Are you seeking to propel your data analytics career to new heights? In the fast-paced world of data analytics, networking can be your secret weapon. Whether you’re a seasoned professional or just starting out, cultivating meaningful connections in the industry can unlock doors to exciting opportunities, fresh perspectives, and invaluable collaborations. Let’s delve into some proven strategies to navigate the data analytics landscape like a pro.

🔍 So, how can you network effectively in this dynamic field? Here are some tried-and-tested tips to help you navigate the data analytics landscape like a pro:

Join Relevant Professional Groups and Associations: Look for organizations, both online and offline, that are dedicated to data analytics. Joining these groups can provide opportunities to meet professionals in the field, attend events, and access resources.

Attend Industry Events and Conferences: Participate in data analytics conferences, workshops, seminars, and networking events. These gatherings offer excellent opportunities to connect with professionals, learn about the latest trends, and exchange ideas.

Utilize Online Platforms: Join online platforms such as LinkedIn, GitHub, and data analytics forums or communities. Engage in discussions, share your insights, and connect with professionals in the field. LinkedIn, in particular, can be a powerful tool for networking and showcasing your skills and experience.

Build a Strong Online Presence: Create and maintain a professional online presence by regularly updating your LinkedIn profile, sharing relevant articles or projects, and contributing to discussions on data analytics topics.

Attend Workshops and Training Programs: Participate in workshops, webinars, and training programs related to data analytics. These events not only help you enhance your skills but also provide opportunities to network with industry experts and fellow professionals.

Volunteer for Industry Projects: Offer your skills and expertise by volunteering for industry projects, hackathons, or open-source initiatives. This allows you to collaborate with other professionals, gain valuable experience, and expand your network.

Informational Interviews: Reach out to professionals in the data analytics field for informational interviews. Ask them about their career paths, experiences, and advice for aspiring professionals. Building genuine relationships through informational interviews can lead to valuable connections and opportunities.

Stay Active on Social Media: Follow influencers, companies, and organizations in the data analytics industry on social media platforms. Engage with their content by liking, commenting, and sharing to establish connections and demonstrate your interest in the field.

Be Proactive and Genuine: When networking, focus on building genuine relationships rather than solely seeking personal gain. Be proactive in reaching out to professionals, express interest in their work, and offer value where you can.

Follow Up: After networking events or interactions, follow up with the individuals you connected with. Send a personalized message expressing your appreciation for the conversation and expressing your interest in staying in touch.

By following these tips and consistently engaging with professionals in the data analytics industry, you can build a strong network that supports your career growth and development.

Remember, networking isn’t just about exchanging business cards or adding connections on LinkedIn—it’s about fostering genuine relationships, sharing knowledge, and supporting each other’s growth journeys. 🌱💼 So, let’s connect, collaborate, and elevate the data analytics community together! 💬

Tableau, a leading data visualization tool, relies on a robust data model to efficiently query connected database tables. Understanding the intricacies of Tableau’s data model is fundamental for harnessing its full potential. In this comprehensive guide, we will delve deep into Tableau’s data model, covering the logical and physical layers, their interactions, and practical examples.

The Heart of Tableau: The Data Model

Every data source you create in Tableau is underpinned by a data model—a critical framework that instructs Tableau on how to interact with your database tables. Let’s explore this concept thoroughly.

The foundation of the data model is built upon the tables you add to the canvas in the Data Source page. This structure can range from a simple, single table to a complex network of interconnected tables via relationships, joins, and unions.

Tableau Desktop’s Data Model comprises two distinct layers:

1. The Logical Layer
2. The Physical Layer

Both layers can work in harmony, but a clear understanding of your source data is essential before deciding which layer to employ.

However the logical layer is generally more forgiving than the physical layer. When you first connect to any data within Tableau, you start with Logical Layer.

If you want to add additional data, you will have to decide between the logical and physical layer.

Logical Layer: A Dynamic Web of Relationships

The initial view you encounter on the data source page canvas is the logical layer of your data source. This logical layer leverages Tableau’s data model to establish connections, often referred to as “noodles,” between two or more data tables. These connections are dynamic and adaptable, only retrieving data when specific fields from these tables are actively utilized.

Furthermore, these relationships have the versatility to accommodate data at varying levels of detail, including the ability to handle many-to-many relationships. Think of this layer as the canvas where relationships come to life, much like an artist’s canvas on the Data Source page.

Example: Consider a scenario where you have a sales table and a customer table. In the logical layer, you can create a dynamic relationship between them using a shared “customer ID” field.

Logical layer represents the canvas for creating relationships between tables.

Physical Layer: Building Structure with Joins and Unions

Within the physical layer, you have the capability to establish joins and/or unions with your data. However, it’s important to note that the physical layer differs from the dynamic and flexible nature of the logical layer. It is most effectively employed when dealing with data that exists at a uniform level of detail, making it suitable for creating one-to-one joins.

In this layer, each logical table must incorporate at least one physical table. To view or implement joins and unions within the physical layer, simply double-click on a logical table.

It’s crucial to exercise caution when utilizing the physical layer for data with varying levels of detail, as this may lead to unintended data duplication.

Example: Imagine you have multiple data tables representing different product categories. The physical layer enables you to join them, creating a unified product catalog.

Physical layer represents the canvas for creating unions & joins between tables.

Remember: The Physical layer joins tables together, while the Logical layer keeps tables separate, but defines relationships between them.

Note: Prior to Tableau 2020.2, Tableau had only the physical layer.

The Dance of the Layers: How They Interact

Since Tableau version 2020.2, the logical layer takes precedence when opening a data source. Adding tables from the left (physical tables) automatically places them in the physical layer, generating corresponding logical tables.

To switch to the physical layer and perform joins, simply double-click on a logical table. It’s worth noting that physical tables joined in this manner will display a small Venn diagram in the resulting logical table.

In your visualization view, the number of logical tables impacts what you see. If you have only one logical table (possibly comprised of joined physical tables), you will see those physical tables in your view. However, when multiple logical tables are present, indicative of relationships, all logical tables will be visible in your view.

Difference between Logical Layer and Physical Layer

Now that we’ve explored both layers in depth, let’s summarize their key characteristics in a tabular format for quick reference:

This tabular overview highlights the distinct characteristics of each layer, aiding in your understanding and selection of the most suitable layer for your specific data modeling needs in Tableau.

To become a Tableau maestro, you must navigate the intricacies of Tableau’s data model, with its logical and physical layers. The logical layer excels at dynamic relationships, while the physical layer brings structure to your data. Understanding their interactions is the key to unleashing the full potential of Tableau for data analysis and visualization. So, roll up your sleeves, explore these layers, and let your data-driven insights shine brilliantly in Tableau!

I hope you liked the article. Stay Tuned…!!!

