@Codingdidi

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Channel Posts

✅ Step-by-Step Approach to Learn Data Analytics 📈🧠 ➊ Excel Fundamentals: ✔ Master formulas, pivot tables, data validation, charts, and graphs. ➋ SQL Basics: ✔ Learn to query databases, use SELECT, FROM, WHERE, JOIN, GROUP BY, and aggregate functions. ➌ Data Visualization: ✔ Get proficient with tools like Tableau or Power BI to create insightful dashboards. ➍ Statistical Concepts: ✔ Understand descriptive statistics (mean, median, mode), distributions, and hypothesis testing. ➎ Data Cleaning & Preprocessing: ✔ Learn how to handle missing data, outliers, and data inconsistencies. ➏ Exploratory Data Analysis (EDA): ✔ Explore datasets, identify patterns, and formulate hypotheses. ➐ Python for Data Analysis (Optional but Recommended): ✔ Learn Pandas and NumPy for data manipulation and analysis. ➑ Real-World Projects: ✔ Analyze datasets from Kaggle, UCI Machine Learning Repository, or your own collection. ➒ Business Acumen: ✔ Understand key business metrics and how data insights impact business decisions. ➓ Build a Portfolio: ✔ Showcase your projects on GitHub, Tableau Public, or a personal website. Highlight the impact of your analysis. 👍 Tap ❤️ for more!

2	Data Engineering Roadmap for Beginners (2025) > Language → Python + SQL. > OS Basics → Linux + Bash + Git. > Data Modeling → Normalization + Star/Snowflake Schema. > Databases → PostgreSQL + MySQL + MongoDB. > Data Warehousing → Snowflake + BigQuery + Redshift. > Data Processing → Apache Spark + PySpark. > Workflow Orchestration → Airflow + Prefect. > Data Lakes → Delta Lake + Apache Hudi + Iceberg. > Streaming → Kafka + Flink > Cloud Platforms → AWS (S3, Glue, EMR) / GCP (GCS, Dataflow, BigQuery) / Azure (Data Factory, Synapse). > Data Quality/Validation → Great Expectations. > Containerization → Docker + Kubernetes. > Infra as Code → Terraform. > Visualization → dbt + Looker/PowerBI/Tableau.	1 121
3	S&P Global is hiring Data Analyst 🚀🔥 Experience : 0-6 Months Location : Bangalore Apply link : https://careers.spglobal.com/jobs/319690?lang=en-us&utm_source=linkedin	1 005
4	company name: JP Morgan Chase role: sde-1 batch: 2024/23 passouts link:https://jpmc.fa.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/job/210667642	1 075
5	Template to ask for referrals (For freshers) 👇👇 Hi [Name], I hope this message finds you well. My name is [Your Name], and I recently graduated with a degree in [Your Degree] from [Your University]. I am passionate about data analytics and have developed a strong foundation through my coursework and practical projects. I am currently seeking opportunities to start my career as a Data Analyst and came across the exciting roles at [Company Name]. I am reaching out to you because I admire your professional journey and expertise in the field of data analytics. Your role at [Company Name] is particularly inspiring, and I am very interested in contributing to such an innovative and dynamic team. I am confident that my skills and enthusiasm would make me a valuable addition to this role [Job ID / Link]. If possible, I would be incredibly grateful for your referral or any advice you could offer on how to best position myself for this opportunity. Thank you very much for considering my request. I understand how busy you must be and truly appreciate any assistance you can provide. Best regards, [Your Full Name] [Your Email Address]	1 338
6	IBM Summer Internship Program! Position: Research Intern - AI Qualifications: Bachelor’s Degree Salary: 30K - 50K Per Month (Expected) Batch: 2024/ 2025/ 2026/ 2027 Experience: Freshers Location: Bangalore; Gurgaon, India (Hybrid) 📌Apply Now: https://ibmglobal.avature.net/en_US/careers/JobDetail?jobId=59041&source=WEB_Search_INDIA All the best 👍👍	985
7	Q. Explain the data preprocessing steps in data analysis. Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks. 1. Data profiling. 2. Data cleansing. 3. Data reduction. 4. Data transformation. 5. Data enrichment. 6. Data validation. Q. What Are the Three Stages of Building a Model in Machine Learning? Ans. The three stages of building a machine learning model are: Model Building: Choosing a suitable algorithm for the model and train it according to the requirement Model Testing: Checking the accuracy of the model through the test data Applying the Model: Making the required changes after testing and use the final model for real-time projects Q. What are the subsets of SQL? Ans. The following are the four significant subsets of the SQL: Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc. Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc. Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE. Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc. Q. What is a Parameter in Tableau? Give an Example. Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines. For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.	1 064
