Check out the list of top 10 Python projects on GitHub given below.
1. Magenta: Explore the artist inside you with this python project. A Google Brain’s brainchild, it leverages deep learning and reinforcement learning algorithms to create drawings, music, and other similar artistic products.
2. Photon: Designing web crawlers can be fun with the Photon project. It is a fast crawler designed for open-source intelligence tools. Photon project helps you perform data crawling functions, which include extracting data from URLs, e-mails, social media accounts, XML and pdf files, and Amazon buckets.
3. Mail Pile: Want to learn some encrypting tricks? This project on GitHub can help you learn to send and receive PGP encrypted electronic mails. Powered by Bayesian classifiers, it is capable of automatic tagging and handling huge volumes of email data, all organized in a clean web interface.
4. XS Strike: XS Strike helps you design a vulnerability to check your network’s security. It is a security suite developed to detect vulnerability attacks. XSS attacks inject malicious scripts into web pages. XSS’s features include four handwritten parsers, a payload generator, a fuzzing engine, and a fast crawler.
5. Google Images Download: It is a script that looks for keywords and phrases to optionally download the image files. All you need to do is, replicate the source code of this project to get a sense of how it works in practice.
6. Pandas Project: Pandas library is a collection of data structures that can be used for flexible data analysis and data manipulation. Compared to other libraries, its flexibility, intuitiveness, and automated data manipulation processes make it a better choice for data manipulation.
7. Xonsh: Used for designing interactive applications without the need for command-line interpreters like Unix. It is a Python-powered Shell language that commands promptly. An easily scriptable application that comes with a standard library, and various types of variables and has its own virtual environment management system.
8. Manim: The Mathematical Animation Engine, Manim, can create video explainers. Using Python 3.7, it produces animated videos, with added illustrations and display graphs. Its source code is freely available on GitHub and for tutorials and installation guides, you can refer to their 3Blue1Brown YouTube channel.
9. AI Basketball Analysis: It is an artificial intelligence application that analyses basketball shots using an object detection concept. All you need to do is upload the files or submit them as a post requests to the API. Then the OpenPose library carries out the calculations to generate the results.
10. Rebound: A great project to put Python to use in building Stackoverflow content, this tool is built on the Urwid console user interface, and solves compiler errors. Using this tool, you can learn how the Beautiful Soup package scrapes StackOverflow and how subprocesses work to find compiler errors.
1. Magenta: Explore the artist inside you with this python project. A Google Brain’s brainchild, it leverages deep learning and reinforcement learning algorithms to create drawings, music, and other similar artistic products.
2. Photon: Designing web crawlers can be fun with the Photon project. It is a fast crawler designed for open-source intelligence tools. Photon project helps you perform data crawling functions, which include extracting data from URLs, e-mails, social media accounts, XML and pdf files, and Amazon buckets.
3. Mail Pile: Want to learn some encrypting tricks? This project on GitHub can help you learn to send and receive PGP encrypted electronic mails. Powered by Bayesian classifiers, it is capable of automatic tagging and handling huge volumes of email data, all organized in a clean web interface.
4. XS Strike: XS Strike helps you design a vulnerability to check your network’s security. It is a security suite developed to detect vulnerability attacks. XSS attacks inject malicious scripts into web pages. XSS’s features include four handwritten parsers, a payload generator, a fuzzing engine, and a fast crawler.
5. Google Images Download: It is a script that looks for keywords and phrases to optionally download the image files. All you need to do is, replicate the source code of this project to get a sense of how it works in practice.
6. Pandas Project: Pandas library is a collection of data structures that can be used for flexible data analysis and data manipulation. Compared to other libraries, its flexibility, intuitiveness, and automated data manipulation processes make it a better choice for data manipulation.
7. Xonsh: Used for designing interactive applications without the need for command-line interpreters like Unix. It is a Python-powered Shell language that commands promptly. An easily scriptable application that comes with a standard library, and various types of variables and has its own virtual environment management system.
