Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
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Don't waste time in remembering company names & searching their Career pages,
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Free courses to learn Data Science in 2023
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Complete Power BI Book
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Unlimited access to data science courses till Nov 20
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Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
Unlimited access to data science courses till Nov 20 ๐๐ https://www.linkedin.com/posts/sql-analysts_datascience-dataanalytics-activity-7128217526924177408-0DtE Like for more ๐โค๏ธ
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Data Analysis vs Data Science
Data analysis often focuses on interpreting and summarizing existing data, requiring skills like statistical analysis, SQL, and data visualization.
On the other hand, data science involves a broader set of skills, including machine learning, predictive modeling, and advanced programming.
In essence, data analysis is a subset of data science, with data scientists often having a more extensive toolkit for handling complex and unstructured data.
Free Resources to become data analyst -> https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7113004712412524545-Uw4k
Steps to become data scientist -> https://t.iss.one/learndataanalysis/559
Data analysis often focuses on interpreting and summarizing existing data, requiring skills like statistical analysis, SQL, and data visualization.
On the other hand, data science involves a broader set of skills, including machine learning, predictive modeling, and advanced programming.
In essence, data analysis is a subset of data science, with data scientists often having a more extensive toolkit for handling complex and unstructured data.
Free Resources to become data analyst -> https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7113004712412524545-Uw4k
Steps to become data scientist -> https://t.iss.one/learndataanalysis/559
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SQL vs Python
SQL is great for managing and querying structured databases, especially when dealing with large datasets. It excels in tasks like filtering, sorting, and aggregating data.
Python, on the other hand, is a versatile programming language used for a broader range of tasks. In the context of data, Python is powerful for data manipulation, analysis, and machine learning. It offers libraries like Pandas for data manipulation, NumPy for numerical operations, and Scikit-Learn for machine learning.
In summary, SQL is essential for efficient database querying, while Python provides a more comprehensive solution for various data-related tasks, making them often used together in data-related workflows.
SQL Practice Questions with Answers -> https://t.iss.one/learndataanalysis/596
Python Roadmap for Data Analysts -> https://t.iss.one/pythonfreebootcamp/207
SQL is great for managing and querying structured databases, especially when dealing with large datasets. It excels in tasks like filtering, sorting, and aggregating data.
Python, on the other hand, is a versatile programming language used for a broader range of tasks. In the context of data, Python is powerful for data manipulation, analysis, and machine learning. It offers libraries like Pandas for data manipulation, NumPy for numerical operations, and Scikit-Learn for machine learning.
In summary, SQL is essential for efficient database querying, while Python provides a more comprehensive solution for various data-related tasks, making them often used together in data-related workflows.
SQL Practice Questions with Answers -> https://t.iss.one/learndataanalysis/596
Python Roadmap for Data Analysts -> https://t.iss.one/pythonfreebootcamp/207
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Free resume guide from Harvard
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Save it for your future reference
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Ultimate Resume & Interview Guide
https://www.linkedin.com/posts/sql-analysts_resume-tips-activity-7130056771062153217-ZSsJ?utm_source=share&utm_medium=member_android
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Beginner-friendly Excel Book
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Avoid directly copying YouTube projects onto your resume because if everyone looks the same, recruiters might discard resumes.
Instead, for eg, let's say you are working on a SQL case study, download a dataset from Kaggle (usually a CSV file), set up a Postgre/MySQL database, connect it with the data, and prompt ChatGPT with questions ranging from basic to advanced SQL.
Solve the questions step by step. When using PowerBI, connect to the database and create a compelling dashboard. Don't just upload the dataset; employ DAX queries, statistical functions, and avoid relying solely on drag-and-drop features. Use Formatting section to do creative stuff and add your unique element in the project.
ENJOY LEARNING ๐๐
Instead, for eg, let's say you are working on a SQL case study, download a dataset from Kaggle (usually a CSV file), set up a Postgre/MySQL database, connect it with the data, and prompt ChatGPT with questions ranging from basic to advanced SQL.
Solve the questions step by step. When using PowerBI, connect to the database and create a compelling dashboard. Don't just upload the dataset; employ DAX queries, statistical functions, and avoid relying solely on drag-and-drop features. Use Formatting section to do creative stuff and add your unique element in the project.
ENJOY LEARNING ๐๐
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New Giveaway 500 TB Tutorials + Books + Courses + Trainings + Workshops + Educational Resources
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Best practices for writing SQL queries:
Join for more: https://t.iss.one/learndataanalysis
1- Write SQL keywords in capital letters.
2- Use table aliases with columns when you are joining multiple tables.
3- Never use select *, always mention list of columns in select clause.
4- Add useful comments wherever you write complex logic. Avoid too many comments.
5- Use joins instead of subqueries when possible for better performance.
6- Create CTEs instead of multiple sub queries , it will make your query easy to read.
7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
8- Never use order by in sub queries , It will unnecessary increase runtime.
9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.
SQL Basics: https://t.iss.one/sqlanalyst/105
Join for more: https://t.iss.one/learndataanalysis
1- Write SQL keywords in capital letters.
2- Use table aliases with columns when you are joining multiple tables.
3- Never use select *, always mention list of columns in select clause.
4- Add useful comments wherever you write complex logic. Avoid too many comments.
5- Use joins instead of subqueries when possible for better performance.
6- Create CTEs instead of multiple sub queries , it will make your query easy to read.
7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
8- Never use order by in sub queries , It will unnecessary increase runtime.
9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.
SQL Basics: https://t.iss.one/sqlanalyst/105
๐15โค5
Advanced data analytics book
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https://www.linkedin.com/posts/sql-analysts_advances-data-analytics-interview-questions-activity-7135479770078658560-U4ip?utm_source=share&utm_medium=member_android
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