Intro to Analytics Chapter 1

Understanding Data Science

• Data Science Defined

  • The practice of using data to understand and solve real-world problems.

  • Not a new concept; however, the amount of data available has dramatically increased in the last decade.

• Key Statistics

  • Data scientists often report high job satisfaction, work autonomy, and competitive salaries (median > $100,000 in the U.S. in 2019).

  • Demand for data scientists has surged since the title's creation in 2008.

Job Realities and Expectations

• Challenges for Data Scientists

  • Companies often have unrealistic expectations of what data scientists can achieve.

  • Misunderstanding of data science can lead to overwhelming workloads without adequate support.

  • Common scenarios include:

  • Lack of mentorship or guidance

  • Pressure to implement complex systems without foundational work

• Misconceptions by Job Seekers

  • Expectations of constant engagement and quick resolutions may overlook the extensive data preparation involved.

Skills Required in Data Science

1. Core Skills

• Mathematics and Statistics

  • Important for understanding techniques and their applications.

  • Three Levels of Knowledge:

  1. Awareness of techniques

  2. Ability to apply techniques

  3. Skill in choosing techniques based on the problem

• Programming and Databases

  • Essential for data extraction, cleaning, manipulation, and analysis.

  • Common languages include R and Python.

  • SQL is fundamental for database management.

• Business Understanding

  • Ability to translate business needs into data questions and actionable insights.

  • Importance of knowing how data is structured and processes within the business.

Types of Data Science Roles

1. Analytics

• Role focuses on preparing and presenting data insights via dashboards and reports.

2. Machine Learning

• Involves creating and deploying machine learning models for continuous use.

3. Decision Science

• Uses data to generate insights that help guide business decisions.

Related Job Roles

• Business Intelligence Analyst

  • Typically uses fewer statistical and programming skills; employs tools like Excel.

• Data Engineer

  • Ensures the maintenance of data infrastructure and supports data scientists rather than performing analyses.

• Research Scientist

  • Focuses on creating new methodologies and tools often requiring advanced degrees.

Career Path and Market Insights

• Understanding Trends

  • Data science is now competitive; many newcomers from bootcamps and courses crowd the job market.

  • Different companies may have varying definitions of 'data scientist' roles.

• Assessing Job Traps

  • Importance of evaluating a company's data infrastructure and team before applying.

  • Look for experienced leadership and a supportive environment for continuous learning.

Conclusion

• Skill Sets in Variability

  • Different areas of focus within data science: analytics, machine learning, and decision science.

  • Not all data scientists need to be experts in every discipline, but foundational knowledge and the ability to learn are crucial.