Happy Tableauing…!!!

SQL is a powerful language used for managing and manipulating data in databases. One of the most important features of SQL is the ability to join tables, which allows you to combine data from multiple tables into a single result set. In this blog post, we will explore the uses of joins in SQL, the different types of joins, and the syntax and examples of each type.

Uses of Joins

Joins are used in SQL to retrieve data from multiple tables that are related to each other. For example, if you have a database that stores information about employees, departments, and projects, you might have separate tables for each of these entities. To get a complete picture of the data, you would need to join these tables together. Joins allow you to:

1. Combine data from two or more tables into a single result set
2. Retrieve data that is stored in related tables
3. Perform complex data analysis by combining data from multiple tables
4. Optimize database performance by reducing the number of queries needed to retrieve data

Conditions for Applying Joins

These conditions are necessary to ensure that the data retrieved from the joined tables is accurate and meaningful. Here are the main conditions for applying joins in SQL:

Common columns: There must be at least one column that is common between the two tables being joined. This is necessary to establish the relationship between the tables and determine which rows should be combined.

Data types: The common columns in the joined tables must have the same data type. For example, if the common column in one table is an integer, the corresponding column in the other table must also be an integer.

Compatible data: The data in the common columns must be compatible. For example, if one table uses a different unit of measurement than the other table, the values in the common column may need to be converted before the join can be applied.

Null values: Null values in the common columns can cause issues when applying joins. To avoid this, you may need to use functions like COALESCE or IFNULL to replace null values with a default value.

Join type: The type of join used must be appropriate for the data you are trying to retrieve. For example, if you want to retrieve only the rows that have matching values in both tables, you would use an INNER JOIN.

Table aliases: When joining multiple tables, it is a good practice to use table aliases to simplify the query and avoid naming conflicts.

Performance considerations: Depending on the size of the tables being joined, the query may take a long time to execute. To improve performance, you can use indexing on the common columns or limit the number of rows being retrieved.

By ensuring that these conditions are met, you can apply joins in SQL to combine data from multiple tables and retrieve the exact information you need.

Types of Joins

Joining tables allows you to retrieve data that is stored in different tables and merge it into a single result set. There are several types of joins that you can use in SQL, including:

Inner Join: An inner join returns only the rows that have matching values in both tables. In other words, it returns the intersection of the two tables.

Left Join: A left join returns all the rows from the left table and the matching rows from the right table. If there are no matching rows in the right table, the result set will contain null values.

Right Join: A right join returns all the rows from the right table and the matching rows from the left table. If there are no matching rows in the left table, the result set will contain null values.

Full Outer Join: A full outer join returns all the rows from both tables, including the rows that have no matching values in the other table. If there are no matching rows in one of the tables, the result set will contain null values.

There are two more types of join cross join and self join which we will observe in next article.

Syntax for Joins in SQL

In SQL, a join combines rows from two or more tables based on a related column between them. The syntax for applying joins in SQL can vary depending on the type of join being used.

Here is a general syntax for applying joins:

SELECT column1, column2, ...
FROM table1
[INNER/LEFT/RIGHT/FULL OUTER] JOIN table2
ON table1.column = table2.column;

SELECT: specifies the columns you want to retrieve from the result set
FROM: specifies the first table from which to retrieve data
[INNER/LEFT/RIGHT/FULL OUTER] JOIN: specifies the type of join to use
table2: specifies the second table to join
ON: specifies the condition that determines which rows to join

Example

To further illustrate the different types of joins, let us consider the following tables:

Employee Table:

Inner Join Example:

Let’s say we want to retrieve the names of all employees in the Sales department. We can use an inner join as follows:

SELECT employee_name
FROM employee
INNER JOIN department
ON employee.department_id = department.department_id
WHERE department.department_name = 'Sales';

This query will return the following result:

Left Join Example:

Now let’s say we want to retrieve the names of all employees and their associated projects. We can use a left join as follows:

SELECT employee_name, project_name
FROM employee
LEFT JOIN project
ON employee.employee_id = project.employee_id;

This query will return the following result:

Right Join Example

Now let’s say we want to retrieve the names of all projects and their associated employees. We can use a right join as follows:

SELECT employee_name, project_name
FROM project
RIGHT JOIN employee
ON project.employee_id = employee.employee_id;

This query will return the following result:

Full Outer Join Example

Finally, let’s say we want to retrieve all the employees and their associated projects, including those without any projects assigned. We can use a full outer join as follows:

SELECT employee_name, project_name
FROM employee
FULL OUTER JOIN project
ON employee.employee_id = project.employee_id;

This query will return the following result:

Conclusion

In conclusion, joins are an essential part of SQL, as they allow you to combine data from multiple tables into a single result set. The different types of joins provide flexibility in retrieving data that meets your specific needs. It is important to understand the syntax and use cases for each type of join to efficiently and accurately retrieve the data you require.

Additionally, it is crucial to understand the relationships between tables, such as primary and foreign keys, to ensure that the joins you perform are accurate and meaningful. Practice and experimentation with SQL joins can help you develop a deeper understanding of their functionality and improve your ability to write effective SQL queries.

In conclusion, SQL joins are an essential tool for anyone working with relational databases. By using joins, you can combine data from multiple tables, enabling you to retrieve the exact information you need. There are several types of joins available in SQL, each with its own syntax and specific use cases. By mastering the various types of joins, you can effectively retrieve and analyze data, making you a more effective SQL user.

Database Management Systems (DBMS) are an essential part of modern computing, and they play a critical role in managing large amounts of data for businesses, organizations, and individuals. However, understanding the basic concepts and terminologies used in DBMS can be challenging, especially for those who are new to this field.

In this blog, we will explore some of the most important terminologies used in DBMS, including tables, fields, records, primary keys, foreign keys, indexes, queries, normalization, and transactions. We will provide clear explanations of each of these concepts and how they relate to one another, as well as practical examples to help you better understand how they work in real-world scenarios.

Whether you are a student, a professional, or just someone who wants to learn more about DBMS, this blog will provide you with a solid foundation of knowledge that will help you to better understand how databases work and how they can be used to manage and analyze large amounts of data. So, let’s dive in and explore this..!

Key Terminologies in DBMS

Database: A database is a collection of data that is organized in a particular way so that it can be easily accessed, managed, and updated. It is a structured way of storing, retrieving, and managing data. A database can contain one or more tables, each of which contains related data.

Table: A table is a collection of related data organized in rows and columns. It is the basic unit of storage in a relational database. Each column represents a specific attribute or field of the data, while each row represents a unique record or instance of that data. Tables can be linked together using primary and foreign keys to establish relationships between them.

