𝗦𝗤𝗟 𝗝𝗼𝗶𝗻𝘀 𝗖𝗵𝗲𝗮𝘁𝘀𝗵𝗲𝗲𝘁 - 𝗙𝘂𝗹𝗹𝘆 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱
𝗪𝗵𝘆 𝗷𝗼𝗶𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?
Joins let you combine data from multiple tables to extract meaningful insights.
Every serious data analyst or backend dev should master these.
Let’s break them down with clarity:
𝗜𝗡𝗡𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns only the rows with matching keys in both tables
→ Think of it as intersection
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Customers who have placed at least one order
SELECT *
FROM Customers
INNER JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗟𝗘𝗙𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the left table + matching rows from the right
→ If no match, right side = NULL
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
List all customers, even if they’ve never ordered
SELECT *
FROM Customers
LEFT JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗥𝗜𝗚𝗛𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the right table + matching rows from the left
→ Rarely used, but similar logic
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
All orders, even from unknown or deleted customers
SELECT *
FROM Customers
RIGHT JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗙𝗨𝗟𝗟 𝗢𝗨𝗧𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns all records when there’s a match in either table
→ Unmatched rows = NULLs
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all customers and all orders, whether matched or not
SELECT *
FROM Customers
FULL OUTER JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗖𝗥𝗢𝗦𝗦 𝗝𝗢𝗜𝗡
→ Returns Cartesian product (all combinations)
→ Use with care. 1,000 x 1,000 rows = 1,000,000 results!
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all possible product and supplier pairings
SELECT *
FROM Products
CROSS JOIN Suppliers;
𝗦𝗘𝗟𝗙 𝗝𝗢𝗜𝗡
→ Join a table to itself
→ Used for hierarchical data like employees & managers
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Find each employee’s manager
SELECT A.Name AS Employee, B.Name AS Manager
FROM Employees A
JOIN Employees B
ON A.ManagerID = B.ID;
𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀
→ Always use aliases (A, B) to simplify joins
→ Use JOIN ON instead of WHERE for better clarity
→ Test each join with LIMIT first to avoid surprises
---
𝗪𝗵𝘆 𝗷𝗼𝗶𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?
Joins let you combine data from multiple tables to extract meaningful insights.
Every serious data analyst or backend dev should master these.
Let’s break them down with clarity:
𝗜𝗡𝗡𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns only the rows with matching keys in both tables
→ Think of it as intersection
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Customers who have placed at least one order
SELECT *
FROM Customers
INNER JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗟𝗘𝗙𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the left table + matching rows from the right
→ If no match, right side = NULL
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
List all customers, even if they’ve never ordered
SELECT *
FROM Customers
LEFT JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗥𝗜𝗚𝗛𝗧 𝗝𝗢𝗜𝗡 (𝗢𝗨𝗧𝗘𝗥)
→ Returns all rows from the right table + matching rows from the left
→ Rarely used, but similar logic
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
All orders, even from unknown or deleted customers
SELECT *
FROM Customers
RIGHT JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗙𝗨𝗟𝗟 𝗢𝗨𝗧𝗘𝗥 𝗝𝗢𝗜𝗡
→ Returns all records when there’s a match in either table
→ Unmatched rows = NULLs
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all customers and all orders, whether matched or not
SELECT *
FROM Customers
FULL OUTER JOIN Orders
ON Customers.ID = Orders.CustomerID;
𝗖𝗥𝗢𝗦𝗦 𝗝𝗢𝗜𝗡
→ Returns Cartesian product (all combinations)
→ Use with care. 1,000 x 1,000 rows = 1,000,000 results!
