How to start your career in data analysis for freshers 😄👇

1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.

Free Resources: https://t.iss.one/pythonanalyst/103

2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.

Free Data Analysis Books: https://t.iss.one/learndataanalysis

3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.

4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.

5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.

6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst

7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst

8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476

9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio

10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.

11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL

12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Python top interviews q a 🚀
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The Department of Justice wants Google to break up Chrome, following a ruling that it operates a search and ad monopoly. If this goes through, it could reshape Google’s entire business

Google is all-in on AI. Can it stay ahead in this race, or will regulatory battles and rising challengers slow it down?

Data Science Projects With Python by Stephen Klosterman

#ebook #datascience #python #ml

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