Field: A field is a specific piece of information within a table. It is also known as a column or attribute. Each field has a unique name and data type, such as text, numeric, date, or boolean. Fields can be used to store a wide range of data, from simple text to complex data structures.

Record: A record is a complete set of data for a specific entity or item within a table. It is also known as a row or tuple. Each record contains values for all the fields in the table. For example, in a customer table, each record would represent a single customer and would include information such as their name, address, and phone number.

Primary key: A primary key is a unique identifier for each record in a table. It ensures that each record can be uniquely identified and is used to link records in different tables. A primary key can be a single field or a combination of fields. For example, in a customer table, the primary key could be a unique customer ID field.

Foreign key: A foreign key is a field in one table that refers to the primary key in another table. It is used to establish relationships between tables. For example, in an orders table, the customer ID field would be a foreign key that refers to the primary key in the customer table.

Index: An index is a data structure that improves the speed of data retrieval operations. It is created on one or more fields in a table. An index allows the database to quickly locate records based on the values in the indexed fields. Without an index, the database would need to scan the entire table to find the records, which can be slow and inefficient.

Query: A query is a request for data from a database. It is used to retrieve, update, or delete data. A query can be simple, such as retrieving all records from a table, or complex, such as retrieving records that meet specific criteria or that are related across multiple tables.

Normalization: Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves breaking down tables into smaller, more specialized tables and establishing relationships between them. Normalization helps to ensure that each piece of data is stored in only one place, which reduces the risk of inconsistencies or errors.

Transaction: A transaction is a sequence of database operations that are executed as a single unit of work. It ensures that all operations are completed or none of them are executed. Transactions are used to maintain the integrity of the database by ensuring that all changes are committed or rolled back as a group. For example, a transaction could include updating multiple records in different tables to ensure that the changes are made consistently.

Conclusion

In conclusion, understanding the basic terminologies used in DBMS is crucial for anyone who wants to work with databases. We hope that this blog has provided you with a clear understanding of the concepts of tables, fields, records, primary keys, foreign keys, indexes, queries, normalization, and transactions. By understanding these basic concepts, you can better manage and analyze large amounts of data and make informed decisions based on your data.

Remember, DBMS is a complex subject that requires practice and experience to master. However, with the knowledge gained from this blog, you can start to explore more advanced topics and learn how to use DBMS to solve real-world problems. If you have any questions or need further clarification on any of the concepts covered in this blog, don’t hesitate to do your own research or seek help from experts in the field.

Thank you for taking the time to read this blog, and we hope that it has been helpful in improving your understanding of DBMS terminologies.

When writing SQL queries, it’s important to understand the order in which the various clauses are executed. This is known as the order of execution, and it can have a significant impact on the performance and results of your queries. In this blog post, we’ll explore the typical order of execution for a SELECT statement in SQL.

Order of execution

This are the points in sequence for Order of Execution in SQL.

FROM clause: The FROM clause specifies the table or tables that the query will retrieve data from. This clause is executed first, and it identifies the underlying data source for the query.

JOIN clause: If there are any JOIN clauses in the query, they are executed after the FROM clause. Joining tables is a way to combine data from multiple tables, and it can be an expensive operation if the tables are large or if there are many joins. Joining tables should be done only when necessary.

WHERE clause: The WHERE clause is executed after the JOIN clause, and it is used to filter rows based on specified conditions. This is an important clause because it can significantly reduce the amount of data that needs to be processed by subsequent clauses.

GROUP BY clause: If there is a GROUP BY clause in the query, it is executed after the WHERE clause. The GROUP BY clause is used to group the data into sets based on the specified columns. This is typically used with aggregate functions such as SUM, AVG, COUNT, MAX, and MIN.

HAVING clause: The HAVING clause is executed after the GROUP BY clause, and it is used to filter groups based on specified conditions. This clause is similar to the WHERE clause, but it operates on groups instead of individual rows.

SELECT clause: The SELECT clause is executed after all of the previous clauses, and it selects the columns to be returned in the query result. This is where you specify the data that you want to retrieve from the query.

ORDER BY clause: Finally, if there is an ORDER BY clause in the query, it is executed last. The ORDER BY clause is used to sort the query result based on the specified columns and sort order.

Optimizing Order of Execution

Although the typical order of execution listed above is a good guideline for understanding how a SQL query is processed, it’s important to note that some database management systems may optimize the order of execution based on the specific query and data being accessed.

For example, some database management systems may push some of the filter conditions from the WHERE clause into the JOIN clause to reduce the amount of data that needs to be processed. Other systems may execute the SELECT clause before the GROUP BY clause to optimize the use of indexes.

To optimize the order of execution for a specific query, you can use the EXPLAIN command in SQL to see the execution plan for the query. The execution plan shows how the database management system will execute the query, and it can help you identify any potential performance issues.

Practical Example

Let us work with the Super Store Dataset and use the Orders worksheet. The dataset will look something like this.

Dataset will have around 9,994 records altogether.

Now let’s consider this SQL Query

SELECT Category, SUM(Sales) AS [Total Sales]
FROM Orders
WHERE year([Order Date]) = 2016
GROUP BY Category
HAVING SUM(Sales) > 10000
ORDER BY SUM(Sales) DESC;

In the above query we have all the components of Order of Execution leaving Joins which we will observe in our later content.

According to above query –

FROM Clause will be executed first from all the commands as it will reterive the data from the dataset. Followed by this we have a WHERE clause condition where SQL has to observe the data where the order data is in the year 2016. Once WHERE clause is executed next it will go to GROUP BY clause where it will group all the categories based on the WHERE condition.

After successful execution of GROUP BY, next clause will be HAVING clause which is used with the aggregating data, hence it will be used to apply condition to the aggregation SUM(Sales). After this it will reterive all the records which SQL has searched through the order of execution.

So next command which will be executed is SELECT clause. Atlast if we want to arrange the data in a particular order, we can use ORDER BY clause with either ascending order or the descending order.

After execution of the above query the output which we will get is shown below,

Conclusion

In conclusion, understanding the order of execution in SQL is important for optimizing the performance and results of your queries. By following the typical order of execution and optimizing it as needed, you can ensure that your queries are efficient and effective in retrieving the data you need.

We will meet with more such content in our future blogs. Till then stay tuned.

Happy Learning !!

In SQL, a key is a column or a group of columns in a table that is used to identify each row in the table. Keys are essential components of a relational database management system (RDBMS) as they help to ensure the integrity and consistency of the data stored in the database.

Uses of Keys in DBMS

There are many uses of Keys in DBMS some of them are listed below,

Uniquely identifying rows: Keys are used to identify each row in a table in a unique manner, which helps to prevent duplicate records and to ensure the accuracy and consistency of the data in the table.

Establishing relationships between tables: Keys are used to establish relationships between tables in a relational database. The primary key of one table can be used as a foreign key in another table to link the two tables together.