8. Manim: The Mathematical Animation Engine, Manim, can create video explainers. Using Python 3.7, it produces animated videos, with added illustrations and display graphs. Its source code is freely available on GitHub and for tutorials and installation guides, you can refer to their 3Blue1Brown YouTube channel.
9. AI Basketball Analysis: It is an artificial intelligence application that analyses basketball shots using an object detection concept. All you need to do is upload the files or submit them as a post requests to the API. Then the OpenPose library carries out the calculations to generate the results.
10. Rebound: A great project to put Python to use in building Stackoverflow content, this tool is built on the Urwid console user interface, and solves compiler errors. Using this tool, you can learn how the Beautiful Soup package scrapes StackOverflow and how subprocesses work to find compiler errors.
❤4🔥2
𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Google :- https://pdlink.in/3H2YJX7
Microsoft :- https://pdlink.in/4iq8QlM
Infosys :- https://pdlink.in/4jsHZXf
IBM :- https://pdlink.in/3QyJyqk
Cisco :- https://pdlink.in/4fYr1xO
Enroll For FREE & Get Certified 🎓
Google :- https://pdlink.in/3H2YJX7
Microsoft :- https://pdlink.in/4iq8QlM
Infosys :- https://pdlink.in/4jsHZXf
IBM :- https://pdlink.in/3QyJyqk
Cisco :- https://pdlink.in/4fYr1xO
Enroll For FREE & Get Certified 🎓
❤2
Today let's understand the fascinating world of Data Science from start.
## What is Data Science?
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. In simpler terms, data science involves obtaining, processing, and analyzing data to gain insights for various purposes¹².
### The Data Science Lifecycle
The data science lifecycle refers to the various stages a data science project typically undergoes. While each project is unique, most follow a similar structure:
1. Data Collection and Storage:
- In this initial phase, data is collected from various sources such as databases, Excel files, text files, APIs, web scraping, or real-time data streams.
- The type and volume of data collected depend on the specific problem being addressed.
- Once collected, the data is stored in an appropriate format for further processing.
2. Data Preparation:
- Often considered the most time-consuming phase, data preparation involves cleaning and transforming raw data into a suitable format for analysis.
- Tasks include handling missing or inconsistent data, removing duplicates, normalization, and data type conversions.
- The goal is to create a clean, high-quality dataset that can yield accurate and reliable analytical results.
3. Exploration and Visualization:
- During this phase, data scientists explore the prepared data to understand its patterns, characteristics, and potential anomalies.
- Techniques like statistical analysis and data visualization are used to summarize the data's main features.
- Visualization methods help convey insights effectively.
4. Model Building and Machine Learning:
- This phase involves selecting appropriate algorithms and building predictive models.
- Machine learning techniques are applied to train models on historical data and make predictions.
- Common tasks include regression, classification, clustering, and recommendation systems.
5. Model Evaluation and Deployment:
- After building models, they are evaluated using metrics such as accuracy, precision, recall, and F1-score.
- Once satisfied with the model's performance, it can be deployed for real-world use.
- Deployment may involve integrating the model into an application or system.
### Why Data Science Matters
- Business Insights: Organizations use data science to gain insights into customer behavior, market trends, and operational efficiency. This informs strategic decisions and drives business growth.
- Healthcare and Medicine: Data science helps analyze patient data, predict disease outbreaks, and optimize treatment plans. It contributes to personalized medicine and drug discovery.
- Finance and Risk Management: Financial institutions use data science for fraud detection, credit scoring, and risk assessment. It enhances decision-making and minimizes financial risks.
- Social Sciences and Public Policy: Data science aids in understanding social phenomena, predicting election outcomes, and optimizing public services.
- Technology and Innovation: Data science fuels innovations in artificial intelligence, natural language processing, and recommendation systems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
## What is Data Science?
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. In simpler terms, data science involves obtaining, processing, and analyzing data to gain insights for various purposes¹².
