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 purposesยนยฒ.
### 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 ๐๐
Hope this helps you ๐
## 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 purposesยนยฒ.
### 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 ๐๐
Hope this helps you ๐
โค5
10 Free Machine Learning Books For 2025
๐ 1. Foundations of Machine Learning
Build a solid theoretical base before diving into machine learning algorithms.
๐ Click Here
๐ 2. Practical Machine Learning: A Beginner's Guide with Ethical Insights
Learn to implement ML with a focus on responsible and ethical AI.
๐ Open Book
๐ 3. Mathematics for Machine Learning
Master the core math concepts that power machine learning algorithms.
๐ Click Here
๐ 4. Algorithms for Decision Making
Use machine learning to make smarter decisions in complex environments.
๐ Open Book
๐ 5. Learning to Quantify
Dive into the niche field of quantification and its real-world impact.
๐ Click Here
๐ 6. Gradient Expectations
Explore predictive neural networks inspired by the mammalian brain.
๐ Open Book
๐ 7. Reinforcement Learning: An Introduction
A comprehensive intro to RL, from theory to practical applications.
๐ Click Here
๐ 8. Interpretable Machine Learning
Understand how to make machine learning models transparent and trustworthy.
๐ Open Book
๐ 9. Fairness and Machine Learning
Tackle bias and ensure fairness in AI and ML model outputs.
๐ Click Here
๐ 10. Machine Learning in Production
Learn how to deploy ML models successfully into real-world systems.
๐ Open Book
Like for more โค๏ธ
๐ 1. Foundations of Machine Learning
Build a solid theoretical base before diving into machine learning algorithms.
๐ Click Here
๐ 2. Practical Machine Learning: A Beginner's Guide with Ethical Insights
Learn to implement ML with a focus on responsible and ethical AI.
๐ Open Book
๐ 3. Mathematics for Machine Learning
Master the core math concepts that power machine learning algorithms.
๐ Click Here
๐ 4. Algorithms for Decision Making
Use machine learning to make smarter decisions in complex environments.
๐ Open Book
๐ 5. Learning to Quantify
Dive into the niche field of quantification and its real-world impact.
๐ Click Here
๐ 6. Gradient Expectations
Explore predictive neural networks inspired by the mammalian brain.
๐ Open Book
๐ 7. Reinforcement Learning: An Introduction
A comprehensive intro to RL, from theory to practical applications.
๐ Click Here
๐ 8. Interpretable Machine Learning
Understand how to make machine learning models transparent and trustworthy.
๐ Open Book
๐ 9. Fairness and Machine Learning
Tackle bias and ensure fairness in AI and ML model outputs.
๐ Click Here
๐ 10. Machine Learning in Production
Learn how to deploy ML models successfully into real-world systems.
๐ Open Book
Like for more โค๏ธ
โค5
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 โค๏ธ 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 โค๏ธ for more
โค2๐1
Power BI Scenario based Questions ๐๐
๐ 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.
๐ 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.
๐ 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.
โก 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.
๐ 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.
๐ 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.
๐ 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.
โก 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.
โค2๐1
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 ๐๐
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 ๐๐
โค2
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 :)
โค4
Here are 10 popular programming languages based on versatile, widely-used, and in-demand languages:
1. Python โ Ideal for beginners and professionals; used in web development, data analysis, AI, and more.
2. Java โ A classic language for building enterprise applications, Android apps, and large-scale systems.
3. C โ The foundation for many other languages; great for understanding low-level programming concepts.
4. C++ โ Popular for game development, competitive programming, and performance-critical applications.
5. C# โ Widely used for Windows applications, game development (Unity), and enterprise software.
6. Go (Golang) โ A modern language designed for performance and scalability, popular in cloud services.
7. Rust โ Known for its safety and performance, ideal for system-level programming.
8. Kotlin โ The preferred language for Android development with modern features.
9. Swift โ Used for developing iOS and macOS applications with simplicity and power.
10. PHP โ A staple for web development, powering many websites and applications.
โค4
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.
โค4
๐๐๐ ๐๐๐ฌ๐ ๐๐ญ๐ฎ๐๐ข๐๐ฌ ๐๐จ๐ซ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ:
Join for more: https://t.iss.one/sqlanalyst
1. Dannyโs 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: Thatโs 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. Dannyโs 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: Thatโs 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
โค1
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 ๐๐
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 ๐๐
โค2
Amazon Interview Process for Data Scientist position
๐Round 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).
