Data Science Basics Roadmap for Non-Data Professionals

From the Business Intelligence team at 2Performant.com, we’ve witnessed firsthand the transformative power of data science in driving successful projects and generating immense value. Across countless initiatives, harnessing the strengths of statistical analysis and data science has unveiled insights that were pivotal for our growth and understanding. As a testament to its significance, we believe that professionals across various fields should equip themselves with these foundational analytical skills. Here’s a roadmap to guide you through this rewarding journey.

1. Laying Foundations and Notions

Notions and Concepts of Data Science

• Understanding Data Science: Grasp the essence, objectives, and significance of data science.
• Data Types and Structures: Differentiate between structured and unstructured data, and familiarize yourself with databases.
• Data Visualization Basics: Get introduced to charts, graphs, and plots to understand data visually.

Recommended Course: Introduction to Data Science by IBM

2. Programming Bases for Data Science

Programming and Data Science

• Python Basics: Learn the syntax, data types, and structures (like lists, tuples, and dictionaries).
• SQL Basics: Familiarize yourself with basic SQL queries, joins, and database management.

Recommended Courses:

1. Python: Python Basics by University of Michigan
2. SQL: Introduction to Structured Query Language

3. Rework and Review

Basic Statistical Concepts, SQL, and Python Foundations

• Descriptive Statistics: Understand measures of central tendency (mean, median, mode) and measures of spread (variance, standard deviation).
• Probability: Grasp basic concepts, including independent events, conditional probability, and Bayes’ theorem.
• Rework SQL and Python: Deepen your understanding and practice your newly acquired SQL and Python skills through exercises.

Recommended Courses:

1. Python: Applied Data Science with Python by University of Michigan
2. Statistics: Statistics and R for Data Science by Duke University
3. SQL: SQL for Data Science by University of California, Davis

4. Advance into Expertise

Advanced Statistical Concepts, SQL for Data Analysis, and Statistical Python

• Inferential Statistics: Delve into hypothesis testing, p-values, and confidence intervals.
• SQL for Analysis: Master complex queries, sub-queries, and optimization techniques for better data extraction.
• Python for Data Analysis: Get comfortable with libraries like Pandas, NumPy, and Matplotlib for efficient data manipulation and visualization.

Recommended Course: Advanced Data Science by IBM

5. Consolidate and Implement

Combining Skills and Knowledge into Projects

• Project Ideas: Depending on your field, tailor data science projects to your needs:
• Sales: Forecast future sales based on historical data using regression.
• Biology: Analyze genome sequences using clustering algorithms.
• Chemistry: Predict properties of unknown compounds based on known data using classification models.
• Engineering: Optimize manufacturing processes using optimization algorithms.
• Remember, learning through courses is crucial, but implementing what you learn in real-world projects is what truly solidifies and showcases your skills.

Conclusion

The omnipresence of data in today’s world signifies more than just numbers. It represents opportunities, insights, and transformative potential. By adding these skills to your professional toolkit, you not only elevate your value in the job market but also embark on a career trajectory that’s dynamic and future-proof. Dive deep into the world of data science and unravel the myriad stories that data can narrate.

Note: This guide is an initial step. The world of data science is vast, and there are numerous resources available to deepen your understanding and hone your skills further. Remember, continuous learning and practical application are the keys to mastery.