📚👀🚀Preparing for a Data science/ Data Analytics interview can be challenging, but with the right strategy, you can enhance your chances of success. Here are some key tips to assist you in getting ready:
Review Fundamental Concepts: Ensure you have a strong grasp of statistics, probability, linear algebra, data structures, algorithms, and programming languages like Python, R, and SQL.
Refresh Machine Learning Knowledge: Familiarize yourself with various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
Practice Coding: Sharpen your coding skills by solving data science-related problems on platforms like HackerRank, LeetCode, and Kaggle.
Build a Project Portfolio: Showcase your proficiency by creating a portfolio highlighting projects covering data cleaning, wrangling, exploratory data analysis, and machine learning.
Hone Communication Skills: Practice articulating complex technical ideas in simple terms, as effective communication is vital for data scientists when interacting with non-technical stakeholders.
Research the Company: Gain insights into the company's operations, industry, and how they leverage data to solve challenges.
🧠👍By adhering to these guidelines, you'll be well-prepared for your upcoming data science interview. Best of luck!
Hope this helps 👍❤️:-)
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Review Fundamental Concepts: Ensure you have a strong grasp of statistics, probability, linear algebra, data structures, algorithms, and programming languages like Python, R, and SQL.
Refresh Machine Learning Knowledge: Familiarize yourself with various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
Practice Coding: Sharpen your coding skills by solving data science-related problems on platforms like HackerRank, LeetCode, and Kaggle.
Build a Project Portfolio: Showcase your proficiency by creating a portfolio highlighting projects covering data cleaning, wrangling, exploratory data analysis, and machine learning.
Hone Communication Skills: Practice articulating complex technical ideas in simple terms, as effective communication is vital for data scientists when interacting with non-technical stakeholders.
Research the Company: Gain insights into the company's operations, industry, and how they leverage data to solve challenges.
🧠👍By adhering to these guidelines, you'll be well-prepared for your upcoming data science interview. Best of luck!
Hope this helps 👍❤️:-)
👍👀Be the first one to know the latest Job openings
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
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1. What is the AdaBoost Algorithm?
AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. What this algorithm does is that it builds a model and gives equal weights to all the data points. It then assigns higher weights to points that are wrongly classified. Now all the points which have higher weights are given more importance in the next model. It will keep training models until and unless a lower error is received.
2. What is the Sliding Window method for Time Series Forecasting?
Time series can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem.
In the sliding window method, the previous time steps can be used as input variables, and the next time steps can be used as the output variable.
In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem.
3. What do you understand by sub-queries in SQL?
A subquery is a query inside another query where a query is defined to retrieve data or information back from the database. In a subquery, the outer query is called as the main query whereas the inner query is called subquery. Subqueries are always executed first and the result of the subquery is passed on to the main query. It can be nested inside a SELECT, UPDATE or any other query. A subquery can also use any comparison operators such as >,< or =.
4. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook?
Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.
5. How is a Random Forest related to Decision Trees?
Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.
6. What are some disadvantages of using Naive Bayes Algorithm?
Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. What this algorithm does is that it builds a model and gives equal weights to all the data points. It then assigns higher weights to points that are wrongly classified. Now all the points which have higher weights are given more importance in the next model. It will keep training models until and unless a lower error is received.
2. What is the Sliding Window method for Time Series Forecasting?
Time series can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem.
In the sliding window method, the previous time steps can be used as input variables, and the next time steps can be used as the output variable.
In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem.
3. What do you understand by sub-queries in SQL?
A subquery is a query inside another query where a query is defined to retrieve data or information back from the database. In a subquery, the outer query is called as the main query whereas the inner query is called subquery. Subqueries are always executed first and the result of the subquery is passed on to the main query. It can be nested inside a SELECT, UPDATE or any other query. A subquery can also use any comparison operators such as >,< or =.
4. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook?
Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.
5. How is a Random Forest related to Decision Trees?
Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.