8	Here are some essential SQL tips for beginners 👇👇 ◆ Primary Key = Unique Key + Not Null constraint ◆ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE ‘A%A’ ◆ LIKE operator is for string data type ◆ COUNT(*), COUNT(1), COUNT(0) all are same ◆ All aggregate functions ignore the NULL values ◆ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type ◆ For row level filtration use WHERE and aggregate level filtration use HAVING ◆ UNION ALL will include duplicates where as UNION excludes duplicates ◆ If the results will not have any duplicates, use UNION ALL instead of UNION ◆ We have to alias the subquery if we are using the columns in the outer select query ◆ Subqueries can be used as output with NOT IN condition. ◆ CTEs look better than subqueries. Performance wise both are same. ◆ When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN. ◆ Window functions work at ROW level. ◆ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same. ◆ EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned. Like for more 😄😄 @codingdidi	1 265
9	Remote work websites.pdf	1 633
10	American Express is hiring Analyst 🚀 Min. Experience : 1 Year Location : Gurugram Apply link : https://aexp.eightfold.ai/careers/job/30504056?hl=en&utm_source=linkedin&domain=aexp.com	1 635
11	Gartner is hiring Associate Data Scientist 🚀 Experience : 0-3 Years Location : Gurugram Apply link : https://gartner.wd5.myworkdayjobs.com/EXT/job/Gurgaon/Associate-Data-Scientist_101739-1/apply?source=JB-10120	1 625
12	No text...	1 587
13	Some practical interview questions for an entry-level data analyst role in Power BI: • Data Import Scenario: Describe how you would import data from various sources (Excel,SQL Server, CSV) into Power BI. • Data Cleaning Exercise: In Power BI, how would you handle a dataset with missing values and inconsistent formats to prepare it for analysis? • Handling Large Datasets: If you're working with a very large dataset in Power BI that is causing performance issues, what strategies would you use to optimize the data processing? • Calculated Columns and Measures: Explain how you would use calculated columns and measures in Power BI to analyze year-over-year growth. • Data Modeling Case: You have sales data in one table and customer data in another. How would you create a data model in Power BI to analyze customer purchase behavior? • Visualizations Task: Describe your approach to visualizing sales data in Power BI to highlight trends over time across different product categories. • Dashboard Optimization: A Power BI dashboard is loading slowly. What steps would you take to diagnose and improve its performance? • Data Refresh Scheduling: How would you set up and manage automatic data refreshes for a weekly sales report in Power BI? • Row-Level Security: How would you implement user-level security in Power BI for a report that needs different access levels for various users? • Troubleshooting a DAX Calculation: If a DAX formula in Power BI is not returning the expected results, how would you go about troubleshooting it? • Integration with Other Tools: Describe a scenario where you integrated Power BI with another tool or service (like Excel, Azure, or a web API). • Interactive Reports Creation: How would you design a Power BI report that allows user interaction, such as using slicers or drill-down features? • Adapting to Data Source Changes: If there are structural changes in a primary data source (like addition or removal of columns), how would you update your Power BI reports and dashboards? • Sharing Reports: Explain how you would share a report with your team and set up access controls using Power BI Service. • SQL Queries in Power BI: How do you use SQL queries in Power BI for advanced data transformation or analysis? • Error Handling in Data Sources: How do you manage and resolve errors in data sources or calculations in Power BI? • Custom Visuals Usage: Have you used custom visuals in Power BI? Describe the scenario and the benefit • Collaboration in Power BI Projects: Discuss how you have worked with others on a Power BI project. What collaboration tools or features within Power BI did you utilize? • Performance Tuning: What steps do you take to ensure your Power BI reports are performing optimally when dealing with large datasets or complex calculations?	1 824
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15	How to build a Data Science portfolio that truly stands out?	1 873
16	No text...	1 958