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Show all possible product and supplier pairings
SELECT *
FROM Products
CROSS JOIN Suppliers;
𝗦𝗘𝗟𝗙 𝗝𝗢𝗜𝗡
→ Join a table to itself
→ Used for hierarchical data like employees & managers
𝗘𝘅𝗮𝗺𝗽𝗹𝗲:
Find each employee’s manager
SELECT A.Name AS Employee, B.Name AS Manager
FROM Employees A
JOIN Employees B
ON A.ManagerID = B.ID;
𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀
→ Always use aliases (A, B) to simplify joins
→ Use JOIN ON instead of WHERE for better clarity
→ Test each join with LIMIT first to avoid surprises
---
❤7
𝐒𝐐𝐋 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰:
Join for more: https://news.1rj.ru/str/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
Subnoscription-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://news.1rj.ru/str/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
Subnoscription-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
❤4
Getting a job in 2017:
Apply, get interview, get offer, negotiate salary, start job.
Getting a job in 2025:
Find job you are overqualified for that is underpaying market rates, connect with current employees and ask for a recommendation, bake a cake for the potential team you’ll be apart of and hope your efforts are better than other candidates, meet with the third cousin of the hiring manager to see if you are a good fit to maybe start the process of interviewing, take a 3-hour long pass
Apply, get interview, get offer, negotiate salary, start job.
Getting a job in 2025:
Find job you are overqualified for that is underpaying market rates, connect with current employees and ask for a recommendation, bake a cake for the potential team you’ll be apart of and hope your efforts are better than other candidates, meet with the third cousin of the hiring manager to see if you are a good fit to maybe start the process of interviewing, take a 3-hour long pass
❤7
Cold email template for Freshers 👇
Dear {NAME},
I hope this email finds you in good health and high spirits. I am writing to express my keen interest in the internship opportunity at the {NAME} and to submit my application for your consideration.
Allow me to introduce myself. My name is Ashok Aggarwal, and I am a statistics major with a specialization in Data Science. I have been following the remarkable work conducted by {NAME} and the valuable contributions it has made to the field of biomedical research and public health. I am truly inspired by the {One USP}
Having reviewed the internship denoscription and requirements, I firmly believe that my academic background and skills make me a strong candidate for this opportunity. I have a solid foundation in statistics and data analysis, along with proficiency in relevant software such as Python, NumPy, Pandas, and visualization tools like Matplotlib. Furthermore, my prior project on {xyz} has reinforced my passion for utilizing data-driven insights to understand {XYZ}
Joining {name} for this internship would provide me with a tremendous platform to contribute my statistical expertise and collaborate with esteemed scientists like yourself. I am eager to work closely with the research team, assist in communications campaigns, engage in community programs, and learn from the collective expertise at {Name}.
I have attached my resume and would be grateful if you could review my application. I am available for an interview at your convenience to further discuss my qualifications and how I can contribute to {NAME} initiatives. I genuinely appreciate your time and consideration.
Thank you for your attention to my application. I look forward to the possibility of joining {NAME} and making a meaningful contribution to the organization's mission. Should you require any further information or documentation, please do not hesitate to contact me.
Wishing you a productive day ahead.
Sincerely,
{Full Name}
Dear {NAME},
I hope this email finds you in good health and high spirits. I am writing to express my keen interest in the internship opportunity at the {NAME} and to submit my application for your consideration.
Allow me to introduce myself. My name is Ashok Aggarwal, and I am a statistics major with a specialization in Data Science. I have been following the remarkable work conducted by {NAME} and the valuable contributions it has made to the field of biomedical research and public health. I am truly inspired by the {One USP}
Having reviewed the internship denoscription and requirements, I firmly believe that my academic background and skills make me a strong candidate for this opportunity. I have a solid foundation in statistics and data analysis, along with proficiency in relevant software such as Python, NumPy, Pandas, and visualization tools like Matplotlib. Furthermore, my prior project on {xyz} has reinforced my passion for utilizing data-driven insights to understand {XYZ}
Joining {name} for this internship would provide me with a tremendous platform to contribute my statistical expertise and collaborate with esteemed scientists like yourself. I am eager to work closely with the research team, assist in communications campaigns, engage in community programs, and learn from the collective expertise at {Name}.
I have attached my resume and would be grateful if you could review my application. I am available for an interview at your convenience to further discuss my qualifications and how I can contribute to {NAME} initiatives. I genuinely appreciate your time and consideration.
Thank you for your attention to my application. I look forward to the possibility of joining {NAME} and making a meaningful contribution to the organization's mission. Should you require any further information or documentation, please do not hesitate to contact me.