### The Data Science Lifecycle
The data science lifecycle refers to the various stages a data science project typically undergoes. While each project is unique, most follow a similar structure:
1. Data Collection and Storage:
- In this initial phase, data is collected from various sources such as databases, Excel files, text files, APIs, web scraping, or real-time data streams.
- The type and volume of data collected depend on the specific problem being addressed.
- Once collected, the data is stored in an appropriate format for further processing.
2. Data Preparation:
- Often considered the most time-consuming phase, data preparation involves cleaning and transforming raw data into a suitable format for analysis.
- Tasks include handling missing or inconsistent data, removing duplicates, normalization, and data type conversions.
- The goal is to create a clean, high-quality dataset that can yield accurate and reliable analytical results.
3. Exploration and Visualization:
- During this phase, data scientists explore the prepared data to understand its patterns, characteristics, and potential anomalies.
- Techniques like statistical analysis and data visualization are used to summarize the data's main features.
- Visualization methods help convey insights effectively.
4. Model Building and Machine Learning:
- This phase involves selecting appropriate algorithms and building predictive models.
- Machine learning techniques are applied to train models on historical data and make predictions.
- Common tasks include regression, classification, clustering, and recommendation systems.
5. Model Evaluation and Deployment:
- After building models, they are evaluated using metrics such as accuracy, precision, recall, and F1-score.
- Once satisfied with the model's performance, it can be deployed for real-world use.
- Deployment may involve integrating the model into an application or system.
### Why Data Science Matters
- Business Insights: Organizations use data science to gain insights into customer behavior, market trends, and operational efficiency. This informs strategic decisions and drives business growth.
- Healthcare and Medicine: Data science helps analyze patient data, predict disease outbreaks, and optimize treatment plans. It contributes to personalized medicine and drug discovery.
- Finance and Risk Management: Financial institutions use data science for fraud detection, credit scoring, and risk assessment. It enhances decision-making and minimizes financial risks.
- Social Sciences and Public Policy: Data science aids in understanding social phenomena, predicting election outcomes, and optimizing public services.
- Technology and Innovation: Data science fuels innovations in artificial intelligence, natural language processing, and recommendation systems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
❤5
10 Free Machine Learning Books For 2025
📘 1. Foundations of Machine Learning
Build a solid theoretical base before diving into machine learning algorithms.
🔘 Click Here
📙 2. Practical Machine Learning: A Beginner's Guide with Ethical Insights
Learn to implement ML with a focus on responsible and ethical AI.
🔘 Open Book
📗 3. Mathematics for Machine Learning
Master the core math concepts that power machine learning algorithms.
🔘 Click Here
📕 4. Algorithms for Decision Making
Use machine learning to make smarter decisions in complex environments.
🔘 Open Book
📘 5. Learning to Quantify
Dive into the niche field of quantification and its real-world impact.
🔘 Click Here
📙 6. Gradient Expectations
Explore predictive neural networks inspired by the mammalian brain.
🔘 Open Book
📗 7. Reinforcement Learning: An Introduction
A comprehensive intro to RL, from theory to practical applications.
🔘 Click Here
📕 8. Interpretable Machine Learning
Understand how to make machine learning models transparent and trustworthy.
🔘 Open Book
📘 9. Fairness and Machine Learning
Tackle bias and ensure fairness in AI and ML model outputs.
🔘 Click Here
📙 10. Machine Learning in Production
Learn how to deploy ML models successfully into real-world systems.
🔘 Open Book
Like for more ❤️
📘 1. Foundations of Machine Learning
Build a solid theoretical base before diving into machine learning algorithms.
🔘 Click Here
📙 2. Practical Machine Learning: A Beginner's Guide with Ethical Insights
Learn to implement ML with a focus on responsible and ethical AI.
🔘 Open Book
📗 3. Mathematics for Machine Learning
Master the core math concepts that power machine learning algorithms.
🔘 Click Here
📕 4. Algorithms for Decision Making
Use machine learning to make smarter decisions in complex environments.
