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Day-4 of SQL 😍

DDL (Data Definition Language) consists of SQL commands used to define and modify the structure of database objects. These commands are crucial for creating, altering, and deleting database structures such as tables, indexes, views, and schemas. Common DDL commands include:

CREATE: Used to create new database objects.
ALTER: Used to modify existing database objects.
DROP: Used to delete existing database objects.
TRUNCATE: Used to remove all records from a table, but not the table itself.
RENAME: Used to rename existing database objects.

DML (Data Manipulation Language), on the other hand, consists of SQL commands used to manipulate the data within these database structures. These commands are essential for performing operations like inserting, updating, and deleting data within tables. Common DML commands include:

INSERT: Used to add new records to a table.
UPDATE: Used to modify existing records within a table.
DELETE: Used to remove records from a table.
SELECT: Although often categorized under Data Query Language (DQL), it is sometimes considered a part of DML for retrieving data from a database.

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Day-4 of Statistics 😍

✅ Quantitative Data:

Quantitative data represents information that can be measured and expressed numerically. This type of data is often associated with quantities and involves counting or measuring attributes.

- Measurable and numerical.
- Includes discrete (countable) and continuous (measurable) data.
- Analyzed using measures of central tendency and dispersion.
- Examples: height, weight, number of students.
✅ Qualitative Data:
Qualitative data, also known as categorical data, represents characteristics or attributes that cannot be measured numerically but can be observed and recorded as categories or labels.

- Descriptive and categorical.
- Includes nominal (unordered categories) and ordinal (ordered categories) data.
- Analyzed using frequency distribution and non-parametric methods.
- Examples: gender, eye color, satisfaction ratings.

Understanding the differences between quantitative and qualitative data is crucial for selecting appropriate statistical techniques and accurately interpreting results in any research or data analysis context.

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Day-5 of MYSQL 😍

In MySQL, data types define the kind of data that can be stored in a column. Here's a brief overview of the main MySQL data types:

1. Numeric Data Types
INT: Integer values. (e.g., INT, TINYINT, SMALLINT, MEDIUMINT, BIGINT)
FLOAT: Floating-point numbers for approximate values.
DOUBLE: Double-precision floating-point numbers for more precise values.
DECIMAL: Fixed-point numbers for exact decimal values.

2. Date and Time Data Types
DATE: Dates in YYYY-MM-DD format.
DATETIME: Dates and times in YYYY-MM-DD HH:MM:SS format.
TIMESTAMP: Timestamps for tracking changes, usually in YYYY-MM-DD HH:MM:SS format.
TIME: Time of day in HH:MM:SS format.
YEAR: Year in YYYY format.

3. String Data Types
CHAR: Fixed-length character strings.
VARCHAR: Variable-length character strings.
TEXT: Large text fields. (e.g., TINYTEXT, MEDIUMTEXT, LONGTEXT)
BLOB: Binary large objects for storing binary data. (e.g., TINYBLOB, MEDIUMBLOB, LONGBLOB)

4. Other Data Types
ENUM: Enumerated list of values; each column value must be one of the predefined set.
SET: A set of values, where each column value can be a combination of predefined values.


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Day-5 of statistics 😍😍


A box plot, also known as a box-and-whisker plot, is a graphical representation of the distribution of a dataset. It provides a visual summary of key statistical measures and is particularly useful for identifying the spread, central tendency, and potential outliers in a dataset.

Components of a Box Plot

Box:

- The box represents the interquartile range (IQR), which encompasses the middle 50% of the data. It is bounded by the first quartile (Q1) and the third quartile (Q3).
- The height of the box indicates the variability or spread of the central 50% of the data.

Median:

- The line inside the box represents the median (Q2) of the dataset, which is the middle value when the data is sorted in ascending order. It divides the data into two equal halves.
Whiskers:

- The whiskers extend from the edges of the box to the smallest and largest values within 1.5 times the IQR from Q1 and Q3, respectively.
- They provide an indication of the range of the data.
Outliers:

- Data points that fall outside the whiskers are considered outliers. These are typically represented as individual points or dots beyond the whiskers.
- Outliers are values that significantly deviate from the rest of the data and can indicate variability or anomalies.