Enforcing data integrity: Keys are used to enforce data integrity in a table by ensuring that each row in the table is uniquely identified and that the data in the table is accurate and consistent.

Improving query performance: Keys can improve the performance of queries by allowing the database to quickly locate and retrieve the data that is needed.

Keys are an essential component of SQL that are used to ensure the integrity and consistency of the data stored in a database. They are used to identify rows, establish relationships between tables, enforce data integrity, and improve query performance.

Different Types of Keys

There are different types of keys which are used in SQL some of them are as follows

1. Primary Key
2. Foreign Key
3. Candidate Key
4. Unique Key
5. Composite Key
6. Alternate Key

Primary key: A primary key is a column or group of columns in a table that uniquely identifies each row in that table. The primary key is used to enforce the integrity of the data in the table and to prevent duplicate records.

For example, in a table of employees, the employee ID column may be designated as the primary key.

Foreign key: A foreign key is a column or group of columns in one table that refers to the primary key of another table. The foreign key is used to establish a relationship between two tables, and to ensure that the data in the two tables is consistent.

For example, in a table of orders, the customer ID column may be a foreign key that refers to the customer ID primary key in a table of customers.

Candidate key: A candidate key is a column or group of columns in a table that could potentially be used as the primary key. A table can have multiple candidate keys, but only one primary key.

For example, in a table of students, both the student ID column and the email address column could be candidate keys.

Unique key: A unique key is a column or group of columns in a table that has a unique value for each row in the table, but is not designated as the primary key. A table can have multiple unique keys.

For example, in a table of employees, the employee email address column may be designated as a unique key.

Composite key: A composite key is a combination of two or more columns in a table that together uniquely identify each row in the table.

For example, in a table of orders, a composite key might be the combination of the order ID and the order date columns.

Alternate Key: An alternate key is a candidate key that is not chosen as the primary key of a table. It is a unique identifier for each row in the table, just like the primary key, but it is not used for referential integrity or to establish relationships with other tables. Instead, it can be used for indexing or querying purposes.

For example, in table of employees, “Email” column could be designated as an alternate key, which means that it is not the primary key but can still be used to uniquely identify each row in the table.

Among all the keys above two main keys which are used most of the time are going to be Primary Key and Foreign key

Primary Key and Foreign Key Scenario based Understanding

A primary key and a foreign key are two important types of keys in a database management system (DBMS) that help to establish relationships between tables.

A primary key is a column or a set of columns in a table that uniquely identifies each row in that table. The primary key is used to enforce the integrity of the data in the table and to prevent duplicate records. For example, in a table of students, the student ID column can be designated as the primary key since each student has a unique ID number.

Here is an example of a table of students with the student ID column as the primary key:

A foreign key is a column or a set of columns in a table that refers to the primary key of another table. The foreign key is used to establish a relationship between two tables and to ensure that the data in the two tables is consistent. For example, let’s assume that we have a table of courses with a primary key of course ID. We can create a foreign key in the table of students to establish a relationship between the two tables. In this case, the foreign key would be the course ID column in the table of students that refers to the course ID primary key in the table of courses.

Here is an example of a table of courses with the course ID column as the primary key:

Now, we can create a foreign key in the table of students to establish a relationship with the table of courses. Here is an example of the modified table of students with the course ID column as the foreign key:

In this example, the course ID column in the table of students refers to the primary key of the table of courses, which ensures that the data is consistent between the two tables.

Let’s take one more example

Let’s say you have a table called “Customers” in a database. Each record in the table represents a different customer, and you want to ensure that each customer has a unique identifier. You could create a column called “CustomerID” and designate it as the primary key for the table.

Then, when you add a new customer record to the table, you would assign a unique value to the “CustomerID” column for that record. This makes it easy to retrieve or modify individual customer records based on their unique identifier.

Also you have a second table called “Orders” that contains information about customer orders. Each order is associated with a specific customer from the “Customers” table. To establish this relationship between the two tables, you could create a column called “CustomerID” in the “Orders” table and designate it as a foreign key.

This means that the “CustomerID” column in the “Orders” table references the “CustomerID” column in the “Customers” table. When you add a new order record to the “Orders” table, you would specify the “CustomerID” value for the customer who placed the order.

Then, you can use this foreign key relationship to join the two tables together and retrieve information about specific customers and their orders.

I hope now you have understood this concepts well !! 🤔💭

Difference between Primary Key and Foreign Key

Knowing the difference between the Primary Key and Foreign Key is very important and crucial when you are learning SQL.

This are some of the points you should remember.

Conclusion

In conclusion, keys are a fundamental concept in SQL and are essential for creating relationships between tables in a database. There are different types of keys, such as primary keys, foreign keys, and composite keys, each serving a specific purpose in ensuring data integrity and maintaining data consistency.

Primary keys uniquely identify each row in a table, and foreign keys establish relationships between tables. Composite keys combine multiple columns to create a unique identifier for a row.

It’s important to carefully choose and define keys when designing a database, as they play a crucial role in ensuring the accuracy and reliability of the data. Understanding keys and their use cases will help you build efficient and robust database structures that can handle complex data relationships and queries.

I hope you have understood the key concepts of the blog.

Happy Learning !!

SQL (Structured Query Language) is a programming language used to manage and manipulate relational databases. It is used to perform tasks such as retrieving, inserting, updating, and deleting data from a database.

Relational databases store data in tables, which are composed of columns and rows. Columns represent the attributes or characteristics of the data, while rows represent individual records. SQL is used to interact with these tables by querying, modifying, and updating the data.

Different Types of SQL Commands

SQL commands can be categorized into four main types: Data Manipulation Language (DML), Data Definition Language (DDL), Data Control Language (DCL), and Transaction Control Language (TCL).

Data Manipulation Language (DML)

DML commands are used to manipulate data stored in a database. The most common DML commands are:

INSERT: This command is used to insert new data into a table. You can specify the values to be inserted, or you can insert data from another table.

UPDATE: This command is used to modify existing data in a table. You can specify which rows to update and what values to change.

DELETE: This command is used to delete data from a table. You can specify which rows to delete, or you can delete all the rows in a table.

Data Definition Language (DDL)

DDL commands are used to define the structure of a database. The most common DDL commands are:

CREATE: This command is used to create a new table, view, or other database object. You specify the columns, data types, and other properties of the object being created.

ALTER: This command is used to modify the structure of a table or other database object. You can add, modify, or delete columns, change data types, and perform other modifications.

DROP: This command is used to delete a table or other database object. Once a table or object is dropped, it cannot be recovered.

Data Query Language (DQL)

DQL commands are used to retrieve data from one or more tables in a database. The most common DQL command is:

SELECT: This command is used to retrieve data from one or more tables. It can be used with various clauses to filter and sort the data, and it can also be used to perform calculations and join multiple tables together.

