Datasciencemind

"Introduction to Data Science with Python." This is a class from Harvard University: It's free. You should be familiar with Python to take this course. The course is for beginners. It's for those who want to build a fundamental understanding of machine learning and artificial intelligence. It covers some of these topics: • Generalization and overfitting • Model building, regularization, and evaluation • Linear and logistic regression models • k-Nearest Neighbor • Scikit-Learn, NumPy, Pandas, and Matplotlib Link: https://pll.harvard.edu/course/introduction-data-science-python

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Are you gearing up for a Power BI interview? ✨️

Can you explain a few visualizations you have used in your projects and why you chose those charts?" Be sure to name the charts and explain why they fit specific scenarios.

Bar Chart:

Example: Comparing the number of apples, bananas, and oranges sold at a fruit stand. Reason: Bar charts are good for comparing the amounts of different items side by side.

Column Chart:

Example: Showing how many hours a student studied each day for a week. Reason: Column charts help you see how values change over specific periods, like days or months.

Line Chart:

Example: Tracking the temperature at noon each day for a month. Reason: Line charts are perfect for showing trends over time, like how the temperature goes up and down.

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IMPORTANT TIP FOR A DATA ANALYST⚠️✅️

If your data can be visualized as a bar chart, column chart, or line graph... ...use a bar chart, column chart, or line graph.

Fancy doesn't always mean better and, in fact, it often means worse.

Your job as a data analyst is to make the data and insights accessible to stakeholders, which means using visuals that a non-technical person can understand.

Are there charts and graphs that are more fun to look at?

Sure. Sankey diagrams, word clouds, waterfall charts, and radar charts have their place. But I would say that 80+% of data visualizations can be done using a very small handful of clear, simple charts. Your stakeholders will thank you. And your future self will thank you when you don't have to help interpret the data viz every time someone looks at it. Win-win✨️✅️

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Get your skills analyzed & the best SQL build for your goal by thedataschoool Academy 📃Personalised SQL Course in just 5 minutes. 📰Verified SQL Certificate recognised by Google and amazon . 🏅Industry projects. All of these in just Rs. 399 with Life Time access Limited time period offer. Click Below 👇 https://tinyurl.com/SQLXCourseTD

Complete SQL road map 👇👇

1.Intro to SQL • Definition • Purpose • Relational DBs • DBMS 2.Basic SQL Syntax • SELECT • FROM • WHERE • ORDER BY • GROUP BY 3. Data Types • Integer • Floating-Point • Character • Date • VARCHAR • TEXT • BLOB • BOOLEAN 4.Sub languages • DML • DDL • DQL • DCL • TCL 5. Data Manipulation • INSERT • UPDATE • DELETE 6. Data Definition • CREATE • ALTER • DROP • Indexes 7.Query Filtering and Sorting • WHERE • AND • OR Conditions • Ascending • Descending 8. Data Aggregation • SUM • AVG • COUNT • MIN • MAX 9.Joins and Relationships • INNER JOIN • LEFT JOIN • RIGHT JOIN • Self-Joins • Cross Joins • FULL OUTER JOIN 10.Subqueries • Subqueries used in • Filtering data • Aggregating data • Joining tables • Correlated Subqueries 11.Views • Creating • Modifying • Dropping Views 12.Transactions • ACID Properties • COMMIT • ROLLBACK • SAVEPOINT • ROLLBACK TO SAVEPOINT 13.Stored Procedures • CREATE PROCEDURE • ALTER PROCEDURE • DROP PROCEDURE • EXECUTE PROCEDURE • User-Defined Functions (UDFs) 14.Triggers • Trigger Events • Trigger Execution and Syntax 15. Security and Permissions • CREATE USER • GRANT • REVOKE • ALTER USER • DROP USER 16.Optimizations • Indexing Strategies • Query Optimization 17.Normalization • 1NF(Normal Form) • 2NF • 3NF • BCNF 18.Backup and Recovery • Database Backups • Point-in-Time Recovery 19.NoSQL Databases • MongoDB • Cassandra etc... • Key differences 20. Data Integrity • Primary Key • Foreign Key 21.Advanced SQL Queries • Window Functions • Common Table Expressions (CTEs) 22.Full-Text Search • Full-Text Indexes • Search Optimization 23. Data Import and Export • Importing Data • Exporting Data (CSV, JSON) • Using SQL Dump Files 24.Database Design • Entity-Relationship Diagrams • Normalization Techniques 25.Advanced Indexing • Composite Indexes • Covering Indexes 26.Database Transactions • Savepoints • Nested Transactions • Two-Phase Commit Protocol 27.Performance Tuning • Query Profiling and Analysis • Query Cache Optimization

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Someone asked me today if they need to learn Python & Data Structures to become a data analyst. What's the right time to start applying for data analyst interview?

I think this is the common question which many of the other freshers might think of. So, I think it's better to answer it here for everyone's benefit.

The right time to start applying for data analyst positions depends on a few factors:

1. Skills and Experience: Ensure you have the necessary skills (e.g., SQL, Excel, Python/R, data visualization tools like Power BI or Tableau) and some relevant experience, whether through projects, internships, or previous jobs. 2. Preparation: Make sure your resume and LinkedIn profile are updated, and you have a portfolio showcasing your projects and skills. It's also important to prepare for common interview questions and case studies. 3. Job Market: Pay attention to the job market trends. Certain times of the year, like the beginning and middle of the fiscal year, might have more openings due to budget cycles. 4. Personal Readiness: Consider your current situation, including any existing commitments or obligations. You should be able to dedicate time to the job search process. Generally, a good time to start applying is around 3-6 months before you aim to start a new job. This gives you ample time to go through the application process, which can include multiple interview rounds and potentially some waiting periods. Also, if you know SQL & have a decent data portfolio, then you don't need to worry much on Python & Data Structures. It's good if you know these but they are not mandatory. You can still confidently apply for data analyst positions without being an expert in Python or data structures. Focus on highlighting your current skills along with hands-on projects in your resume. Like this post if it was helpful for you♥️👍 Follow @datascience_mind for more

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Requirements for data analyst role based on some jobs✅️✨️👇

👉 Must be proficient in writing complex SQL Queries. 👉 Understand business requirements in BI context and design data models to transform raw data into meaningful insights. 👉 Connecting data sources, importing data, and transforming data for Business intelligence. 👉 Strong working knowledge in Excel and visualization tools like PowerBI, Tableau or QlikView 👉 Developing visual reports, KPI scorecards, and dashboards using Power BI desktop. Nowadays, recruiters primary focus on SQL & BI skills for data analyst roles. So try practicing SQL & create some BI projects using Tableau or Power BI. You can refer our Power BI & SQL Series to understand the essential concepts. Follow @datascience_mind for more Like this post if you find this content helpful👍❤️ Enjoy Learning ✅️