Statistical Measures Represented

Minimum: The smallest value in the dataset within the whiskers' range.
Q1 (First Quartile): The median of the lower half of the data (25th percentile).
Median (Q2): The middle value of the dataset (50th percentile).
Q3 (Third Quartile): The median of the upper half of the data (75th percentile).
Maximum: The largest value in the dataset within the whiskers' range.
IQR (Interquartile Range): The range between Q1 and Q3, representing the middle 50% of the data.


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Day-6 of MYSQL 😍😍

👉🏻👉🏻 What is constraint in mysql?
SQL constraints are used to specify rules for the data in a table. Constraints are used to limit the type of data that can go into a table. This ensures the accuracy and reliability of the data in the table. If there is any violation between the constraint and the data action, the action is aborted.

👉🏻👉🏻 What is primary and foreign key?
Primary keys serve as unique identifiers for each row in a database table. Foreign keys link data in one table to the data in another table. A foreign key column in a table points to a column with unique values in another table (often the primary key column) to create a way of cross-referencing the two tables. This is a crucial aspect of SQL keys that ensure data integrity and relationships between tables.

If a column is assigned a foreign key, each row of that column must contain a value that exists in the ‘foreign’ column it references. The referenced (i.e. “foreign”) column must contain only unique values – often it is the primary key of its table.

In short:

✅ Primary keys are used to uniquely identify and index each row within a single table.
✅ Foreign keys are used to link rows in two different tables such that a row can only be added or updated in table_a if the value in its foreign key column exists in the relevant column of table_b.


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Day-6 of Statistics😍✅

Measure of central tendency!!
A measure of central tendency (also referred to as measures of centre or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution.

There are three main measures of central tendency:

👉🏻mode
👉🏻median
👉🏻mean
Each of these measures describes a different indication of the typical or central value in the distribution.

✅ Mode
The mode is the most commonly occurring value in a distribution.

✅ Median
The median is the middle value in distribution when the values are arranged in ascending or descending order.

✅ Mean
The mean is the sum of the value of each observation in a dataset divided by the number of observations. This is also known as the arithmetic average.


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Here are some resources that can significantly help youduring interview preparation:

SQL
1. Video Tutorials:
- techTFQ YouTube Channel
- Ankit Bansal YouTube Channel

2. Practice Websites:
- Datalemur
- Leetcode
- Hackerrank
- Stratascratch

Python
1. Video Tutorials:
- Jose Portilla's "Python for Data Science and Machine Learning Bootcamp" on Udemy

2. Case Studies and Practice:
- Various Pandas case studies on YouTube Channel - "Data Thinkers"
- Continued practice of Python (& pandas too) on Leetcode and Datalemur

Excel & Power BI
1. Courses:
- Leila Gharani’s Excel and Power BI courses on Udemy and YouTube

2. Self-Learning:
- Create dashboards in Excel and Power BI using various YouTube tutorials for practical learning
- Follow Various Excel interview questions videos from "Kenji Explains" YouTube channel.

Business Case studies
1. Follow "Insider Gyan" YouTube channel & "Case in Point" book for business case studies & Guesstimate problems.

When learning SQL and database management systems (DBMS), it's helpful to cover a broad range of topics to build a solid foundation. Here’s a structured approach:

1. Introduction to Databases
- What is a database?
- Types of databases (relational vs. non-relational)
- Basic concepts (tables, records, fields)

2. SQL Basics
- SQL syntax and structure
- Data types (INT, VARCHAR, DATE, etc.)
- Basic commands (SELECT, INSERT, UPDATE, DELETE)

3. Data Querying
- Filtering data with WHERE
- Sorting results with ORDER BY
- Limiting results with LIMIT (or FETCH)

4. Advanced Query Techniques
- Joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN)
- Subqueries and nested queries
- Aggregation functions (COUNT, SUM, AVG, MAX, MIN)
- GROUP BY and HAVING clauses

5. Database Design
- Normalization (1NF, 2NF, 3NF)
- Designing schemas and relationships
- Primary keys and foreign keys

6. Indexes and Performance
- Creating and managing indexes
- Understanding query performance
- Basic optimization techniques

