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

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

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

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.

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 👍👍

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]

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

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

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.

✅ 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!