🔘 Open Book
📘 5. Learning to Quantify
Dive into the niche field of quantification and its real-world impact.
🔘 Click Here
📙 6. Gradient Expectations
Explore predictive neural networks inspired by the mammalian brain.
🔘 Open Book
📗 7. Reinforcement Learning: An Introduction
A comprehensive intro to RL, from theory to practical applications.
🔘 Click Here
📕 8. Interpretable Machine Learning
Understand how to make machine learning models transparent and trustworthy.
🔘 Open Book
📘 9. Fairness and Machine Learning
Tackle bias and ensure fairness in AI and ML model outputs.
🔘 Click Here
📙 10. Machine Learning in Production
Learn how to deploy ML models successfully into real-world systems.
🔘 Open Book
Like for more ❤️
❤5
Data Analytics project ideas to build your portfolio in 2025:
1. Sales Data Analysis Dashboard
Analyze sales trends, seasonal patterns, and product performance.
Use Power BI, Tableau, or Python (Dash/Plotly) for visualization.
2. Customer Segmentation
Use clustering (K-means, hierarchical) on customer data to identify groups.
Provide actionable marketing insights.
3. Social Media Sentiment Analysis
Analyze tweets or reviews using NLP to gauge public sentiment.
Visualize positive, negative, and neutral trends over time.
4. Churn Prediction Model
Analyze customer data to predict who might leave a service.
Use logistic regression, decision trees, or random forest.
5. Financial Data Analysis
Study stock prices, moving averages, and volatility.
Create an interactive dashboard with key metrics.
6. Healthcare Analytics
Analyze patient data for disease trends or hospital resource usage.
Use visualization to highlight key findings.
7. Website Traffic Analysis
Use Google Analytics data to identify user behavior patterns.
Suggest improvements for user engagement and conversion.
8. Employee Attrition Analysis
Analyze HR data to find factors leading to employee turnover.
Use statistical tests and visualization.
React ❤️ for more
1. Sales Data Analysis Dashboard
Analyze sales trends, seasonal patterns, and product performance.
Use Power BI, Tableau, or Python (Dash/Plotly) for visualization.
2. Customer Segmentation
Use clustering (K-means, hierarchical) on customer data to identify groups.
Provide actionable marketing insights.
3. Social Media Sentiment Analysis
Analyze tweets or reviews using NLP to gauge public sentiment.
Visualize positive, negative, and neutral trends over time.
4. Churn Prediction Model
Analyze customer data to predict who might leave a service.
Use logistic regression, decision trees, or random forest.
5. Financial Data Analysis
Study stock prices, moving averages, and volatility.
Create an interactive dashboard with key metrics.
6. Healthcare Analytics
Analyze patient data for disease trends or hospital resource usage.
Use visualization to highlight key findings.
7. Website Traffic Analysis
Use Google Analytics data to identify user behavior patterns.
Suggest improvements for user engagement and conversion.
8. Employee Attrition Analysis
Analyze HR data to find factors leading to employee turnover.
Use statistical tests and visualization.
React ❤️ for more
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Power BI Scenario based Questions 👇👇
📈 Scenario 1:Question: Imagine you need to visualize year-over-year growth in product sales. What approach would you take to calculate and present this information effectively in Power BI?
Answer: To visualize year-over-year growth in product sales, I would first calculate the sales for each product for the current year and the previous year using DAX measures in Power BI. Then, I would create a line chart visual where the x-axis represents the months or quarters, and the y-axis represents the sales amount. I would plot two lines on the chart, one for the current year's sales and one for the previous year's sales, allowing stakeholders to easily compare the growth trends over time.
🔄 Scenario 2: Question: You're working with a dataset that requires extensive data cleaning and transformation before analysis. Describe your process for cleaning and preparing the data in Power BI, ensuring accuracy and efficiency.
