Interview Books & Preparation Materials
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https://news.1rj.ru/str/InterviewBooks

https://news.1rj.ru/str/Interview_Jobs

Steps to become a successful data scientist

Top 5 DAX (Data Analysis Expressions) functions to master Power BI
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https://www.linkedin.com/posts/sql-analysts_top-5-dax-data-analysis-expressions-functions-activity-7121484515243360256-3x-K

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Free Courses, Projects & Internship for data analytics
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https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7123979295600836608-Ut3b

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319 views but less than 20 likes. Guys, like the linkedin post if its helping you so that linkedin algorithm can send it to more data enthusiasts who can't afford paid courses :)

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Don't waste time in remembering company names & searching their Career pages,

Apply here directly : https://www.linkedin.com/posts/sql-analysts_career-software-design-activity-7126059959988928514-SZjo

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Free courses to learn Data Science in 2023
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https://www.linkedin.com/posts/sql-analysts_programming-computerscience-datascience-activity-7126408061472112641-agJe

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Credit Card Data Analysis Project
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https://news.1rj.ru/str/sqlproject/59

Complete Power BI Book
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https://www.linkedin.com/posts/sql-analysts_power-bi-book-sqlspecialist-activity-7128010834630369280-AQAR

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Unlimited access to data science courses till Nov 20
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https://www.linkedin.com/posts/sql-analysts_datascience-dataanalytics-activity-7128217526924177408-0DtE

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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://news.1rj.ru/str/learndataanalysis/559

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://news.1rj.ru/str/learndataanalysis/596

Python Roadmap for Data Analysts -> https://news.1rj.ru/str/pythonfreebootcamp/207

Free resume guide from Harvard
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https://www.linkedin.com/posts/sql-analysts_harvard-resume-and-cv-career-guide-activity-7129694373688070144-RS2m

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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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SQL Cheatsheet
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https://www.linkedin.com/posts/sql-analysts_need-to-learn-sql-but-dont-want-to-spend-activity-7130075632184508417-PKrc?utm_source=share&utm_medium=member_android

Importance of Data Cleaning

https://www.linkedin.com/posts/sql-analysts_datacleaning-dataanalytics-numpy-activity-7131211154634264577-C21z?utm_source=share&utm_medium=member_android

Beginner-friendly Excel Book
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https://www.linkedin.com/posts/sql-analysts_excel-for-beginners-activity-7131506316677693440-Ip8j

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 👍👍