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
โค4
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 youโll 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 youโll 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
โค7
Cold email template for Freshers ๐
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 description 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 description 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}
โค5
Handling Datasets of All Types โ Part 1 of 5: Introduction and Basic Concepts โ๏ธ
1. What is a Dataset?
โข 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
โข Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
โข Unstructured Data: Images, text, audio, video.
โข Semi-structured Data: JSON, XML files containing hierarchical data.
3. Common Dataset Formats
โข CSV (Comma-Separated Values)
โข Excel (.xls, .xlsx)
โข JSON (JavaScript Object Notation)
โข XML (eXtensible Markup Language)
โข Images (JPEG, PNG, TIFF)
โข Audio (WAV, MP3)
4. Loading Datasets in Python
โข Use libraries like
โข Use libraries like
5. Basic Dataset Exploration
โข Check shape and size:
โข Preview data:
โข Check for missing values:
6. Summary
โข Understanding dataset types is crucial before processing.
โข Loading and exploring datasets helps identify cleaning and preprocessing needs.
Exercise
โข Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
Hope this helped youโ๏ธ
1. What is a Dataset?
โข 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
โข Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
โข Unstructured Data: Images, text, audio, video.
โข Semi-structured Data: JSON, XML files containing hierarchical data.
3. Common Dataset Formats
โข CSV (Comma-Separated Values)
โข Excel (.xls, .xlsx)
โข JSON (JavaScript Object Notation)
โข XML (eXtensible Markup Language)
โข Images (JPEG, PNG, TIFF)
โข Audio (WAV, MP3)
4. Loading Datasets in Python
โข Use libraries like
pandas for structured data:import pandas as pd
df = pd.read_csv('data.csv')
โข Use libraries like
json for JSON files:import json
with open('data.json') as f:
data = json.load(f)
5. Basic Dataset Exploration
โข Check shape and size:
print(df.shape)
โข Preview data:
print(df.head())
โข Check for missing values:
print(df.isnull().sum())
6. Summary
โข Understanding dataset types is crucial before processing.
โข Loading and exploring datasets helps identify cleaning and preprocessing needs.
Exercise
โข Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
Hope this helped you
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โค5๐2
Data Science & Machine Learning
What's the correct answer ๐๐
a = "10" โ Variable a is assigned the string "10".
b = a โ Variable b also holds the string "10" (but it's not used afterward).
a = a * 2 โ Since a is a string, multiplying it by an integer results in string repetition.
"10" * 2 results in "1010"
print(a) โ prints the new value of a, which is "1010".โ Correct answer: D. 1010
โค5
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:
๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐
โ๏ธ Bayes' Theorem & conditional probability
โ๏ธ Permutations & combinations
โ๏ธ Card & die roll problem-solving
๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐๐ฒ ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ & ๐ฑ๐ถ๐๐๐ฟ๐ถ๐ฏ๐๐๐ถ๐ผ๐ป๐
โ๏ธ Mean, median, mode
โ๏ธ Standard deviation and variance
โ๏ธ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐๐ถ๐ฎ๐น ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐
โ๏ธ A/B experimentation
โ๏ธ T-test, Z-test, Chi-squared tests
โ๏ธ Type 1 & 2 errors
โ๏ธ Sampling techniques & biases
โ๏ธ Confidence intervals & p-values
โ๏ธ Central Limit Theorem
โ๏ธ Causal inference techniques
๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
โ๏ธ Logistic & Linear regression
โ๏ธ Decision trees & random forests
โ๏ธ Clustering models
โ๏ธ Feature engineering
โ๏ธ Feature selection methods
โ๏ธ Model testing & validation
โ๏ธ 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:
๐ฃ๐ฟ๐ผ๐ฏ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐
โ๏ธ Bayes' Theorem & conditional probability
โ๏ธ Permutations & combinations
โ๏ธ Card & die roll problem-solving
๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐๐ฒ ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ & ๐ฑ๐ถ๐๐๐ฟ๐ถ๐ฏ๐๐๐ถ๐ผ๐ป๐
โ๏ธ Mean, median, mode
โ๏ธ Standard deviation and variance
โ๏ธ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐๐ถ๐ฎ๐น ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐
โ๏ธ A/B experimentation
โ๏ธ T-test, Z-test, Chi-squared tests
โ๏ธ Type 1 & 2 errors
โ๏ธ Sampling techniques & biases
โ๏ธ Confidence intervals & p-values
โ๏ธ Central Limit Theorem
โ๏ธ Causal inference techniques
๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
