How Much Math You Need to Learn to Become a Data Professional

Introduction

• Overview of required math for data professionals
• Focus on various categories and examples to simplify understanding
• Importance of subscribing to resources for further learning

Categories of Data Professionals

1. Data Analyst

   • Entry level position
   • Responsible for basic data interactions and reporting basic insights.

2. Business Analyst

   • Works on extracting insights to solve business problems.
   • Requires interaction with business heads and stakeholders.

3. Data Scientist

   • Engages in sophisticated data analysis and model building.
   • Requires advanced understanding of algorithms and predictive analytics.

4. Others

   • MLOps Engineer, Data Engineer, Machine Learning Manager, AI Manager.

Importance of Math in Data Science

• Math is essential across all data roles.
• Key areas of math required:
  1. Statistics
     • Subcategories: Descriptive, Inferential, Hypothesis Testing, Regression, Time Series.
  2. Linear Algebra
     • Key areas: Matrices, Vectors, Optimization.
  3. Calculus
     • Focus on differentiation and integration.
  4. Discrete Mathematics
     • Topics: Combinatorics, Graph Theory, Probability Theory.

Statistics Breakdown

Descriptive Statistics

• Provides summaries of data through measures of central tendency and dispersion.
  • Measures of Central Tendency: Mean, Median, Mode.
  • Measures of Dispersion: Variance, Standard Deviation, Range.

Inferential Statistics

• Makes predictions or inferences about a population based on a sample.
• Involves estimating population parameters and hypothesis testing.

Hypothesis Testing

1. Formulate Hypothesis
   • Null Hypothesis (H0): Initial assumption.
   • Alternate Hypothesis (H1): What you aim to prove.
2. Testing
   • Use Z-Test or T-Test to analyze data.
   • Determine acceptance or rejection of null hypothesis based on test results.

Type of Errors in Hypothesis Testing

1. Type I Error (α): Rejecting a true null hypothesis.
   • Results in a false positive.
2. Type II Error (β): Failing to reject a false null hypothesis.
   • Results in a false negative.

Z-Test vs T-Test

• Z-Test: Used when sample size > 30 and population standard deviation is known.
• T-Test: Used when sample size < 30 or population standard deviation is unknown.

Conclusion

• Math is critical for various data roles.
• Emphasis on statistics, especially in hypothesis testing.
• Understand errors and their implications in data analysis.
• Encouragement to explore further resources and practice.