Course Content:
Fundamentals of Data Analytics:
Introduction to data analytics: key concepts and their relevance in the modern business environment.
Data analytics life cycle: collection, processing, analysis, interpretation and communication of results.
Data management principles: data quality, cleaning and preparation for analysis.
Data Analysis Tools and Techniques:
Advanced use of tools such as Excel, SQL and Python for the analysis of large volumes of data.
Introduction to R and SAS for statistical analysis and data modeling.
Data mining techniques: clustering, regression, and time series analysis.
Data visualization:
Principles of data visualization: how to tell a story with data using graphs, tables, and dashboards.
Use of visualization tools such as Tableau and Power BI to create interactive reports and executive presentations.
Best practices in communicating insights to non-technical audiences.
Predictive Modeling and Machine Learning:
Introduction to machine learning and its application in business data analytics.
Development and validation of predictive models: linear regression, decision trees and classification models.
Implementation of machine learning algorithms for forecasting and customer segmentation.
Advanced Data Analysis:
Advanced analysis techniques: cohort analysis, basket analysis and recommendation models.
Sentiment analysis and natural language processing (NLP) for the interpretation of unstructured data.
Use of big data in decision making: case studies in sectors such as finance, retail and healthcare.
Data Analytics Project Management:
Design and implementation of data analytics projects in a business environment.
Management of data analytics teams: roles and responsibilities, agile methodologies.
Integration of data analytics in corporate strategy and its impact on competitiveness.
Ethics and Privacy in Data Analytics:
Ethical considerations in data collection and use: privacy, consent and security.
Compliance with regulations such as GDPR and CCPA in handling personal data.
Strategies to protect data integrity and avoid bias in analysis.
Final Certification Project:
Participants will apply everything they have learned to develop a real data analytics project.
The project will include data collection and preparation, advanced analysis, creation of visualizations and presentation of insights to a panel of experts.
Evaluation of the impact of the analysis performed and strategic recommendations based on the results.
Additional Benefits:
International Certification in Data Analytics:
Upon completion of the course, you will obtain a globally recognized certification that endorses your competencies in data analytics, making you a highly demanded professional in the market.
Access to Tools and Specialized Software:
During the course, you will have access to data analytics software licenses such as Tableau, Power BI, and machine learning tools for practical use.
Personalized Mentoring and Networking Opportunities:
Connect with experts in the field of data analytics, receive personalized career guidance, and participate in exclusive networking events.
Course Duration: 9 months
Modality: 100% online, with live classes, hands-on labs, and access to recorded content.
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