Basic-Level Data Science Roadmap [PRELIMINAR VERSION - WORK IN PROGRESS]
Basic-Level Data Science Roadmap
[PRELIMINARY VERSION - WORK IN PROGRESS]
Key Recommendations:
Practice, Practice, Practice: Continuously replicate existing projects and modify key aspects.
Understand Statistics Early: Ignoring statistics will slow down progress and lead to confusion later.
Initial Focus:
- 70% on Statistics & Mathematics Foundations
- 30% on Programming
Real-World Projects:
- If you come from accounting: Automate reporting using scripts.
- Develop fact-based decision models for financial data.
Ask for Help: Engage with experts, join data science communities (e.g., Stack Overflow).
Use Reliable Resources: Select high-quality learning materials and commit to completing them.
High-Demand Skills to Develop Quickly
- Data Manipulation (SQL, Pandas, dplyr)
- Data Visualization (Choose the tool that suits you best)
- Data Analysis (Interpreting statistical insights)
Key Soft Skills
- Problem-Solving (critical for data-driven decision-making)
- Storytelling (effective communication of data insights)
- Critical Thinking
- Teamwork
Resources
Programming & SQL:
Community Support: Stack Overflow, Kaggle
Data Science Platforms: DataCamp, Coursera, edX
Key Concepts for Everyday Applications in Data Science
Statistics & Mathematics Foundations
- Descriptive Analysis (Mean, Median, Mode, Variance, Standard Deviation, Histograms, etc.)
- Exploratory Data Analysis (EDA: identifying outliers, missing data handling, transformations)
- Inferential Statistics (Hypothesis Testing, p-value, Central Limit Theorem, Confidence Intervals)
- Regression Models (Linear, Logistic, ARIMA, Multiple, Time Series Analysis)
- Mathematical Foundations (Linear Algebra, Calculus)
Essential Skills
- Data Manipulation: SQL (SELECT, JOIN, GROUP BY), Python (Pandas, R dplyr)
- Data Structures: Types, syntax
- Productivity Tools: GitHub, VSCode, DB Management Systems
- Debugging & Error Handling: Stack Overflow, AI-assisted tools
Software Development & Cloud Computing
- Collaboration Tools: GitHub, version control
- Cloud Platforms: Basics of cloud storage and computing for large-scale projects