6. What are some disadvantages of using Naive Bayes Algorithm?
Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
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∟📂 Learn OOPs & Design Patterns
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If you want to Excel at using one of the most powerful programming languages in the world, learn these essential Python features:
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• Map, Filter, Reduce – Functional programming magic
• F-strings – f"Hello, {name}!" (Best way to format strings)
• Enumerate & Zip – Iterate smarter
• Generators & Yield – Efficient memory usage
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• Decorators – @
• Pandas & NumPy – Data manipulation & numerical computing
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• List Comprehensions – [x for x in range(10) if x % 2 == 0]
• Lambda Functions – lambda x: x * 2
• Map, Filter, Reduce – Functional programming magic
• F-strings – f"Hello, {name}!" (Best way to format strings)
• Enumerate & Zip – Iterate smarter
• Generators & Yield – Efficient memory usage
• Exception Handling – try-except-finally for error-proof code
• Decorators – @
staticmethod, @classmethod, @property• Pandas & NumPy – Data manipulation & numerical computing
• Async Programming – Speed up tasks with asyncio
Free Python Resources: 👇
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HTML/CSS 🎨 – Kevin Powell
JavaScript 🌐 – The Net Ninja
TypeScript 📘 – Academind
React ⚛️ – Traversy Media
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Node. js 🚀 – Traversy Media
PHP 🐘 – PHP Academy
Ruby on Rails 💎 – Drifting Ruby
Django (Python) 🐍 – Corey Schafer
Flask (Python) 🔥 – Pretty Printed
ASP. NET (C#) 🎯 – IAmTimCorey
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Docker 🐳 – TechWorld with Nana
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𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 & 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀
HTML/CSS 🎨 – Kevin Powell
JavaScript 🌐 – The Net Ninja
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React ⚛️ – Traversy Media
Angular 🔺 – Academind
Vue. js 🟩 – Vue Mastery
𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 & 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀
Node. js 🚀 – Traversy Media
PHP 🐘 – PHP Academy
Ruby on Rails 💎 – Drifting Ruby
Django (Python) 🐍 – Corey Schafer
Flask (Python) 🔥 – Pretty Printed
ASP. NET (C#) 🎯 – IAmTimCorey
𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 & 𝗗𝗲𝘃𝗢𝗽𝘀
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There are many programming languages, each serving different purposes. Here are some key ones you should know:
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🔹 2. JavaScript – Essential for frontend and backend web development, powering interactive websites and applications.
🔹 3. Java – Used for enterprise applications, Android development, and large-scale systems due to its stability.
🔹 4. C++ – High-performance language ideal for game development, operating systems, and embedded systems.
🔹 5. C# – Commonly used in game development (Unity), Windows applications, and enterprise software.
🔹 6. Swift – The go-to language for iOS and macOS development, known for its efficiency.
🔹 7. Go (Golang) – Designed for high-performance applications, cloud computing, and network programming.
🔹 8. Rust – Focuses on memory safety and performance, making it great for system-level programming.
🔹 9. SQL – Essential for database management, allowing efficient data retrieval and manipulation.
🔹 10. Kotlin – Popular for Android app development, offering modern features compared to Java.
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There are many programming languages, each serving different purposes. Here are some key ones you should know:
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🔹 2. JavaScript – Essential for frontend and backend web development, powering interactive websites and applications.
🔹 3. Java – Used for enterprise applications, Android development, and large-scale systems due to its stability.
🔹 4. C++ – High-performance language ideal for game development, operating systems, and embedded systems.
🔹 5. C# – Commonly used in game development (Unity), Windows applications, and enterprise software.
🔹 6. Swift – The go-to language for iOS and macOS development, known for its efficiency.
🔹 7. Go (Golang) – Designed for high-performance applications, cloud computing, and network programming.
🔹 8. Rust – Focuses on memory safety and performance, making it great for system-level programming.
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𝐒𝐐𝐋 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰:
Join for more: https://t.me/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.me/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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