin Chen (https://blog.echen.me)
5. Hunch (https://hunch.net)
6. KDNuggets (https://www.kdnuggets.com)
7. Data Science Central (https://www.datasciencecentral.com)
8. Kaggle Competitions (https://www.kaggle.com/competitions)
9. Simply Statistics (https://simplystatistics.org)
10. FastML (https://fastml.com)
1. Flowing Data (https://flowingdata.com)
2. Data Simplifier (https://www.datasimplifier.com)
3. R-Bloggers (https://www.r-bloggers.com)
4. Edwin Chen (https://blog.echen.me)
5. Hunch (https://hunch.net)
6. KDNuggets (https://www.kdnuggets.com)
7. Data Science Central (https://www.datasciencecentral.com)
8. Kaggle Competitions (https://www.kaggle.com/competitions)
9. Simply Statistics (https://simplystatistics.org)
10. FastML (https://fastml.com)
Use these ChatGPT Prompts To 10X your Interview Chances
1. Company research
Prompt: "I have an interview with [company] for the position of [job].
Please summarize the company's mission, its main products or services, and its recent news or achievements by analyzing its website [website link] and any recent press release."
2. Resume Optimization
Prompt: "Review my current attached resume and suggest improvements tailored to applying for a [job] at [company]. Highlight gaps in my experience and recommend ways to fill them through online courses or projects."
3. Writing the cover letter
Prompt: "Based on the job description for [job title] at [company], generate a cover letter that highlights my relevant experience, skills, and why I am passionate about working for [company]."
4. Interview preparation
Prompt: "For [job title] at [company], what are some industry-specific challenges or trends I should be aware of? How can I demonstrate my understanding or propose possible solutions during the interview?"
5. Behavioral Interview Questions
Prompt: "Create a set of behavioural interview questions relevant to the [job] role at [company]. Include a brief guide on how to structure answers using the STAR (Situation, Task, Action, Result) method, tailored to my needs." experiences."
6. Craft Your Resume Perfectly
Prompt: "I want to tailor my resume to specific job descriptions so I get shortlisted more often. Analyze this job posting for [insert job title], extract the most important keywords and skills, and help me rewrite my resume to match it perfectly while maintaining authenticity."
7. Data-Driven Job Search
Prompt: "I want to use data and hiring trends to increase my chances of landing a high-paying job in [insert industry]. Provide me with data-backed job search strategies, salary benchmarks, and negotiation tips based on market trends."
8. Network Like a Pro
Prompt: "I want to build relationships with influential professionals in [insert industry] to increase my chances of getting a job.
Give me a step-by-step networking strategy, including outreach messages, follow-ups, and ways to provide value to them."
9. Craft the Perfect Elevator Pitch
Prompt: "I need a powerful 30-second elevator pitch that instantly impresses interviewers for [insert job title]. Craft a clear, concise, and compelling pitch that highlights my skills, experience, and what makes me unique."
10. The 30-Day Job Search Plan
Prompt: "I need to land a high-paying job in [insert industry] within 30 days. Create a daily action plan that includes networking, outreach, applications, and personal branding strategies to maximize my chances of success."
#aiprompts #jobs
1. Company research
Prompt: "I have an interview with [company] for the position of [job].
Please summarize the company's mission, its main products or services, and its recent news or achievements by analyzing its website [website link] and any recent press release."
2. Resume Optimization
Prompt: "Review my current attached resume and suggest improvements tailored to applying for a [job] at [company]. Highlight gaps in my experience and recommend ways to fill them through online courses or projects."
3. Writing the cover letter
Prompt: "Based on the job description for [job title] at [company], generate a cover letter that highlights my relevant experience, skills, and why I am passionate about working for [company]."
4. Interview preparation
Prompt: "For [job title] at [company], what are some industry-specific challenges or trends I should be aware of? How can I demonstrate my understanding or propose possible solutions during the interview?"
5. Behavioral Interview Questions
Prompt: "Create a set of behavioural interview questions relevant to the [job] role at [company]. Include a brief guide on how to structure answers using the STAR (Situation, Task, Action, Result) method, tailored to my needs." experiences."
6. Craft Your Resume Perfectly
Prompt: "I want to tailor my resume to specific job descriptions so I get shortlisted more often. Analyze this job posting for [insert job title], extract the most important keywords and skills, and help me rewrite my resume to match it perfectly while maintaining authenticity."