Answer: For cleaning and preparing the dataset in Power BI, I would start by identifying and addressing missing or duplicate values, outliers, and inconsistencies in data formats. I would use Power Query Editor to perform data cleaning operations such as removing null values, renaming columns, and applying transformations like data type conversion and standardization. Additionally, I would create calculated columns or measures as needed to derive new insights from the cleaned data.
🔌 Scenario 3: Question: Your organization wants to incorporate real-time data updates into their Power BI reports. How would you set up and manage live data connections in Power BI to ensure timely insights?
Answer: To incorporate real-time data updates into Power BI reports, I would utilize Power BI's streaming datasets feature. I would set up a data streaming connection to the source system, such as a database or API, and configure the dataset to receive real-time data updates at specified intervals. Then, I would design reports and visuals based on the streaming dataset, enabling stakeholders to view and analyze the latest data as it is updated in real-time.
⚡ Scenario 4: Question: You've noticed that your Power BI reports are taking longer to load and refresh than usual. How would you diagnose and address performance issues to optimize report performance?
Answer: If Power BI reports are experiencing performance issues, I would first identify potential bottlenecks by analyzing factors such as data volume, query complexity, and visual design. Then, I would optimize report performance by applying techniques such as data model optimization, query optimization, and visualization best practices.
📈 Scenario 1:Question: Imagine you need to visualize year-over-year growth in product sales. What approach would you take to calculate and present this information effectively in Power BI?
Answer: To visualize year-over-year growth in product sales, I would first calculate the sales for each product for the current year and the previous year using DAX measures in Power BI. Then, I would create a line chart visual where the x-axis represents the months or quarters, and the y-axis represents the sales amount. I would plot two lines on the chart, one for the current year's sales and one for the previous year's sales, allowing stakeholders to easily compare the growth trends over time.
🔄 Scenario 2: Question: You're working with a dataset that requires extensive data cleaning and transformation before analysis. Describe your process for cleaning and preparing the data in Power BI, ensuring accuracy and efficiency.
Answer: For cleaning and preparing the dataset in Power BI, I would start by identifying and addressing missing or duplicate values, outliers, and inconsistencies in data formats. I would use Power Query Editor to perform data cleaning operations such as removing null values, renaming columns, and applying transformations like data type conversion and standardization. Additionally, I would create calculated columns or measures as needed to derive new insights from the cleaned data.
🔌 Scenario 3: Question: Your organization wants to incorporate real-time data updates into their Power BI reports. How would you set up and manage live data connections in Power BI to ensure timely insights?
Answer: To incorporate real-time data updates into Power BI reports, I would utilize Power BI's streaming datasets feature. I would set up a data streaming connection to the source system, such as a database or API, and configure the dataset to receive real-time data updates at specified intervals. Then, I would design reports and visuals based on the streaming dataset, enabling stakeholders to view and analyze the latest data as it is updated in real-time.
⚡ Scenario 4: Question: You've noticed that your Power BI reports are taking longer to load and refresh than usual. How would you diagnose and address performance issues to optimize report performance?
Answer: If Power BI reports are experiencing performance issues, I would first identify potential bottlenecks by analyzing factors such as data volume, query complexity, and visual design. Then, I would optimize report performance by applying techniques such as data model optimization, query optimization, and visualization best practices.
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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
BEST RESOURCES TO LEARN DATA SCIENCE AND MACHINE LEARNING FOR FREE
https://developers.google.com/machine-learning/crash-course
https://www.kaggle.com/learn/overview
https://forums.fast.ai/t/recommended-python-learning-resources/26888
https://www.fast.ai/
https://imp.i115008.net/JrBjZR
https://ern.li/OP/1qvkxbfaxqj
ENJOY LEARNING 👍👍
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
BEST RESOURCES TO LEARN DATA SCIENCE AND MACHINE LEARNING FOR FREE
https://developers.google.com/machine-learning/crash-course
https://www.kaggle.com/learn/overview
https://forums.fast.ai/t/recommended-python-learning-resources/26888
https://www.fast.ai/
https://imp.i115008.net/JrBjZR
https://ern.li/OP/1qvkxbfaxqj
ENJOY LEARNING 👍👍
❤2
Essential SQL Topics for Data Analysts
SQL for Data Analysts Free Resources -> https://t.iss.one/sqlanalyst
- Basic Queries: SELECT, FROM, WHERE clauses.