Data Control Language (DCL)

DCL commands are used to control access to data in a database. The most common DCL commands are:

GRANT: This command is used to grant permissions to a user or role. You can specify which actions the user or role is allowed to perform on a particular table or object.

REVOKE: This command is used to revoke permissions from a user or role. You can remove the permissions that were previously granted.

Transaction Control Language (TCL)

TCL commands are used to control transactions in a database. Transactions are sequences of SQL statements that are executed as a single unit of work. The most common TCL commands are:

COMMIT: This command is used to permanently save changes made in a transaction. Once a transaction is committed, the changes cannot be rolled back.

SAVEPOINT: A savepoint is a marker within a transaction that allows you to roll back part of the transaction while leaving the rest of the changes intact.

ROLLBACK: This command is used to undo changes made in a transaction. If there is an error or problem with the transaction, you can use the ROLLBACK command to undo the changes and return the database to its previous state.

In addition to these basic SQL commands, there are also many advanced SQL commands and functions that can be used to perform complex operations on data in a database. Some examples include JOIN, UNION, GROUP BY, and HAVING.

I hope you liked this content, in the next content we shall discuss each category of commands in detail with different examples.

Happy Learning !!

As we have heard that

“Data is new fuel, to run a business”.

I hope you agree with it. Although all data is important somehow that does not necessarily mean that we need all the data available to improve businesses. In order for data to be valuable to your business. It should help you with the following points

1. Address certain business needs
2. Solve biggest problem of a business.
3. Achieveing your strategic goals.

To be able to determine a good strategy, you first need to understand your business objectives. So here are some usecases through which data can be used in industry.

Data Driven Decisions through data

Using data to make better informed, fact-based decisions refers to the process of collecting, analyzing, and interpreting data to inform business decisions. The goal is to make decisions that are based on data and facts, rather than intuition, assumptions, or guesses.

By collecting and analyzing data, organizations can gain insights into customer behavior, market trends, operational efficiency, and other factors that are relevant to their business. This information can then be used to inform decisions about product development, marketing strategies, customer service, and other areas of the business.

For example, data analysis can help a company understand which products are selling well and which are not, which marketing channels are most effective, and which customer segments are most valuable. This information can then be used to make informed decisions about which products to focus on, where to allocate marketing resources, and how to best serve customers.

In addition to increasing the accuracy of decisions, using data to make better informed, fact-based decisions can also help organizations make decisions more quickly and efficiently. Rather than relying on intuition or assumptions, decision-makers can use data to identify trends, patterns, and other important insights that can inform their decisions.

Better Understanding of correct market and customers

Data helps in understanding markets and customers by providing insights into their behavior, preferences, and patterns. This information can be gathered through various methods such as surveys, customer interactions, and digital tracking. By analyzing data, companies can identify trends, predict customer needs, and make informed decisions about product development, marketing, and sales strategies.

Additionally, data can also help companies measure the success of their initiatives and make improvements where necessary. In short, data provides a comprehensive view of markets and customers that is essential for making informed business decisions.

In business, data plays a crucial role in understanding markets and customers by providing valuable insights into their behavior, preferences, and patterns. This information is critical for making informed decisions that can lead to better customer experiences, increased customer loyalty, and overall business success.

Here are some ways in which data can help you understand your markets and customers:

Identifying customer needs and preferences: Data collected through customer interactions, surveys, and digital tracking can provide information on what customers want, need, and value in a product or service. This information can be used to develop products and services that meet customers’ needs and improve their overall experience.

Segmentation and targeting: Data analysis can help companies segment their customers based on various characteristics, such as demographics, behavior, and purchase history. This information can then be used to target specific customer groups with tailored messages and offers.

Measuring customer satisfaction: Data collected through customer feedback mechanisms, such as surveys and online reviews, can provide insights into how customers perceive a company’s products and services. This information can help companies identify areas for improvement and measure the success of their customer satisfaction initiatives.

Tracking customer behavior: Digital tracking tools, such as website analytics and mobile app analytics, can provide data on how customers interact with a company’s products and services. This information can be used to optimize the customer journey and improve overall customer experience.

Predictive analysis: Predictive analytics uses historical data and machine learning algorithms to identify patterns and make predictions about future customer behavior. This information can help companies anticipate customer needs and make informed decisions about product development, marketing, and sales strategies.

Data helps companies understand their markets and customers by providing a comprehensive view of their behavior, preferences, and patterns. This information is essential for making informed business decisions that can lead to better customer experiences and increased business success.

Smart Devices and Intelligent Products

Data can be used to offer smarter services and intelligent products by providing insights into customer behavior and preferences. This information can then be used to develop products and services that are more tailored to customer needs and improve their overall experience.

Here are some ways in which data can be used to offer smarter services and intelligent products:

Personalization: Data analysis can provide insights into individual customer preferences, allowing companies to personalize their products and services to meet the specific needs of each customer. This can result in a more satisfying customer experience and increased customer loyalty.

Predictive maintenance: Data from IoT devices and sensors can be used to predict when equipment will fail, allowing companies to schedule maintenance before a problem occurs. This can improve equipment reliability and reduce downtime.

Optimization: Data analysis can be used to optimize the performance of products and services, leading to improvements in efficiency, cost-effectiveness, and customer satisfaction.

Enhanced customer service: Data collected through customer interactions and feedback mechanisms can be used to identify common customer issues and improve the quality of customer service. Additionally, data can also be used to develop self-service options, such as online chatbots and knowledge bases, that can improve the customer experience.

Data plays a crucial role in offering smarter services and intelligent products. By providing insights into customer behavior and preferences, data can be used to develop products and services that are more tailored to customer needs and improve their overall experience.

Improving & Automating Business Processes

Data is used to improve and automate business processes by providing valuable insights into process efficiency and effectiveness. This information can then be used to identify areas for improvement and implement changes that can increase efficiency, reduce errors, and improve the overall customer experience.

Here are some ways in which data can be used to improve and automate business processes:

Process mapping: Data analysis can be used to map out business processes and identify bottlenecks, inefficiencies, and areas for improvement. This information can then be used to redesign processes for increased efficiency.

Workflow automation: Data can be used to automate repetitive tasks and reduce the risk of errors. This can lead to improved efficiency and reduced costs, allowing companies to allocate more resources to higher-value tasks.

Monitoring and tracking: Data collected through process monitoring and tracking tools can provide insights into process performance, allowing companies to identify areas for improvement and measure the success of their process improvement initiatives.

Process optimization: Data analysis can be used to optimize processes by identifying bottlenecks, inefficiencies, and areas for improvement. This information can then be used to make informed decisions about process redesign and improvement.