7. Data-Driven Job Search
Prompt: "I want to use data and hiring trends to increase my chances of landing a high-paying job in [insert industry]. Provide me with data-backed job search strategies, salary benchmarks, and negotiation tips based on market trends."
8. Network Like a Pro
Prompt: "I want to build relationships with influential professionals in [insert industry] to increase my chances of getting a job.
Give me a step-by-step networking strategy, including outreach messages, follow-ups, and ways to provide value to them."
9. Craft the Perfect Elevator Pitch
Prompt: "I need a powerful 30-second elevator pitch that instantly impresses interviewers for [insert job title]. Craft a clear, concise, and compelling pitch that highlights my skills, experience, and what makes me unique."
10. The 30-Day Job Search Plan
Prompt: "I need to land a high-paying job in [insert industry] within 30 days. Create a daily action plan that includes networking, outreach, applications, and personal branding strategies to maximize my chances of success."
#aiprompts #jobs
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7 Free Kaggle Micro-Courses for Data Science Beginners with Certification
Python
https://www.kaggle.com/learn/python
Pandas
https://www.kaggle.com/learn/pandas
Data visualization
https://www.kaggle.com/learn/data-visualization
Intro to sql
https://www.kaggle.com/learn/intro-to-sql
Advanced Sql
https://www.kaggle.com/learn/advanced-sql
Intro to ML
https://www.kaggle.com/learn/intro-to-machine-learning
Advanced ML
https://www.kaggle.com/learn/intermediate-machine-learning
#datascienceprojects #kaggle
Python
https://www.kaggle.com/learn/python
Pandas
https://www.kaggle.com/learn/pandas
Data visualization
https://www.kaggle.com/learn/data-visualization
Intro to sql
https://www.kaggle.com/learn/intro-to-sql
Advanced Sql
https://www.kaggle.com/learn/advanced-sql
Intro to ML
https://www.kaggle.com/learn/intro-to-machine-learning
Advanced ML
https://www.kaggle.com/learn/intermediate-machine-learning
#datascienceprojects #kaggle
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Data Science – Essential Topics 🚀
1️⃣ Data Collection & Processing
Web scraping, APIs, and databases
Handling missing data, duplicates, and outliers
Data transformation and normalization
2️⃣ Exploratory Data Analysis (EDA)
Descriptive statistics (mean, median, variance, correlation)
Data visualization (bar charts, scatter plots, heatmaps)
Identifying patterns and trends
3️⃣ Feature Engineering & Selection
Encoding categorical variables
Scaling and normalization techniques
Handling multicollinearity and dimensionality reduction
4️⃣ Machine Learning Model Building
Supervised learning (classification, regression)
Unsupervised learning (clustering, anomaly detection)
Model selection and hyperparameter tuning
5️⃣ Model Evaluation & Performance Metrics
Accuracy, precision, recall, F1-score, ROC-AUC
Cross-validation and bias-variance tradeoff
Confusion matrix and error analysis
6️⃣ Deep Learning & Neural Networks
Basics of artificial neural networks (ANNs)
Convolutional neural networks (CNNs) for image processing
Recurrent neural networks (RNNs) for sequential data
7️⃣ Big Data & Cloud Computing
Working with large datasets (Hadoop, Spark)
Cloud platforms (AWS, Google Cloud, Azure)
Scalable data pipelines and automation
8️⃣ Model Deployment & Automation
Model deployment with Flask, FastAPI, or Streamlit
Monitoring and maintaining machine learning models
Automating data workflows with Airflow
Join our WhatsApp channel for more resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
1️⃣ Data Collection & Processing
Web scraping, APIs, and databases
Handling missing data, duplicates, and outliers
Data transformation and normalization
2️⃣ Exploratory Data Analysis (EDA)
Descriptive statistics (mean, median, variance, correlation)
Data visualization (bar charts, scatter plots, heatmaps)
Identifying patterns and trends
3️⃣ Feature Engineering & Selection
Encoding categorical variables
Scaling and normalization techniques
Handling multicollinearity and dimensionality reduction
4️⃣ Machine Learning Model Building
Supervised learning (classification, regression)
Unsupervised learning (clustering, anomaly detection)
Model selection and hyperparameter tuning
5️⃣ Model Evaluation & Performance Metrics
Accuracy, precision, recall, F1-score, ROC-AUC
Cross-validation and bias-variance tradeoff
Confusion matrix and error analysis
6️⃣ Deep Learning & Neural Networks
Basics of artificial neural networks (ANNs)
Convolutional neural networks (CNNs) for image processing
Recurrent neural networks (RNNs) for sequential data
7️⃣ Big Data & Cloud Computing
Working with large datasets (Hadoop, Spark)
Cloud platforms (AWS, Google Cloud, Azure)
Scalable data pipelines and automation
8️⃣ Model Deployment & Automation
Model deployment with Flask, FastAPI, or Streamlit
Monitoring and maintaining machine learning models
Automating data workflows with Airflow
Join our WhatsApp channel for more resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
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If you're a data science beginner, Python is the best programming language to get started.
