𝐅𝐫𝐨𝐦 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: 𝐊𝐞𝐲 𝐒𝐤𝐢𝐥𝐥𝐬 𝐀𝐜𝐫𝐨𝐬𝐬 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐌𝐋 𝐑𝐨𝐥𝐞𝐬.

📝 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 (Avg salary for a fresher: 6-8 LPA)

1. Excel
2. SQL (80% of the interview will be on expertise in SQL)
3. Python (Basic to intermediate knowledge required)
4. Data visualization tool (Most common: Tableau/PowerBI)
5. Statistics (Basic to intermediate)

📝 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 (Avg salary for a fresher: 10-15 LPA)

1. Excel, SQL, Python, Tableau/PowerBI, Statistics (All data analyst skills)
2. Mathematics (Linear algebra, Calculus)
3. Machine learning (Scikit-learn: Supervised, Unsupervised, Recommender systems, Timeseries modelling)
4. Deep learning (TensorFlow, PyTorch)
5. NLP (NLTK, spacy, gensim)

📝 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (Avg salary for a fresher: 9-12 LPA)

1. Big data tools (Hadoop, Spark, Hive)
2. Python, Java or Scala
3. Data pipeline automation
4. SQL & NoSQL databases
5. ETL tools & Data warehousing (Apache Nifi, Talend, Airflow)
6. Cloud computing (AWS, Azure, GCP)

📝 𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (Avg salary for a fresher: 10-12 LPA)

1. Cloud platforms (AWS, Azure, GCP)
2. Machine learning
3. DevOps & CI/CD
4. Version control
5. Code optimization & Tuning

📝 𝐌𝐋𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (Avg salary for a fresher: 8-10 LPA)

1. CI/CD for ML Pipelines
2. Docker, Kubernetes & Container orchestration
3. Monitoring & Logging (Prometheus, Grafana, ELK stack: Elasticsearch, Logstash, Kibana)
4. Model versioning & Governance (MLflow, DVC)
5. Infrastructure as code (IaC): Teraform, CloudFormation, Ansible
6. API development & Integration
7. Automated testing for data validation, model performance & pipeline integrity