7. Data Integrity and Constraints
- Constraints (NOT NULL, UNIQUE, CHECK, DEFAULT)
- Transactions (COMMIT, ROLLBACK)
- Concurrency control (locking, isolation levels)

8. Stored Procedures and Functions
- Creating and using stored procedures
- User-defined functions
- Error handling in stored procedures

9. Database Security
- User roles and permissions
- Encryption
- Backup and recovery strategies

10. Database Management Systems (DBMS)
- Overview of popular DBMS (MySQL, PostgreSQL, SQL Server, Oracle)
- Differences and specific features of each DBMS

11. Data Manipulation and Transformation
- Using SQL for data cleaning and transformation
- ETL (Extract, Transform, Load) processes

12. Advanced SQL Topics
- Recursive queries
- Window functions
- Common Table Expressions (CTEs)

13. Practical Applications
- Real-world database design and implementation
- Using SQL in application development

14. Tools and Interfaces
- SQL command-line tools
- GUI tools for managing databases (e.g., MySQL Workbench, pgAdmin)

Covering these topics will give you a well-rounded understanding of SQL and database management, equipping you with the skills to handle a variety of database-related tasks.


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𝐓𝐨𝐩 𝟏𝟓 𝐦𝐨𝐬𝐭 𝐜𝐨𝐦𝐦𝐨𝐧𝐥𝐲 𝐚𝐬𝐤𝐞𝐝 𝐀𝐩𝐚𝐜𝐡𝐞 𝐒𝐩𝐚𝐫𝐤 𝐒𝐐𝐋 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝟐𝟎𝟐𝟒:

1. What is Apache Spark SQL?
Explain its components and how it integrates with Spark Core.

2. How do you create a DataFrame in Spark SQL?
Describe different ways to create DataFrames and provide examples.

3. What are the advantages of using DataFrames over RDDs in Spark SQL?
Explain the concept of a DataFrame API.

4. How does it differ from traditional SQL?
5. What is a Catalyst Optimizer?
Explain its role in Spark SQL.

6. How do you perform joins in Spark SQL?
Describe different types of joins and provide examples.

7. What is a Window Function in Spark SQL?
Explain its usage and provide an example.

8. How do you handle missing or null values in Spark SQL?
9. What are the differences between Spark SQL and Hive?
Discuss performance, flexibility, and use cases.

10. How do you optimize Spark SQL queries?
Provide tips and techniques for query optimization.
Explain the concept of SparkSession.

11. How do you create and use it in Spark SQL?
12. What are UDFs (User Defined Functions) in Spark SQL?
13. How do you create and use them?
14. What are DataFrame Transformations and Actions in Spark SQL?
Provide examples of each.

15. How do you use the groupBy and agg functions in Spark SQL?
Explain with examples.
16. What is the difference between select and selectExpr in Spark SQL?
Provide use cases and examples.

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**✅ Unique vs. Distinct**

🔍 SQL Constraints: Unique
- The Unique constraint in SQL is used to ensure that no duplicate tuples (rows) exist in the result of a sub-query.
- It returns a boolean value:
- True: No duplicate tuples found.
- False: Duplicate tuples are present.

📌 Important Points:
- Evaluates to True on an empty subquery.
- Returns True only if all tuples in the sub-query are unique (two tuples are unique if the value of any attribute differs).
- Returns True even if the sub-query has two duplicate rows where at least one attribute is NULL.

🖋 Syntax:

CREATE TABLE table_name (
  column1 datatype UNIQUE,
  column2 datatype,
  ...
);

---

🎯 SQL DISTINCT Clause
- The DISTINCT clause is used to remove duplicate columns from the result set.
- It is typically used with the SELECT keyword to retrieve unique values from specified columns/tables.

📌 Key Points:
- SELECT DISTINCT returns only distinct (different) values.
- DISTINCT eliminates duplicate records from the table.
- DISTINCT can be used with aggregates like COUNT, AVG, MAX, etc.
- DISTINCT operates on a single column.
- Multiple columns are not supported for DISTINCT.