- Sorting and Filtering: ORDER BY, GROUP BY, HAVING.
- Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN.
- Aggregation Functions: COUNT, SUM, AVG, MIN, MAX.
- Subqueries: Embedding queries within queries.
- Data Modification: INSERT, UPDATE, DELETE.
- Indexes: Optimizing query performance.
- Normalization: Ensuring efficient database design.
- Views: Creating virtual tables for simplified queries.
- Understanding Database Relationships: One-to-One, One-to-Many, Many-to-Many.
Window functions are also important for data analysts. They allow for advanced data analysis and manipulation within specified subsets of data. Commonly used window functions include:
- ROW_NUMBER(): Assigns a unique number to each row based on a specified order.
- RANK() and DENSE_RANK(): Rank data based on a specified order, handling ties differently.
- LAG() and LEAD(): Access data from preceding or following rows within a partition.
- SUM(), AVG(), MIN(), MAX(): Aggregations over a defined window of rows.
Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
SQL for Data Analysts Free Resources -> https://t.iss.one/sqlanalyst
- Basic Queries: SELECT, FROM, WHERE clauses.
- Sorting and Filtering: ORDER BY, GROUP BY, HAVING.
- Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN.
- Aggregation Functions: COUNT, SUM, AVG, MIN, MAX.
- Subqueries: Embedding queries within queries.
- Data Modification: INSERT, UPDATE, DELETE.
- Indexes: Optimizing query performance.
- Normalization: Ensuring efficient database design.
- Views: Creating virtual tables for simplified queries.
- Understanding Database Relationships: One-to-One, One-to-Many, Many-to-Many.
Window functions are also important for data analysts. They allow for advanced data analysis and manipulation within specified subsets of data. Commonly used window functions include:
- ROW_NUMBER(): Assigns a unique number to each row based on a specified order.
- RANK() and DENSE_RANK(): Rank data based on a specified order, handling ties differently.
- LAG() and LEAD(): Access data from preceding or following rows within a partition.
- SUM(), AVG(), MIN(), MAX(): Aggregations over a defined window of rows.
Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤4
Here are 10 popular programming languages based on versatile, widely-used, and in-demand languages:
1. Python – Ideal for beginners and professionals; used in web development, data analysis, AI, and more.
2. Java – A classic language for building enterprise applications, Android apps, and large-scale systems.
3. C – The foundation for many other languages; great for understanding low-level programming concepts.
4. C++ – Popular for game development, competitive programming, and performance-critical applications.
5. C# – Widely used for Windows applications, game development (Unity), and enterprise software.
6. Go (Golang) – A modern language designed for performance and scalability, popular in cloud services.
7. Rust – Known for its safety and performance, ideal for system-level programming.
8. Kotlin – The preferred language for Android development with modern features.
9. Swift – Used for developing iOS and macOS applications with simplicity and power.
10. PHP – A staple for web development, powering many websites and applications.
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Creating a data science portfolio is a great way to showcase your skills and experience to potential employers. Here are some steps to help you create a strong data science portfolio:
1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions.
2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis.
3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization.
4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs.
5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis.
6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented.
7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail.
8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills.
By following these steps, you can create a comprehensive and visually appealing data science portfolio that will impress potential employers and help you stand out in the competitive job market.
1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions.
2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis.
3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization.
4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs.
5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis.
6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented.
7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail.
8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills.
By following these steps, you can create a comprehensive and visually appealing data science portfolio that will impress potential employers and help you stand out in the competitive job market.