โ๏ธ Logistic & Linear regression
โ๏ธ Decision trees & random forests
โ๏ธ Clustering models
โ๏ธ Feature engineering
โ๏ธ Feature selection methods
โ๏ธ Model testing & validation
โ๏ธ Time series analysis
Math & Statistics: https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
โค5๐1
Data Scientist Roadmap ๐
๐ Python Basics
โ๐ Numpy & Pandas
โโ๐ Data Cleaning
โโโ๐ Data Visualization (Seaborn, Plotly)
โโโโ๐ Statistics & Probability
โโโโโ๐ Machine Learning (Sklearn)
โโโโโโ๐ Deep Learning (TensorFlow / PyTorch)
โโโโโโโ๐ Model Deployment
โโโโโโโโ๐ Real-World Projects
โโโโโโโโโโ Apply for Data Science Roles
React "โค๏ธ" For More
๐ Python Basics
โ๐ Numpy & Pandas
โโ๐ Data Cleaning
โโโ๐ Data Visualization (Seaborn, Plotly)
โโโโ๐ Statistics & Probability
โโโโโ๐ Machine Learning (Sklearn)
โโโโโโ๐ Deep Learning (TensorFlow / PyTorch)
โโโโโโโ๐ Model Deployment
โโโโโโโโ๐ Real-World Projects
โโโโโโโโโโ Apply for Data Science Roles
React "โค๏ธ" For More
โค27
SQL Basics for Beginners: Must-Know Concepts
1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.
2. SQL Syntax
SQL is written using statements, which consist of keywords like
- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g.,
3. SQL Data Types
Databases store data in different formats. The most common data types are:
-
-
-
-
4. Basic SQL Queries
Here are some fundamental SQL operations:
- SELECT Statement: Used to retrieve data from a database.
- WHERE Clause: Filters data based on conditions.
- ORDER BY: Sorts data in ascending (
- LIMIT: Limits the number of rows returned.
5. Filtering Data with WHERE Clause
The
You can use comparison operators like:
-
-
-
-
6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.
- SUM(): Adds up values in a column.
- AVG(): Calculates the average value.
- GROUP BY: Groups rows that have the same values into summary rows.
7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.
- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
8. Inserting Data
To add new data to a table, you use the
9. Updating Data
You can update existing data in a table using the
10. Deleting Data
To remove data from a table, use the
Here you can find essential SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Like this post if you need more ๐โค๏ธ
Hope it helps :)
1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.
2. SQL Syntax
SQL is written using statements, which consist of keywords like
SELECT, FROM, WHERE, etc., to perform operations on the data.- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g.,
SELECT, FROM).3. SQL Data Types
Databases store data in different formats. The most common data types are:
-
INT (Integer): For whole numbers.-
VARCHAR(n) or TEXT: For storing text data.-
DATE: For dates.-
DECIMAL: For precise decimal values, often used in financial calculations.4. Basic SQL Queries
Here are some fundamental SQL operations:
- SELECT Statement: Used to retrieve data from a database.
SELECT column1, column2 FROM table_name;
- WHERE Clause: Filters data based on conditions.
SELECT * FROM table_name WHERE condition;
- ORDER BY: Sorts data in ascending (
ASC) or descending (DESC) order.SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
- LIMIT: Limits the number of rows returned.
SELECT * FROM table_name LIMIT 5;
5. Filtering Data with WHERE Clause
The
WHERE clause helps you filter data based on a condition:SELECT * FROM employees WHERE salary > 50000;
You can use comparison operators like:
-
=: Equal to-
>: Greater than-
<: Less than-
LIKE: For pattern matching6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.
SELECT COUNT(*) FROM table_name;
- SUM(): Adds up values in a column.
SELECT SUM(salary) FROM employees;
- AVG(): Calculates the average value.
SELECT AVG(salary) FROM employees;
- GROUP BY: Groups rows that have the same values into summary rows.
SELECT department, AVG(salary) FROM employees GROUP BY department;
7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.
SELECT employees.name, departments.department
FROM employees
INNER JOIN departments
ON employees.department_id = departments.id;
- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.
SELECT employees.name, departments.department
FROM employees
LEFT JOIN departments
ON employees.department_id = departments.id;
8. Inserting Data
To add new data to a table, you use the
INSERT INTO statement: INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
9. Updating Data
You can update existing data in a table using the
UPDATE statement:UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
10. Deleting Data
To remove data from a table, use the
DELETE statement:DELETE FROM employees WHERE name = 'John Doe';
Here you can find essential SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Like this post if you need more ๐โค๏ธ
Hope it helps :)
โค5