๐ ๐ฅ๐ผ๐๐ป๐ฑ ๐ฎ- ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฟ๐ฒ๐ฎ๐ฑ๐๐ต:
In this round the interviewer tested my knowledge on different kinds of topics.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฏ- ๐๐ฒ๐ฝ๐๐ต ๐ฅ๐ผ๐๐ป๐ฑ:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฐ- ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐ฅ๐ผ๐๐ป๐ฑ-
This was a Python coding round, which I cleared successfully.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฑ- This was ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ where my fitment for the team got assessed.
๐๐๐ฎ๐๐ ๐ฅ๐ผ๐๐ป๐ฑ- ๐๐ฎ๐ฟ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ฟ- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if youโre targeting any Data Science role:
-> Never make up stuff & donโt 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 ๐๐
๐Round 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).
๐ ๐ฅ๐ผ๐๐ป๐ฑ ๐ฎ- ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฟ๐ฒ๐ฎ๐ฑ๐๐ต:
In this round the interviewer tested my knowledge on different kinds of topics.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฏ- ๐๐ฒ๐ฝ๐๐ต ๐ฅ๐ผ๐๐ป๐ฑ:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฐ- ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐ฅ๐ผ๐๐ป๐ฑ-
This was a Python coding round, which I cleared successfully.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฑ- This was ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ where my fitment for the team got assessed.
๐๐๐ฎ๐๐ ๐ฅ๐ผ๐๐ป๐ฑ- ๐๐ฎ๐ฟ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ฟ- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if youโre targeting any Data Science role:
-> Never make up stuff & donโt 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 ๐๐
โค3๐ฅ1
Here are some essential SQL tips for beginners ๐๐
โ Primary Key = Unique Key + Not Null constraint
โ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE โA%Aโ
โ LIKE operator is for string data type
โ COUNT(*), COUNT(1), COUNT(0) all are same
โ All aggregate functions ignore the NULL values
โ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
โ For row level filtration use WHERE and aggregate level filtration use HAVING
โ UNION ALL will include duplicates where as UNION excludes duplicates
โ If the results will not have any duplicates, use UNION ALL instead of UNION
โ We have to alias the subquery if we are using the columns in the outer select query
โ Subqueries can be used as output with NOT IN condition.
โ CTEs look better than subqueries. Performance wise both are same.
โ 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.
โ Window functions work at ROW level.
โ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
โ 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 ๐๐
โ Primary Key = Unique Key + Not Null constraint
โ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE โA%Aโ
โ LIKE operator is for string data type
โ COUNT(*), COUNT(1), COUNT(0) all are same
โ All aggregate functions ignore the NULL values
โ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
โ For row level filtration use WHERE and aggregate level filtration use HAVING
โ UNION ALL will include duplicates where as UNION excludes duplicates
โ If the results will not have any duplicates, use UNION ALL instead of UNION
โ We have to alias the subquery if we are using the columns in the outer select query
โ Subqueries can be used as output with NOT IN condition.
โ CTEs look better than subqueries. Performance wise both are same.
โ 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.
โ Window functions work at ROW level.
โ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
โ 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 ๐๐
โค2
Complete Roadmap to learn Generative AI in 2 months ๐๐
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI ๐๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI ๐๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
โค2
Artificial Intelligence isn't easy!
Itโs the cutting-edge field that enables machines to think, learn, and act like humans.
To truly master Artificial Intelligence, focus on these key areas:
0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.
1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.
2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.
3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.
4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).
5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.
6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.
7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.
8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.
9. Staying Updated with AI Research: AI is an ever-evolving fieldโstay on top of cutting-edge advancements, papers, and new algorithms.
Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.
๐ก Embrace the journey of learning and building systems that can reason, understand, and adapt.
โณ With dedication, hands-on practice, and continuous learning, youโll contribute to shaping the future of intelligent systems!
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
Itโs the cutting-edge field that enables machines to think, learn, and act like humans.
To truly master Artificial Intelligence, focus on these key areas:
0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.
1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.
2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.