Predictive analytics: Predictive analytics uses historical data and machine learning algorithms to identify patterns and make predictions about future process outcomes. This information can be used to identify potential process failures before they occur, allowing companies to proactively address issues and reduce downtime.

We can say that over here data plays a important role in improving and automating business processes. By providing valuable insights into process efficiency and effectiveness, data can be used to identify areas for improvement and implement changes that can increase efficiency, reduce errors, and improve the overall customer experience.

Monetizing Data

Data monetization refers to the process of converting data into a revenue-generating asset. This involves collecting and analyzing data, and then using the insights gained from that analysis to create new revenue streams or improve existing ones.

Companies can monetize their data in several ways, including:

Selling data: Companies can sell their data to third-party organizations, such as market research firms, data brokers, and other businesses, who can then use it for their own purposes.

Data-driven products and services: Companies can use their data to create new products and services, such as personalized recommendations, targeted advertising, and predictive analytics, that can be sold to customers.

Advertising: Companies can use their data to improve the targeting and relevance of their advertising, leading to higher engagement and conversion rates, and increased revenue.

Licensing: Companies can license their data to other organizations, who can then use it to develop their own products and services.

Improving existing business processes: Companies can use their data to improve their existing business processes, such as supply chain management, customer service, and product development, leading to increased efficiency and cost savings.

Data monetization involves converting data into a valuable asset that can be used to create new revenue streams or improve existing ones. Companies can monetize their data in several ways, including selling data, creating data-driven products and services, using data for advertising, licensing data, and improving existing business processes.

Conclusion & Summary

In conclusion, data plays a crucial role in modern business. It can be used in several strategic ways to drive business success and improve customer experience. These five data use cases include:

Understanding markets and customers: By analyzing customer behavior and preferences, data can provide valuable insights into customer needs and preferences, allowing companies to tailor their products and services to meet customer needs.

Offering smarter services and intelligent products: Data analysis can provide insights into customer behavior and preferences, allowing companies to personalize their products and services and improve the overall customer experience.

Improving and automating business processes: Data can be used to improve and automate business processes by providing valuable insights into process efficiency and effectiveness. This information can then be used to identify areas for improvement and implement changes that can increase efficiency and reduce errors.

Data monetization: Data monetization involves converting data into a valuable asset that can be used to create new revenue streams or improve existing ones. Companies can monetize their data in several ways, including selling data, creating data-driven products and services, using data for advertising, licensing data, and improving existing business processes.

Making informed decisions: Data analysis can provide insights into customer behavior, market trends, and other factors that impact business success. This information can then be used to make informed decisions about product development, marketing, and sales strategies.

In conclusion, data is a valuable asset that can be leveraged in several strategic ways to drive business success and improve the customer experience. Companies that effectively harness the power of data are more likely to achieve their business goals and remain competitive in today’s fast-paced business environment.

I hope you liked this article. In the next article we will see how data can be useful to improve your decisions in effective manner.

Stay tuned! Happy Learning!

Let’s assume you are a data analyst working for a company that has the following three sectors: Marketing, Sales and Finance. Now, let’s assume that each department maintains a separate database.

This could lead to a situation wherein each department has its own version of the facts. For a question such as ‘What is the total revenue of the last month?’, every department might have a different answer. This is because each department draws information from a different database.

This is where a data warehouse can prove to be useful. It can help with creating a single version of the truth and the facts. A data warehouse would thus be the central repository of data of the entire enterprise.

What is Data Warehouse?

A data warehouse is a system used for reporting and data analysis, and is considered a core component of business intelligence. It is a large, centralized repository of data from one or more sources, that is used to support the reporting and analysis of business data.

Data warehouses are designed to support the efficient querying and analysis of data, and typically include a range of tools and technologies for data integration, data management, and reporting. They are commonly used to support decision making in organizations by providing a single source of truth for data.

A data warehouse is like a big library, but instead of books, it stores information about things like sales, customer information, and inventory. Just like in a library, where you can go to find a book you need, in a data warehouse you can go to find information you need to make important decisions.

Like how much money a store made last month or how many blue teddy bears were sold last week. It’s like a big brain for a company to make sure they know what’s happening and make smart choices.

Properties & Characteristics of Data Warehouse

Data warehouses have several key properties that define their characteristics and capabilities:

Subject-oriented: Data in a data warehouse is organized around specific subjects, such as sales or inventory, rather than specific applications or transactions.

Integrated: Data in a data warehouse is integrated from a variety of different sources, such as transactional systems, external data, and legacy systems.

Time-variant: Data in a data warehouse is stored with a timestamp, allowing for the analysis of historical data over time.

Non-volatile: Once data is loaded into a data warehouse, it is not updated or deleted, allowing for accurate reporting and analysis of historical data.

Read-optimized: Data in a data warehouse is optimized for read-heavy workloads, such as reporting and analysis, rather than write-heavy workloads, such as transactional processing.

Schema-on-write: Data in a data warehouse is transformed and structured before it is loaded.

Scalable: Data warehouses are designed to handle large amounts of data and to support a large number of concurrent users.

Multi-dimensional: Data in a data warehouse is organized in a multi-dimensional structure, such as a star or snowflake schema, to support efficient querying and analysis.

Structure of Data Warehouse

Primary methods of designing a data warehouse is dimensional modelling. The two key elements of dimensional modelling include facts and dimensions, which are basically the different types of variables that are used in data warehouse. When this two elements are arranged in a particular manner it is called as “schema design”.

In a data warehouse, “facts” and “dimensions” are two types of data that are used to organize and analyze information.

Facts: They are the numerical data that are being analyzed, such as sales figures, revenue, or inventory levels. They are often stored in tables called “fact tables”.

Dimensions: They are the context in which the facts are being analyzed, such as time, location, or product category. They are also called as “metadata”. They are often stored in tables called “dimension tables”.

For example, a fact table might contain information about sales figures, while a dimension table might contain information about the products that were sold, the time the sales occurred, or the location of the store where the sales took place. Together, the facts and dimensions allow you to slice and dice the data in various ways, to answer different questions and gain insights from the data.

Components of Data Warehouse

The structure of a data warehouse typically includes several components:

Data sources: These are the various systems and databases that provide the data that will be loaded into the data warehouse. Data sources can include transactional systems, external data feeds, and legacy systems.

Staging area: This is an intermediate location where the data is temporarily stored after it is extracted from the data sources, but before it is transformed and loaded into the data warehouse. The staging area is used to perform initial data validation and cleaning, and to resolve any data quality issues.

Data integration: This is the process of integrating data from different sources, and resolving any inconsistencies or conflicts. It also includes data scrubbing, data validation and data cleansing.

