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Peerlogic – Data Analyst Intern
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The Centers – Data Analytics Intern
W. R. Berkley – Data Science Intern
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Royal Cyber – Data Scientist Intern

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Day-3 of Statistics 📊

✅ What are Types of Data in Statistics?

The data is classified into majorly four categories:

Nominal data
Ordinal data
Discrete data
Continuous data

Nominal Data

Nominal data is one of the types of qualitative information which helps to label the variables without providing the numerical value.

Ordinal Data

Ordinal data/variable is a type of data that follows a natural order. The significant feature of the nominal data is that the difference between the data values is not determined.

Discrete Data

Discrete data can take only discrete values. Discrete information contains only a finite number of possible values.
Example: Number of students in the class

Continuous Data

Continuous data is data that can be calculated. It has an infinite number of probable values that can be selected within a given specific range.

Example: Temperature range


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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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