**What is Data Science?**

Data Science is like a detective for businesses. It delves deep into data to uncover **hidden patterns,** trends, and insights.

Imagine a supermarket deciding which products to place at the checkout counters. Data Science can analyze past sales data and predict which items are most likely to be impulse buys.

**Essential Pillars of Data Science**

Data Science is a vibrant blend of three primary colors or pillars:

**1. Computer Science**

**Programming**: Tools like Python, R, and Java help you talk to computers.**Database and SQL**: Think of these as vast digital libraries. PostgreSQL and MySQL are some popular ones.**Data Analysis**: Pandas and Numpy are like your data magnifying glasses.**Data Visualization**: Tools like Matplotlib and Seaborn help you paint data stories.**Machine Learning**: With Scikit-learn and TensorFlow, you can teach computers to learn from data.

**2. Statistics and Mathematics**

**Statistics**: Dive into hypothesis testing and regression analysis to make sense of data.**Mathematics**: Linear algebra and calculus are the building blocks of many algorithms.**Machine Learning Theory**: Learn about supervised learning (teaching computers with labeled data) and unsupervised learning (letting computers figure it out).

**3. Business Acumen**

**Problem Solving**: Spot challenges, propose hypotheses, and prove them.**Communication**: Translate complex data findings into actionable business strategies.**Marketing**: Use data to tailor marketing strategies and reach the right audience.

**How Much Does Data Scientist Make?**

According to the research of Built In, a Data Scientist in the US earns an average salary of **$124,887**.

And they receive an additional cash compensation of $13,721 on average.

The salaries can range from $50K to $345K based on experience and location.

**Process of Data Science**

- Define the problem or question.
- Collect and clean the data.
- Explore and analyze the data.
- Build and validate predictive models.
- Interpret and communicate the results.
- Deploy the solution and monitor its performance.

**Learn Data Science from Scratch**

**Kickstart with Programming**: Begin with Python due to its simplicity and vast libraries.**Dive into Data Analysis**: Use Pandas and Numpy to dissect data and draw insights.**Data Visualization**: Use Matplotlib and Seaborn to represent data visually.**Organize Data with Databases**: Learn SQL and use databases like PostgreSQL.**Statistics and Mathematics**: Understand hypothesis testing, regression analysis, and foundational math.**Machine Learning**: Dive into supervised and unsupervised learning.**Business Instincts**: Develop problem-solving techniques and learn effective communication.**Hands-on Practice**: Engage in real-world projects, hackathons, or Kaggle competitions.

*Beginner’s Tip*: Start with online Python courses for beginners and stay curious!

**For futher information, refer to How to Learn Data Science from Scratch: Expert Approved (2023)**