3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.
4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).
5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.
6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.
7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.
8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.
9. Staying Updated with AI Research: AI is an ever-evolving fieldโstay on top of cutting-edge advancements, papers, and new algorithms.
Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.
๐ก Embrace the journey of learning and building systems that can reason, understand, and adapt.
โณ With dedication, hands-on practice, and continuous learning, youโll contribute to shaping the future of intelligent systems!
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
โค4
Recent Interview Question for Data Analyst Role
Question 1) You have two tables:
Employee:-
Columns: EID (Employee ID), ESalary (Employee Salary)
empdetails:-
Columns: EID (Employee ID), EDOB (Employee Date of Birth)
Your task is to:
1) Identify all employees whose salary (ESalary) is an odd number?
2) Retrieve the date of birth (EDOB) for these employees from the empdetails table.
How would you write a SQL query to achieve this?
SELECT e.EID, ed.EDOB
FROM (
SELECT EID
FROM Employee
WHERE ESalary % 2 <> 0
) e
JOIN empdetails ed ON e.EID = ed.EID;
Explanation of the query :-
Filter Employees with Odd Salaries:
The subquery SELECT EID FROM Employee WHERE ESalary % 2 <> 0 filters out Employee IDs (EID) where the salary (ESalary) is an odd number. The modulo operator % checks if ESalary divided by 2 leaves a remainder (<>0).
Merge with empdetails:
The main query then takes the filtered Employee IDs from the subquery and performs a join with the empdetails table using the EID column. This retrieves the date of birth (EDOB) for these employees.
Join this channel to learn everything about Data Analytics ๐
https://t.iss.one/sqlspecialist
Hope this helps you ๐
Question 1) You have two tables:
Employee:-
Columns: EID (Employee ID), ESalary (Employee Salary)
empdetails:-
Columns: EID (Employee ID), EDOB (Employee Date of Birth)
Your task is to:
1) Identify all employees whose salary (ESalary) is an odd number?
2) Retrieve the date of birth (EDOB) for these employees from the empdetails table.
How would you write a SQL query to achieve this?
SELECT e.EID, ed.EDOB
FROM (
SELECT EID
FROM Employee
WHERE ESalary % 2 <> 0
) e
JOIN empdetails ed ON e.EID = ed.EID;
Explanation of the query :-
Filter Employees with Odd Salaries:
The subquery SELECT EID FROM Employee WHERE ESalary % 2 <> 0 filters out Employee IDs (EID) where the salary (ESalary) is an odd number. The modulo operator % checks if ESalary divided by 2 leaves a remainder (<>0).
Merge with empdetails:
The main query then takes the filtered Employee IDs from the subquery and performs a join with the empdetails table using the EID column. This retrieves the date of birth (EDOB) for these employees.
Join this channel to learn everything about Data Analytics ๐
https://t.iss.one/sqlspecialist
Hope this helps you ๐
โค4
Machine Learning Algorithm:
1. Linear Regression:
- Imagine drawing a straight line on a graph to show the relationship between two things, like how the height of a plant might relate to the amount of sunlight it gets.
2. Decision Trees:
- Think of a game where you have to answer yes or no questions to find an object. It's like a flowchart helping you decide what the object is based on your answers.
3. Random Forest:
- Picture a group of friends making decisions together. Random Forest is like combining the opinions of many friends to make a more reliable decision.
4. Support Vector Machines (SVM):
- Imagine drawing a line to separate different types of things, like putting all red balls on one side and blue balls on the other, with the line in between them.
5. k-Nearest Neighbors (kNN):
- Pretend you have a collection of toys, and you want to find out which toys are similar to a new one. kNN is like asking your friends which toys are closest in looks to the new one.
6. Naive Bayes:
- Think of a detective trying to solve a mystery. Naive Bayes is like the detective making guesses based on the probability of certain clues leading to the culprit.
7. K-Means Clustering:
- Imagine sorting your toys into different groups based on their similarities, like putting all the cars in one group and all the dolls in another.
8. Hierarchical Clustering:
- Picture organizing your toys into groups, and then those groups into bigger groups. It's like creating a family tree for your toys based on their similarities.
9. Principal Component Analysis (PCA):
- Suppose you have many different measurements for your toys, and PCA helps you find the most important ones to understand and compare them easily.