17	SQL Advanced Concepts for Data Analyst Interviews 1. Window Functions: Gain proficiency in window functions like ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE(), and LAG()/LEAD(). These functions allow you to perform calculations across a set of table rows related to the current row without collapsing the result set into a single output. 2. Common Table Expressions (CTEs): Understand how to use CTEs with the WITH clause to create temporary result sets that can be referenced within a SELECT, INSERT, UPDATE, or DELETE statement. CTEs improve the readability and maintainability of complex queries. 3. Recursive CTEs: Learn how to use recursive CTEs to solve hierarchical or recursive data problems, such as navigating organizational charts or bill-of-materials structures. 4. Advanced Joins: Master complex join techniques, including self-joins (joining a table with itself), cross joins (Cartesian product), and using multiple joins in a single query. 5. Subqueries and Correlated Subqueries: Be adept at writing subqueries that return a single value or a set of values. Correlated subqueries, which reference columns from the outer query, are particularly powerful for row-by-row operations. 6. Indexing Strategies: Learn advanced indexing strategies, such as covering indexes, composite indexes, and partial indexes. Understand how to optimize query performance by designing the right indexes and when to use CLUSTERED versus NON-CLUSTERED indexes. 7. Query Optimization and Execution Plans: Develop skills in reading and interpreting SQL execution plans to understand how queries are executed. Use tools like EXPLAIN or EXPLAIN ANALYZE to identify performance bottlenecks and optimize query performance. 8. Stored Procedures: Understand how to create and use stored procedures to encapsulate complex SQL logic into reusable, modular code. Learn how to pass parameters, handle errors, and return multiple result sets from a stored procedure. 9. Triggers: Learn how to create triggers to automatically execute a specified action in response to certain events on a table (e.g., AFTER INSERT, BEFORE UPDATE). Triggers are useful for maintaining data integrity and automating workflows. 10. Transactions and Isolation Levels: Master the use of transactions to ensure that a series of SQL operations are executed as a single unit of work. Understand different isolation levels (READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, SERIALIZABLE) and their impact on data consistency and concurrency. 11. PIVOT and UNPIVOT: Use the PIVOT operator to transform row data into columnar data and UNPIVOT to convert columns back into rows. These operations are crucial for reshaping data for reporting and analysis. 12. Dynamic SQL: Learn how to write dynamic SQL queries that are constructed and executed at runtime. This is useful when the exact SQL query cannot be determined until runtime, such as in scenarios involving user-defined filters or conditional logic. 13. Data Partitioning: Understand how to implement data partitioning strategies, such as range partitioning or list partitioning, to manage large tables efficiently. Partitioning can significantly improve query performance and manageability. 14. Temporary Tables: Learn how to create and use temporary tables to store intermediate results within a session. Understand the differences between local and global temporary tables, and when to use them. 15. Materialized Views: Use materialized views to store the result of a query physically and update it periodically. This can drastically improve performance for complex queries that need to be executed frequently. 16. Handling Complex Data Types: Understand how to work with complex data types such as JSON, XML, and arrays. Learn how to store, query, and manipulate these types in SQL databases, including using functions like JSON_EXTRACT(), XMLQUERY(), or array functions.	1
18	https://youtu.be/Bd6EeCdDRiw -- Github in one complete video	1 563
19	What’s the biggest challenge you face in learning AI/ML?	1 423
20	🚨 Join Our Discord Community! 🎓📊 Hey #CodingFam! I'm super excited to invite you to new Discord server – made just for YOU 🔥 If you're a student, job seeker, or self-learner trying to build a career in Data Science, Python, SQL, Power BI, or ML — this is the ultimate space you’ve been waiting for 💻💡 🧠 What’s Inside? ✅ Topic-wise roadmaps (Python, ML, SQL, etc.) ✅ Daily goals & learning challenges ✅ Project ideas & resume boosters ✅ Notes, resources, and YouTube playlists ✅ Real-time help & doubt-solving ✅ Career guidance + Interview prep 💬 Why Discord? Because learning shouldn't feel lonely! With Discord, we can: 👉 Interact instantly in focused channels 👉 Stay updated through announcements 👉 Ask & answer doubts in real time 👉 Build a support system with learners like you 👉 Get exclusive tips, content drops & live session alerts 🎯 Whether you're just starting out or already in the game, this community will help you stay consistent, stay motivated, and level up your skills — together 💪 🔗 Click to join: https://discord.gg/khBWeH5T (It's FREE & beginner-friendly!) Let’s build, learn, and grow together 👩‍💻👨‍💻 See you on the server! 💙	1 338

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