2017年-数据局_Packt：数据科学领域薪资和技能报告_24页_1mb

Summary of the Skill Up Report: Data Science & Business Intelligence

Core Content

This report provides an in-depth analysis of the current state and future trends in the field of Data Science and Business Intelligence (BI). It highlights the value of data-oriented roles across various industries, the technologies commonly used, and the skills that are expected to be in high demand in the coming years. The report is based on the results of Packt's Skill Up survey, which received over 3,800 responses from professionals in the data science community.

Main Points and Key Insights

1. Industry Breakdown

• Finance and Banking is the best paid industry for inexperienced data professionals, driven by the rise of algorithmic trading and the importance of Big Data in financial operations.
• Media / Advertising / Entertainment and Gaming is also a significant sector for data jobs, offering substantial salaries to newcomers.
• SMEs (Small and Medium Enterprises) are a great starting point for inexperienced data scientists and analysts due to their higher starting salaries compared to Enterprise organizations.
• Consulting is where the most experienced professionals are concentrated, indicating a high demand for external data expertise.

2. Career Development

• Data Architect is one of the most valuable roles in the Media and Entertainment sector, commanding higher salaries compared to other roles in that industry.
• Statistician earns slightly more than Data Architect in the Finance sector, suggesting different priorities in skill valuation across industries.
• Machine Learning is becoming a central focus for all data professionals, driven by the demand for predictive insights and statistical analysis.

3. Technology Usage

• Python and R are the most widely used data science languages, with over 25% of respondents using Python daily.
• Distributed computing and machine learning tools are increasingly important.
• Big Data tools such as Hadoop and Apache Spark are gaining prominence, with Spark showing particular growth.
• Data Visualizers use JavaScript, HTML, CSS, and jQuery to create web-based data insights.
• Programmatic Data Wranglers rely heavily on Python, C++, Pandas, and MATLAB for data cleaning and manipulation.
• Big Data Experts use Hadoop, Scala, Spark, and Java for handling large-scale data.
• Data Architects use SQL, Oracle, Microsoft, and SSIS/SSRS for organizing and managing data.

4. Emerging Trends

• Machine Learning is expected to be a focal point in the next 12 months, with a strong emphasis on delivering rapid insights.
• Internet of Things (IoT) and Augmented Reality (AR) are anticipated to be key challenges and opportunities for data scientists, changing how data is collected and analyzed.
• Web-based technologies are becoming more important, enabling data to be shared and visualized more effectively.
• NoSQL databases are expected to grow in popularity due to their scalability and flexibility.
• Excel remains a vital tool, with many professionals still using it for data analysis, especially in less technically advanced environments.

5. Future Skills and Tools

• Learning Julia is gaining traction due to its performance and features, such as multiple dispatch and JIT compiler benchmarks.
• Apache Spark is likely to become more prominent than Hadoop in the coming years, but Hadoop is still widely used.
• Broadening language skills (e.g., learning both Python and R) is essential for flexibility and adaptability.
• Understanding distributed computing and cluster networks is becoming increasingly valuable as data projects grow in scale and complexity.
• IoT is a trend to watch, with its potential to redefine the data science landscape.

6. Recommendations for Data Scientists

• Expand your technical skills by learning multiple programming languages.
• Focus on Machine Learning to stay competitive and relevant.
• Consider working in Finance or SMEs for better starting salaries and more opportunities.
• Specialize in Data Architecture if interested in the Media and Entertainment industry.
• Master Big Data tools like Hadoop and Spark, especially in the context of distributed networks.
• Stay updated on emerging technologies like IoT and AR, which are expected to shape the future of data science.

Conclusion

The data science field is evolving rapidly, with a strong emphasis on machine learning, distributed computing, and emerging technologies like IoT and AR. The report underscores the importance of continuous learning and adaptability in the face of these changes, highlighting that the demand for data professionals is growing across various sectors, especially in Finance and Media. Excel remains a staple tool, while new languages and frameworks are on the rise, offering exciting opportunities for those who stay ahead of the curve.