❤4
𝐒𝐐𝐋 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰:
Join for more: https://t.iss.one/sqlanalyst
1. Danny’s Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/
2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/
3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT
4. Data Bank: That’s money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv
5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf
6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG
7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7
8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8
Join for more: https://t.iss.one/sqlanalyst
1. Danny’s Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/
2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/
3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT
4. Data Bank: That’s money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv
5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf
6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG
7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7
8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8
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I have curated the list of useful Coding Channels on WhatsApp:
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javascript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING 👍👍
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javascript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING 👍👍
❤2
Amazon Interview Process for Data Scientist position
📍Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
📍 𝗥𝗼𝘂𝗻𝗱 𝟮- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵:
In this round the interviewer tested my knowledge on different kinds of topics.
📍𝗥𝗼𝘂𝗻𝗱 𝟯- 𝗗𝗲𝗽𝘁𝗵 𝗥𝗼𝘂𝗻𝗱:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
📍𝗥𝗼𝘂𝗻𝗱 𝟰- 𝗖𝗼𝗱𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱-
This was a Python coding round, which I cleared successfully.
📍𝗥𝗼𝘂𝗻𝗱 𝟱- This was 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 where my fitment for the team got assessed.
📍𝗟𝗮𝘀𝘁 𝗥𝗼𝘂𝗻𝗱- 𝗕𝗮𝗿 𝗥𝗮𝗶𝘀𝗲𝗿- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if you’re targeting any Data Science role:
-> Never make up stuff & don’t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
📍Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
📍 𝗥𝗼𝘂𝗻𝗱 𝟮- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵:
In this round the interviewer tested my knowledge on different kinds of topics.
📍𝗥𝗼𝘂𝗻𝗱 𝟯- 𝗗𝗲𝗽𝘁𝗵 𝗥𝗼𝘂𝗻𝗱:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
📍𝗥𝗼𝘂𝗻𝗱 𝟰- 𝗖𝗼𝗱𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱-
This was a Python coding round, which I cleared successfully.
📍𝗥𝗼𝘂𝗻𝗱 𝟱- This was 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 where my fitment for the team got assessed.
📍𝗟𝗮𝘀𝘁 𝗥𝗼𝘂𝗻𝗱- 𝗕𝗮𝗿 𝗥𝗮𝗶𝘀𝗲𝗿- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if you’re targeting any Data Science role:
-> Never make up stuff & don’t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
❤3🔥1
Here are some essential SQL tips for beginners 👇👇
◆ Primary Key = Unique Key + Not Null constraint
◆ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE ‘A%A’
◆ LIKE operator is for string data type
◆ COUNT(*), COUNT(1), COUNT(0) all are same
◆ All aggregate functions ignore the NULL values
◆ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
◆ For row level filtration use WHERE and aggregate level filtration use HAVING
◆ UNION ALL will include duplicates where as UNION excludes duplicates
◆ If the results will not have any duplicates, use UNION ALL instead of UNION
◆ We have to alias the subquery if we are using the columns in the outer select query
◆ Subqueries can be used as output with NOT IN condition.
◆ CTEs look better than subqueries. Performance wise both are same.
◆ When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN.
◆ Window functions work at ROW level.
◆ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
◆ EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned.
Like for more 😄😄
◆ Primary Key = Unique Key + Not Null constraint
◆ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE ‘A%A’
◆ LIKE operator is for string data type
◆ COUNT(*), COUNT(1), COUNT(0) all are same
◆ All aggregate functions ignore the NULL values
◆ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
◆ For row level filtration use WHERE and aggregate level filtration use HAVING
◆ UNION ALL will include duplicates where as UNION excludes duplicates
◆ If the results will not have any duplicates, use UNION ALL instead of UNION
◆ We have to alias the subquery if we are using the columns in the outer select query
◆ Subqueries can be used as output with NOT IN condition.
◆ CTEs look better than subqueries. Performance wise both are same.
◆ When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN.
◆ Window functions work at ROW level.
◆ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
◆ EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned.
Like for more 😄😄
❤2