Data transformation: This process involves transforming the data into a format that is consistent and can be loaded into the data warehouse. This includes things like data type conversion, data mapping, and data aggregation.

Data loading: This process involves loading the data into the data warehouse, and may also include indexing the data to make it more easily searchable and queryable.

Data mart: A Data mart is a subset of a Data warehouse, it is a small and focused data warehouse that is built to serve a specific business function or department.

Data warehouse schema: This is the structure of the data warehouse, which defines how the data is organized and how it can be queried and analyzed. Common data warehouse schemas include star and snowflake schemas.

Metadata: This is data about the data, such as definitions, descriptions, and relationships. Metadata is used to understand the data and to ensure that it is accurate and consistent.

Business Intelligence (BI) and Analytics Tools: These are the tools used to query, analyze and report on the data in the data warehouse. They can range from simple reporting tools to more advanced analytics and visualization platforms.

Key Points on Data Warehouse

• A data warehouse is a large, centralized repository of data that is specifically designed for reporting and analysis.
• Data is extracted from various sources, transformed to fit the data warehouse schema and then loaded into the data warehouse.
• Data warehousing uses a process called ETL (Extract, Transform, Load) to move data from various sources into a central repository.
• Data is transformed into a format that is easy to query and analyze before it is loaded into the data warehouse
• Data warehouse typically includes several components such as Data sources, Staging area, Data integration, Data transformation, Data loading, Data mart, Data warehouse schema, Metadata and Business Intelligence (BI) and Analytics Tools.
• Data warehouse enables organizations to store and analyze large amounts of data in a way that is efficient, accurate, and easily queryable.

Conclusion

In the next blog we will discuss about Extract, Transfrom & Load (ETL) and see how it is related to Data Warehouse. I hope you are excited about the same.

Stay Tuned !! Happy Learning !!

Introduction to Python Lists

In Python, a list is a collection of items that are ordered and changeable. Lists are defined by square brackets [] and the items inside can be of any data type, such as integers, strings, or even other lists. Lists are commonly used to store and manipulate data in Python.

Here’s an example of a list:

fruits = ['apple', 'banana', 'orange']

Characteristics of Lists

In Python, lists have several characteristics that make them useful for different types of data manipulation and storage:

Ordered: Lists maintain the order of elements that are added to them, allowing you to access items by their index.

Changeable: Lists are mutable, meaning you can add, remove, and modify elements in a list.

Heterogeneous: Lists can contain elements of different data types, such as integers, strings, and other lists.

Indexing: Lists can be indexed, allowing you to access individual elements of a list by specifying their position in the list.

Slicing: Lists can be sliced, which means you can access a sub-list by specifying a start and end index.

Functions and methods: Lists have a variety of built-in functions and methods that can be used to perform operations on them, such as adding, removing, and sorting elements.

Iterable: Lists are iterable, so you can use them in for loops, list comprehension and other iterable context.

Pass by Reference: Lists are passed by reference and not by value, so any change made on list will reflect on the original copy of the list.

Indexing in Lists

In Python, lists are indexed, which means you can access individual elements of a list by specifying their position in the list. Indexing starts at 0, so the first element of a list is at index 0, the second element is at index 1, and so on.

You can access an element of a list using square brackets [ ] and the index of the element.

For example:

fruits = ['apple', 'banana', 'orange']
print(fruits[0]) # Output: 'apple'
print(fruits[1]) # Output: 'banana'
print(fruits[2]) # Output: 'orange'

You can also use negative indexing to access elements from the end of the list. The last element of a list is at index -1, the second-to-last element is at index -2, and so on.

fruits = ['apple', 'banana', 'orange']
print(fruits[-1]) # Output: 'orange'
print(fruits[-2]) # Output: 'banana'
print(fruits[-3]) # Output: 'apple'

You can also use indexing to modify an element of a list by assigning a new value to that index.

fruits = ['apple', 'banana', 'orange']
fruits[1] = 'mango'
print(fruits) # Output: ['apple', 'mango', 'orange']

It’s important to note that if you try to access an index that does not exist in the list, you will get an “IndexError” Exception.

Updating Lists

In Python, lists are mutable, which means you can update the elements of a list after it has been created. There are several ways to update elements in a list:

1. Assignment: You can use indexing to change the value of an element in a list by assigning a new value to that index.

fruits = ['apple', 'banana', 'orange']
fruits[1] = 'mango'
print(fruits) # Output: ['apple', 'mango', 'orange']

2. Append: The “append()” method allows you to add an element to the end of a list.

fruits = ['apple', 'banana', 'orange']
fruits.append('kiwi')
print(fruits) # Output: ['apple', 'banana', 'orange', 'kiwi']

3. Insert: The “insert()” method allows you to add an element at a specific index in a list.

fruits = ['apple', 'banana', 'orange']
fruits.insert(1, 'mango')
print(fruits) # Output: ['apple', 'mango', 'banana', 'orange']

4. Extend: The “extend()” method allows you to add multiple elements to a list at once.

fruits = ['apple', 'banana', 'orange']
new_fruits = ['mango', 'kiwi']
fruits.extend(new_fruits)
print(fruits) # Output: ['apple', 'banana', 'orange', 'mango', 'kiwi']

5. Concatenation: You can concatenate two lists using the “+” operator.

fruits = ['apple', 'banana', 'orange']
new_fruits = ['mango', 'kiwi']
fruits += new_fruits
print(fruits) # Output: ['apple', 'banana', 'orange', 'mango', 'kiwi']

6. Assignment with slices: You can also change multiple elements of a list by using slicing and assignment.

fruits = ['apple', 'banana', 'orange']
fruits[1:3] = ['mango','kiwi']
print(fruits) # Output: ['apple','mango','kiwi']

It’s important to note that when updating the list, you should be careful not to exceed the length of the list. When an index is not exist in the list, you will get an “IndexError” Exception.

