/learn-data-analytics

Simple project to learn Data analytics. Using RAW SQL Query with PostgreSQL. There are few steps upto advance level that one must follow.

Primary LanguagePython

How to become Data analytics

  • Becoming a data analyst involves acquiring a mix of:
  1. Technical skills
  2. Domain knowledge
  3. Practical experience

Steps

1. Educational Background

  • Degree: While not always mandatory, a degree in a related field such as Computer Science, Mathematics, Statistics, Economics, or Engineering can be very beneficial.
  • Courses and Certifications: Online courses and certifications can supplement your education. Websites like Coursera, edX, Udacity, and DataCamp offer courses specifically tailored to data analysis.

2. Learn the Necessary Skills

  • Statistical Analysis: Understand basic and advanced statistical methods. Key topics include probability, distributions, hypothesis testing, and regression analysis.
  • Programming:
    • Python: Learn libraries like Pandas, NumPy, Matplotlib, and Seaborn.
    • R: A language specifically designed for statistical analysis and visualization.
  • Data Visualization: Master tools like Tableau, Power BI, or libraries in Python (e.g., Matplotlib, Seaborn).
  • Database Management: Learn SQL to extract and manipulate data from databases.
  • Excel: Proficiency in Excel is crucial, especially for smaller datasets and quick analyses.
  • Machine Learning: Basic knowledge of machine learning can be beneficial. Libraries such as Scikit-learn in Python are widely used.

3. Practical Experience

  • Projects: Work on personal or open-source projects to apply your skills. Kaggle is a great platform for finding datasets and participating in competitions.
  • Internships: Gain experience through internships, even if they are unpaid or part-time.
  • Freelancing: Offer your services on platforms like Upwork or Fiverr to build a portfolio.

4. Build a Portfolio

  • Showcase your projects on platforms like GitHub.
  • Create a personal website or a LinkedIn profile to highlight your work, skills, and experience.
  • Document your projects clearly, explaining the problem, methodology, and results.

5. Networking

  • Join professional groups and attend meetups or conferences related to data analysis.
  • Engage with the community on forums such as Reddit’s r/datascience, Stack Overflow, or LinkedIn groups.
  • Connect with professionals in the field for mentorship and advice.

6. Stay Updated

  • Follow industry blogs, podcasts, and thought leaders.
  • Regularly read research papers and articles to stay informed about the latest trends and technologies.

7. Apply for Jobs

  • Tailor your resume to highlight relevant skills and experiences.
  • Prepare for technical interviews by practicing common data analysis problems and scenarios.
  • Showcase your ability to translate complex data insights into actionable business strategies.

Recommended Resources

  • Books:

    • "Python for Data Analysis" by Wes McKinney
    • "Data Science for Business" by Foster Provost and Tom Fawcett
    • "Naked Statistics" by Charles Wheelan

  • Online Courses:

    • Data Analyst Nanodegree by Udacity
    • Google Data Analytics Professional Certificate on Coursera
    • IBM Data Analyst Professional Certificate on Coursera

  • Websites:

    • Coursera, edX, Udacity, DataCamp for structured learning paths
    • Kaggle for datasets and competitions
    • GitHub for showcasing projects