10. Neural Networks (Deep Learning):
- Think of a robot brain with lots of interconnected parts. Each part helps the robot understand different aspects of things, like recognizing shapes or colors.
11. Gradient Boosting algorithms:
- Imagine you are trying to reach the top of a hill, and each time you take a step, you learn from the mistakes of the previous step to get closer to the summit. XGBoost and LightGBM are like smart ways of learning from those steps.
Share with credits: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
1. Linear Regression:
- Imagine drawing a straight line on a graph to show the relationship between two things, like how the height of a plant might relate to the amount of sunlight it gets.
2. Decision Trees:
- Think of a game where you have to answer yes or no questions to find an object. It's like a flowchart helping you decide what the object is based on your answers.
3. Random Forest:
- Picture a group of friends making decisions together. Random Forest is like combining the opinions of many friends to make a more reliable decision.
4. Support Vector Machines (SVM):
- Imagine drawing a line to separate different types of things, like putting all red balls on one side and blue balls on the other, with the line in between them.
5. k-Nearest Neighbors (kNN):
- Pretend you have a collection of toys, and you want to find out which toys are similar to a new one. kNN is like asking your friends which toys are closest in looks to the new one.
6. Naive Bayes:
- Think of a detective trying to solve a mystery. Naive Bayes is like the detective making guesses based on the probability of certain clues leading to the culprit.
7. K-Means Clustering:
- Imagine sorting your toys into different groups based on their similarities, like putting all the cars in one group and all the dolls in another.
8. Hierarchical Clustering:
- Picture organizing your toys into groups, and then those groups into bigger groups. It's like creating a family tree for your toys based on their similarities.
9. Principal Component Analysis (PCA):
- Suppose you have many different measurements for your toys, and PCA helps you find the most important ones to understand and compare them easily.
10. Neural Networks (Deep Learning):
- Think of a robot brain with lots of interconnected parts. Each part helps the robot understand different aspects of things, like recognizing shapes or colors.
11. Gradient Boosting algorithms:
- Imagine you are trying to reach the top of a hill, and each time you take a step, you learn from the mistakes of the previous step to get closer to the summit. XGBoost and LightGBM are like smart ways of learning from those steps.
Share with credits: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING ๐๐
โค3
10 AI Side Hustles You Can Start Today
โ Prompt Engineering Services โ Craft prompts for businesses using ChatGPT or Claude
โ AI-Powered Resume Writer โ Help people optimize resumes using GPT + design tools
โ YouTube Script Generator โ Offer scriptwriting using LLMs for creators & influencers
โ AI Course Creation โ Build and sell niche courses powered by AI tools (ChatGPT + Canva)
โ Copywriting & SEO Services โ Use AI to generate blog posts, ad copy, and product descriptions
โ Newsletter Curation โ Launch an AI-generated niche newsletter using curated content
โ Chatbot Development โ Build custom AI chatbots for small businesses
โ Voiceover Generator โ Convert scripts into realistic voiceovers for YouTube shorts or reels
โ AI Art & Merch Store โ Design AI-generated art and sell it on print-on-demand platforms
โ Data Labeling & AI Testing โ Offer manual or semi-automated labeling to startups training models
React if youโre thinking of monetizing your AI skills!
#aiskills
โ Prompt Engineering Services โ Craft prompts for businesses using ChatGPT or Claude
โ AI-Powered Resume Writer โ Help people optimize resumes using GPT + design tools
โ YouTube Script Generator โ Offer scriptwriting using LLMs for creators & influencers
โ AI Course Creation โ Build and sell niche courses powered by AI tools (ChatGPT + Canva)
โ Copywriting & SEO Services โ Use AI to generate blog posts, ad copy, and product descriptions
โ Newsletter Curation โ Launch an AI-generated niche newsletter using curated content
โ Chatbot Development โ Build custom AI chatbots for small businesses
โ Voiceover Generator โ Convert scripts into realistic voiceovers for YouTube shorts or reels
โ AI Art & Merch Store โ Design AI-generated art and sell it on print-on-demand platforms
โ Data Labeling & AI Testing โ Offer manual or semi-automated labeling to startups training models
React if youโre thinking of monetizing your AI skills!
#aiskills
โค4