Certificate in Digital Metrics for Science Analysis Professionals
-- ViewingNowThe Certificate in Digital Metrics for Science Analysis Professionals is a comprehensive course designed to equip learners with the essential skills needed to thrive in today's data-driven world. This course focuses on the importance of digital metrics in scientific analysis, providing learners with a deep understanding of how to collect, analyze, and interpret data to make informed decisions.
2,106+
Students enrolled
GBP £ 149
GBP £ 215
Save 44% with our special offer
ě´ ęłźě ě ëí´
100% ě¨ëźě¸
ě´ëěë íěľ
ęłľě ę°ëĽí ě¸ěŚě
LinkedIn íëĄíě ěśę°
ěëŁęšě§ 2ę°ě
죟 2-3ěę°
ě¸ě ë ěě
ë기 ę¸°ę° ěě
ęłźě ě¸ëśěŹí
⢠Introduction to Digital Metrics: Understanding key terms, concepts and the importance of digital metrics in science analysis.
⢠Web Analytics Tools: Overview of tools and platforms for collecting and analyzing digital data, including Google Analytics.
⢠Data Collection and Management: Techniques for collecting and organizing data from various digital sources.
⢠Data Analysis Techniques: Statistical methods and data visualization tools for analyzing digital data.
⢠Science-Specific Metrics: Key performance indicators (KPIs) for scientific research, including article views, citations, and social media engagement.
⢠Digital Metrics for Research Impact: Using digital metrics to measure and communicate the impact of scientific research.
⢠Ethical Considerations: Best practices for protecting privacy, combating bias, and ensuring the responsible use of digital metrics in science analysis.
⢠Case Studies: Real-world examples of digital metrics in action in scientific research and analysis.
⢠Future Trends: Emerging trends and technologies in digital metrics, including artificial intelligence and machine learning.
Note: The above list of units is a suggestion, and the final curriculum should be tailored to meet the needs and goals of the target audience.
ę˛˝ë Ľ 경ëĄ
- Data Scientist: These professionals use statistical methods and machine learning techniques to extract insights from large datasets.
- Data Analyst: Data analysts collect, process, and perform statistical analyses on data to help businesses make informed decisions.
- Business Intelligence Analyst: These professionals use data to identify business trends, opportunities, and areas for improvement.
- Data Engineer: Data engineers design and build the infrastructure needed to collect, store, and process large datasets.
- Data Visualization Specialist: These professionals create visual representations of complex data to help stakeholders understand key insights and trends.
ě í ěęą´
- 죟ě ě ëí 기본 ě´í´
- ěě´ ě¸ě´ ëĽěë
- ěť´í¨í° ë° ě¸í°ëˇ ě ꡟ
- 기본 ěť´í¨í° 기ě
- ęłźě ěëŁě ëí íě
ěŹě ęłľě ěę˛Šě´ íěíě§ ěěľëë¤. ě ꡟěąě ěí´ ě¤ęłë ęłźě .
ęłźě ěí
ě´ ęłźě ě ę˛˝ë Ľ ę°ë°ě ěí ě¤ěŠě ě¸ ě§ěęłź 기ě ě ě ęłľíŠëë¤. ꡸ę˛ě:
- ě¸ě ë°ě 기ę´ě ěí´ ě¸ěŚëě§ ěě
- ęśíě´ ěë 기ę´ě ěí´ ęˇě ëě§ ěě
- ęłľě ě겊ě ëł´ěě
ęłźě ě ěąęłľě ěźëĄ ěëŁí늴 ěëŁ ě¸ěŚě뼟 ë°ę˛ ëŠëë¤.
ě ěŹëë¤ě´ ę˛˝ë Ľě ěí´ ě°ëŚŹëĽź ě ííëę°
댏롰 ëĄëŠ ě¤...
ě죟 돝ë ě§ëʏ
ě˝ě¤ ěę°ëŁ
- 죟 3-4ěę°
- 쥰기 ě¸ěŚě ë°°ěĄ
- ę°ë°Ší ëąëĄ - ě¸ě ë ě§ ěě
- 죟 2-3ěę°
- ě 기 ě¸ěŚě ë°°ěĄ
- ę°ë°Ší ëąëĄ - ě¸ě ë ě§ ěě
- ě 체 ě˝ě¤ ě ꡟ
- ëě§í¸ ě¸ěŚě
- ě˝ě¤ ěëŁ
ęłźě ě ëł´ ë°ę¸°
íěŹëĄ ě§ëś
ě´ ęłźě ě ëšěŠě ě§ëśí기 ěí´ íěŹëĽź ěí ě˛ęľŹě뼟 ěě˛íě¸ě.
ě˛ęľŹěëĄ ę˛°ě ę˛˝ë Ľ ě¸ěŚě íë