Here are 7 Python libraries for data science you need to know if you want to learn:
- Data analysis
- Data visualization
- Machine learning
- Deep learning
NumPy
NumPy is a library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.
Pandas
Widely used library for data manipulation and analysis, offering data structures like DataFrame and Series that simplify handling of structured data and performing tasks such as filtering, grouping, and merging.
Matplotlib
Powerful plotting library for creating static, interactive, and animated visualizations in Python, enabling data scientists to generate a wide variety of plots, charts, and graphs to explore and communicate data effectively.
Scikit-learn
Comprehensive machine learning library that includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection, as well as utilities for data preprocessing and evaluation.
Seaborn
Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics, making it easier to generate complex visualizations with minimal code.
TensorFlow or PyTorch
TensorFlow, Keras, or PyTorch are three prominent deep learning frameworks utilized by data scientists to construct, train, and deploy neural networks for various applications, each offering distinct advantages and capabilities tailored to different preferences and requirements.
SciPy
Collection of mathematical algorithms and functions built on top of NumPy, providing additional capabilities for optimization, integration, interpolation, signal processing, linear algebra, and more, which are commonly used in scientific computing and data analysis workflows.
Enjoy 😄👍
Here are 7 Python libraries for data science you need to know if you want to learn:
- Data analysis
- Data visualization
- Machine learning
- Deep learning
NumPy
NumPy is a library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.
Pandas
Widely used library for data manipulation and analysis, offering data structures like DataFrame and Series that simplify handling of structured data and performing tasks such as filtering, grouping, and merging.
Matplotlib
Powerful plotting library for creating static, interactive, and animated visualizations in Python, enabling data scientists to generate a wide variety of plots, charts, and graphs to explore and communicate data effectively.
Scikit-learn
Comprehensive machine learning library that includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection, as well as utilities for data preprocessing and evaluation.
Seaborn
Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics, making it easier to generate complex visualizations with minimal code.
TensorFlow or PyTorch
TensorFlow, Keras, or PyTorch are three prominent deep learning frameworks utilized by data scientists to construct, train, and deploy neural networks for various applications, each offering distinct advantages and capabilities tailored to different preferences and requirements.
SciPy
Collection of mathematical algorithms and functions built on top of NumPy, providing additional capabilities for optimization, integration, interpolation, signal processing, linear algebra, and more, which are commonly used in scientific computing and data analysis workflows.
Enjoy 😄👍
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Step-by-Step Approach to Learn Data Science
➊ Learn a Programming Language → Python or R
↓
➋ Fundamentals → Statistics, Probability, Linear Algebra
↓
➌ Data Handling & Processing → Pandas, NumPy
↓
➍ Data Visualization → Matplotlib, Seaborn, Plotly
↓
➎ Exploratory Data Analysis (EDA) → Missing Values, Outliers, Feature Engineering
↓
➏ Machine Learning Basics → Supervised vs Unsupervised Learning
↓
➐ Model Building & Evaluation → Scikit-Learn, Cross-Validation, Metrics
↓
➑ Advanced Topics → Deep Learning, NLP, Time Series Analysis
Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
➊ Learn a Programming Language → Python or R
↓
➋ Fundamentals → Statistics, Probability, Linear Algebra
↓
➌ Data Handling & Processing → Pandas, NumPy
↓
➍ Data Visualization → Matplotlib, Seaborn, Plotly
↓
➎ Exploratory Data Analysis (EDA) → Missing Values, Outliers, Feature Engineering
↓
➏ Machine Learning Basics → Supervised vs Unsupervised Learning
↓
➐ Model Building & Evaluation → Scikit-Learn, Cross-Validation, Metrics
↓
➑ Advanced Topics → Deep Learning, NLP, Time Series Analysis
Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
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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.
Like if you need similar content 😄👍
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.
Like if you need similar content 😄👍
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