Wishing you a productive day ahead.
Sincerely,
{Full Name}
❤5
Handling Datasets of All Types – Part 1 of 5: Introduction and Basic Concepts ☑️
1. What is a Dataset?
• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.
2. Types of Datasets
• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
• Unstructured Data: Images, text, audio, video.
• Semi-structured Data: JSON, XML files containing hierarchical data.
3. Common Dataset Formats
• CSV (Comma-Separated Values)
• Excel (.xls, .xlsx)
• JSON (JavaScript Object Notation)
• XML (eXtensible Markup Language)
• Images (JPEG, PNG, TIFF)
• Audio (WAV, MP3)
4. Loading Datasets in Python
• Use libraries like
• Use libraries like
5. Basic Dataset Exploration
• Check shape and size:
• Preview data:
• Check for missing values:
6. Summary
• Understanding dataset types is crucial before processing.
• Loading and exploring datasets helps identify cleaning and preprocessing needs.
Exercise
• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
Hope this helped you✔️
1. What is a Dataset?
• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.
2. Types of Datasets
• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
• Unstructured Data: Images, text, audio, video.
• Semi-structured Data: JSON, XML files containing hierarchical data.
3. Common Dataset Formats
• CSV (Comma-Separated Values)
• Excel (.xls, .xlsx)
• JSON (JavaScript Object Notation)
• XML (eXtensible Markup Language)
• Images (JPEG, PNG, TIFF)
• Audio (WAV, MP3)
4. Loading Datasets in Python
• Use libraries like
pandas for structured data:import pandas as pd
df = pd.read_csv('data.csv')
• Use libraries like
json for JSON files:import json
with open('data.json') as f:
data = json.load(f)
5. Basic Dataset Exploration
• Check shape and size:
print(df.shape)
• Preview data:
print(df.head())
• Check for missing values:
print(df.isnull().sum())
6. Summary
• Understanding dataset types is crucial before processing.
• Loading and exploring datasets helps identify cleaning and preprocessing needs.
Exercise
• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
Hope this helped you
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❤5👍2
Data Science & Machine Learning
What's the correct answer 👇👇
a = "10" → Variable a is assigned the string "10".
b = a → Variable b also holds the string "10" (but it's not used afterward).
a = a * 2 → Since a is a string, multiplying it by an integer results in string repetition.
"10" * 2 results in "1010"
print(a) → prints the new value of a, which is "1010".✅ Correct answer: D. 1010
❤5
How much Statistics must I know to become a Data Scientist?
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆
↗️ Bayes' Theorem & conditional probability
↗️ Permutations & combinations
↗️ Card & die roll problem-solving
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀
↗️ Mean, median, mode
↗️ Standard deviation and variance
↗️ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀
↗️ A/B experimentation
↗️ T-test, Z-test, Chi-squared tests
↗️ Type 1 & 2 errors
↗️ Sampling techniques & biases
↗️ Confidence intervals & p-values
↗️ Central Limit Theorem
↗️ Causal inference techniques
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴
↗️ Logistic & Linear regression
↗️ Decision trees & random forests
↗️ Clustering models
↗️ Feature engineering
↗️ Feature selection methods
↗️ Model testing & validation
↗️ Time series analysis
Math & Statistics: https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆
↗️ Bayes' Theorem & conditional probability
↗️ Permutations & combinations
↗️ Card & die roll problem-solving
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀
↗️ Mean, median, mode
↗️ Standard deviation and variance
↗️ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀
↗️ A/B experimentation
↗️ T-test, Z-test, Chi-squared tests
↗️ Type 1 & 2 errors
↗️ Sampling techniques & biases
↗️ Confidence intervals & p-values
↗️ Central Limit Theorem
↗️ Causal inference techniques
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴
↗️ Logistic & Linear regression
↗️ Decision trees & random forests
↗️ Clustering models
↗️ Feature engineering
↗️ Feature selection methods
↗️ Model testing & validation
↗️ Time series analysis
Math & Statistics: https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
❤5👏1