---

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Skewness is a statistical measure that describes the asymmetry of the distribution of values in a dataset. It indicates the extent to which the values deviate from a normal distribution (which is symmetrical). If a dataset has skewness, it means that the data is not evenly distributed around the mean.

Types of Skewness

1. Positive Skewness (Right Skewed):
- Description: In a positively skewed distribution, the tail on the right side (higher values) is longer or fatter than the left side. Most of the data points are concentrated on the left side of the distribution, with fewer larger values stretching out towards the right.
- Effect on Mean and Median: The mean is greater than the median because the long tail on the right pulls the mean to the right.

2. Negative Skewness (Left Skewed):
- Description: In a negatively skewed distribution, the tail on the left side (lower values) is longer or fatter than the right side. Most of the data points are concentrated on the right side of the distribution, with fewer smaller values stretching out towards the left.
- Effect on Mean and Median: The mean is less than the median because the long tail on the left pulls the mean to the left.

3. Zero Skewness (Symmetrical Distribution):
- Description: In a perfectly symmetrical distribution, the data is evenly distributed on both sides of the mean, with no skewness. This is typically seen in a normal distribution (bell curve).
- Effect on Mean and Median: The mean and median are equal, and the distribution is not skewed in either direction.


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What is Sampling?

*Sampling* is the process of selecting a subset of individuals, observations, or data points from a larger population to make inferences about that population. It is often used in statistics because studying an entire population can be impractical, time-consuming, or costly.

---

Types of Sampling

Sampling methods can be broadly categorized into two main types: *probability sampling* and *non-probability sampling*.

---

1. Probability Sampling

In *probability sampling*, every member of the population has a known, non-zero chance of being selected. This type of sampling allows for more accurate and unbiased inferences about the population.

- Simple Random Sampling:
- *Description:* Every member of the population has an equal chance of being selected. It is the most straightforward method where samples are chosen randomly without any specific criteria.
- *Example:* Drawing names from a hat.

- Stratified Sampling:
- *Description:* The population is divided into distinct subgroups (strata) based on a specific characteristic (e.g., age, gender), and samples are randomly selected from each subgroup. This ensures that each subgroup is adequately represented.
- *Example:* Dividing a population by age groups and randomly selecting individuals from each age group.

- Systematic Sampling:
- *Description:* A sample is selected at regular intervals from a list or sequence. The first member is selected randomly, and subsequent members are chosen at regular intervals.
- *Example:* Selecting every 10th person from a list of employees.

- Cluster Sampling:
- *Description:* The population is divided into clusters (groups), and a random selection of entire clusters is made. All members of the selected clusters are then included in the sample.
- *Example:* Selecting entire schools as clusters and surveying all students within those selected schools.

- Multistage Sampling:
- *Description:* Combines several sampling methods. For example, first, clusters are randomly selected, and then a random sample is taken within each selected cluster.
- *Example:* Selecting states (first stage), then cities within those states (second stage), and then households within those cities (third stage).

---

2. Non-Probability Sampling

In *non-probability sampling*, the probability of each member being selected is unknown. This method is often easier and quicker but can introduce bias.

- Convenience Sampling:
- *Description:* Samples are chosen based on their convenience and availability to the researcher. It’s quick and easy but may not be representative of the entire population.
- *Example:* Surveying people at a shopping mall.

- Judgmental (Purposive) Sampling:
- *Description:* Samples are selected based on the researcher’s judgment and the purpose of the study. The researcher uses their knowledge to choose individuals who are believed to be representative of the population.
- *Example:* Selecting experts in a particular field to study their opinions.

- Snowball Sampling:
- *Description:* Existing study subjects recruit future subjects from among their acquaintances. This method is often used for studies involving hidden or hard-to-reach populations.
- *Example:* Studying a specific subculture by having participants refer others in the same subculture.

- Quota Sampling:
- *Description:* The population is segmented into mutually exclusive subgroups, and then a non-random sample is chosen from each subgroup to meet a predefined quota.
- *Example:* Interviewing a fixed number of individuals from different age groups to meet a demographic quota.

---

Each sampling method has its own advantages and limitations, and the choice of method depends on the study’s objectives, the nature of the population, and available resources.

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