Executive Development Programme in Data Science Implementation
-- ViewingNowThe Executive Development Programme in Data Science Implementation is a certificate course designed to empower professionals with the necessary skills to implement data science in their organizations. This program bridges the gap between traditional management practices and modern data science techniques, making it highly relevant in today's technology-driven world.
6,207+
Students enrolled
GBP £ 140
GBP £ 202
Save 44% with our special offer
ๅ ณไบ่ฟ้จ่ฏพ็จ
100%ๅจ็บฟ
้ๆถ้ๅฐๅญฆไน
ๅฏๅไบซ็่ฏไนฆ
ๆทปๅ ๅฐๆจ็LinkedInไธชไบบ่ตๆ
2ไธชๆๅฎๆ
ๆฏๅจ2-3ๅฐๆถ
้ๆถๅผๅง
ๆ ็ญๅพ ๆ
่ฏพ็จ่ฏฆๆ
โข Fundamentals of Data Science: Introduction to key concepts of data science, including data mining, machine learning, and big data analytics. Understanding of data types, data management, and data pre-processing techniques.
โข Data Analysis Tools and Techniques: Hands-on training in using popular data analysis tools such as Python, R, and SQL. Emphasis on data cleaning, data visualization, and statistical analysis.
โข Data Management and Infrastructure: Exploration of data storage and processing options, including traditional databases, data warehouses, and cloud-based solutions. Overview of data governance, data security, and data privacy.
โข Machine Learning and Predictive Analytics: Deep dive into machine learning algorithms and techniques, including supervised and unsupervised learning, neural networks, and deep learning. Practical applications of predictive analytics and modeling.
โข Data Visualization and Communication: Techniques for presenting data in a clear and effective manner, including data storytelling, data journalism, and visualization tools such as Tableau, Power BI, and ggplot.
โข Business Intelligence and Decision Making: Overview of business intelligence (BI) concepts, including data warehousing, online analytical processing (OLAP), and reporting. Emphasis on using data to inform business decisions and strategy.
โข Data Ethics and Responsibility: Exploration of ethical considerations in data science, including data privacy, data bias, and fairness. Overview of legal and regulatory requirements related to data use.
โข Leadership and Change Management: Strategies for leading and managing data science projects, including stakeholder management, project management, and change management. Emphasis on building a data-driven culture within an organization.
่ไธ้่ทฏ
ๅ ฅๅญฆ่ฆๆฑ
- ๅฏนไธป้ข็ๅบๆฌ็่งฃ
- ่ฑ่ฏญ่ฏญ่จ่ฝๅ
- ่ฎก็ฎๆบๅไบ่็ฝ่ฎฟ้ฎ
- ๅบๆฌ่ฎก็ฎๆบๆ่ฝ
- ๅฎๆ่ฏพ็จ็ๅฅ็ฎ็ฒพ็ฅ
ๆ ้ไบๅ ็ๆญฃๅผ่ตๆ ผใ่ฏพ็จ่ฎพ่ฎกๆณจ้ๅฏ่ฎฟ้ฎๆงใ
่ฏพ็จ็ถๆ
ๆฌ่ฏพ็จไธบ่ไธๅๅฑๆไพๅฎ็จ็็ฅ่ฏๅๆ่ฝใๅฎๆฏ๏ผ
- ๆช็ป่ฎคๅฏๆบๆ่ฎค่ฏ
- ๆช็ปๆๆๆบๆ็็ฎก
- ๅฏนๆญฃๅผ่ตๆ ผ็่กฅๅ
ๆๅๅฎๆ่ฏพ็จๅ๏ผๆจๅฐ่ทๅพ็ปไธ่ฏไนฆใ
ไธบไปไนไบบไปฌ้ๆฉๆไปฌไฝไธบ่ไธๅๅฑ
ๆญฃๅจๅ ่ฝฝ่ฏ่ฎบ...
ๅธธ่ง้ฎ้ข
่ฏพ็จ่ดน็จ
- ๆฏๅจ3-4ๅฐๆถ
- ๆๅ่ฏไนฆไบคไป
- ๅผๆพๆณจๅ - ้ๆถๅผๅง
- ๆฏๅจ2-3ๅฐๆถ
- ๅธธ่ง่ฏไนฆไบคไป
- ๅผๆพๆณจๅ - ้ๆถๅผๅง
- ๅฎๆด่ฏพ็จ่ฎฟ้ฎ
- ๆฐๅญ่ฏไนฆ
- ่ฏพ็จๆๆ
่ทๅ่ฏพ็จไฟกๆฏ
่ทๅพ่ไธ่ฏไนฆ