Various List in-built functions

In Python, there are several built-in functions that can be used to perform operations on lists:

len(): The “len()” function returns the number of elements in a list.

fruits = ['apple', 'banana', 'orange']
print(len(fruits)) # Output: 3

max() and min(): The “max()” and “min()” functions return the maximum and minimum elements in a list, respectively. This only works for lists that contains elements that are comparable, otherwise it will raise an exception.

numbers = [1, 3, 2, 5, 4]
print(max(numbers)) # Output: 5
print(min(numbers)) # Output: 1

sorted(): The “sorted()” function returns a sorted copy of a list. It can take an optional argument “reverse=True” to sort the list in descending order.

fruits = ['orange', 'apple', 'banana']
print(sorted(fruits)) # Output: ['apple', 'banana', 'orange']
print(sorted(fruits, reverse=True)) # Output: ['orange', 'banana', 'apple']

sum(): The “sum()” function returns the sum of all elements in a list of numbers.

numbers = [1, 3, 2, 5, 4]
print(sum(numbers)) # Output: 15

count(): The “count()” method returns the number of occurrences of a specified element in a list.

fruits = ['apple', 'banana', 'orange', 'banana']
print(fruits.count('banana')) # Output: 2

index(): The “index()” method returns the index of the first occurrence of a specified element in a list.

fruits = ['apple', 'banana', 'orange']
print(fruits.index('banana')) # Output: 1

remove(): The “remove()” method removes the first occurrence of a specified element from a list.

fruits = ['apple', 'banana', 'orange']
fruits.remove('banana')
print(fruits) # Output: ['apple', 'orange']

pop(): The “pop()” method removes the element at the specified index, and returns the removed element. If no index is specified, it removes and returns the last element.

fruits = ['apple', 'banana', 'orange']
print(fruits.pop(1)) # Output: 'banana'
print(fruits) # Output: ['apple', 'orange']

reverse(): The “reverse()” method reverses the order of elements in a list.

fruits = ['apple', 'banana', 'orange']
fruits.reverse()
print(fruits) # Output: ['orange', 'banana', 'apple']

clear(): The “clear()” method removes all elements from a list.

fruits = ['apple', 'banana', 'orange']
fruits.clear()
print(

Summary

1. Lists are a built-in data type in Python and are used to store an ordered collection of items.
2. Lists are mutable, meaning they can be modified after they are created.
3. Lists can hold items of any data type, including other lists.
4. Lists can be indexed and sliced to access specific elements.
5. Lists have various built-in methods such as append(), insert(), remove(), pop(), and sort().
6. Lists can be concatenated and repeated using the + and * operators.
7. Lists can be nested to create multidimensional lists.
8. Lists can be used with loops and other control structures to perform various operations.
9. Lists can be used with list comprehension to create a new list based on an existing one.
10. Lists can be converted to other data types such as sets and tuples.

Top Common Questions related to lists

Q: How do I create a list in Python?

A: To create a list in Python, you can use square brackets [] and separate the items with commas. For example: my_list = [1, 2, 3, 4, 5]

Q: How do I access elements of a list in Python?

A: You can access elements of a list in Python by using indexing. The index of the first element is 0 and the index of the last element is -1. You can also use negative indexing to access elements from the end of the list. For example, my_list[0] will access the first element, my_list[-1] will access the last element.

Q: How do I add elements to a list in Python?

A: You can use the append() method to add elements to the end of a list, or the insert() method to add elements at a specific index. For example, my_list.append(6) will add the number 6 to the end of the list, my_list.insert(2, 7) will add the number 7 at the index 2.

Q: How do I remove elements from a list in Python?

A: You can use the remove() method to remove elements by value, or the pop() method to remove elements by index. For example, my_list.remove(6) will remove the first occurrence of the number 6 from the list, my_list.pop(2) will remove the element at index 2.

Q: How do I sort a list in Python?

A: You can use the sort() method to sort a list in ascending order. You can also pass the reverse=True argument to sort the list in descending order. For example, my_list.sort() will sort the list in ascending order, my_list.sort(reverse=True) will sort the list in descending order.

Q: How do I reverse a list in python?

A: You can use the reverse() method or slicing technique to reverse a list. For example, my_list.reverse() will reverse the list in place, my_list[::-1] will give you a new list which is reversed.

Q: How do I find the length of a list in Python?

A: You can use the len() function to find the length of a list. For example, len(my_list) will return the number of elements in the list.

Q: Can I use a for loop to iterate through a list in Python?

A: Yes, you can use a for loop to iterate through the elements of a list. For example:

for item in my_list:
    print(item)

Q: Can I nest lists in python?

A: Yes, you can nest lists in python. It means you can have a list which contains other lists as its elements.

In conclusion, Python lists are a powerful and versatile data structure that can be used in a variety of ways. They offer many built-in methods for adding, removing, and manipulating elements, as well as slicing and indexing capabilities. Whether you are working with simple lists of numbers or more complex data structures, Python lists can help you organize and process your data effectively. With a solid understanding of how to use them, you can take your Python programming skills to the next level.

It’s not a doubt that data is really very powerful, when you can actually understand what it’s telling you. It’s not easy to get clear insights by looking at numbers, stats and raw data.

You need data to be presented in a logical, easy to understand manner so that you can get proper information. That’s where the “Data Visualization” comes into picture.

Data Visualization is used plenty number of times in analytics industry and indeed good data visualizations gives a proper impact on your insights as well.

In this article we are going to learn about how data visualization is effective and see much more related stuffs about it.

What is Data Visualization?

Data Visualization allows you to understand your data in a better way which is easy to understand. It is all about representing your data in a visual manner through various types of charts, maps. diagrams, etc. It can help to give proper significance to your data.

How Data Visualization work?

Data is usually in raw format. Just by watching numbers you won’t understand anything. But a proper visual format will help people to extract meaning from that data and can be able to get quick information.

Data Visualizations allows you to expose patterns, trends, and correlations that may otherwise go undetected, too.

Best Practices for Data Visualizations

While determining how you will visualize your data, some of the best practices you have to keep in your mind are –

• Choose the visual which fits best for your data and it’s purpose.
• Ensure your visual is easily understandable and viewable.
• Make proper context arrangement with your visual so that everyone can understand.
• Keep it as simple and straightforward.
• Give proper insights through your visuals.

Questions to ask before deciding any visual for your data

Many times we don’t know which visual is to selected for what purpose. So this are some of the questions one can ask before selecting the proper visualization for their data.

Do you want to compare values?

When you want to compare the values of various columns of your data set you can use comparison chart visualizations. The can easily show the high and low trend in the data values.

Some of the visualization chart you can choose are –

• Column
• Line
• Mekko
• Bar
• Scatter
• Pie

Do want to represent the composition of something in the data?

Many visuals can show up how a individual category make up the whole of something. For example, total sales done by sales representative.

Some of the charts favoring this category are –

• Stacked Bar
• Stacked Column
• Area
• Waterfall
• Mekko
• Pie

Do you want to understand how your data is distributed?

Distribution charts can represent the distribution among the data and the range of information in your values.

Some of this charts are –

• Scatter Plot
• Line
• Column
• Bar

When you want to know the trends in your data set?

If you want to represent the time series data or want to know the information about a specific time period you can use the following charts.

• Line
• Dual – Axis Line
• Column

When you want to better understand the relationship between the value sets?

Relationship charts are suited to show how one variable related to other multiple variables. You can show positive relationship or negative relationship on another variable.

You can use the following charts to find the relationship between variables –

• Scatter plot
• Bubble chart
• Line chart

In the next article we will understand the dive deep into the theoretical part of each of the visualization and then into code.

Happy Learning !