Data Analysis Training Certification Course

Why Select this Training Course?

Opting for the Data Analysis Training Certification Course is a career-defining step for professionals who want to critically analyse and interpret data. This course gives you a hands-on learning experience and an in-depth understanding of the most powerful data analysis tools and methodologies used in the industry.

Is this course practical and hands-on?

Yes, the course is constructed to help you gain practical experience through hands-on exercises using real-world data so that your skills are ready for a business context from day one.

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Is this course current with data analysis trends?

Yes, this course incorporates the most in-demand trends, tools, and best practices that will help you stay on top in the fast-changing field of data analysis.

Who Should Attend?

This course is designed for:

• Data Analysts
• Business Analysts
• Marketing Analysts
• Financial Analysts
• Data Science Enthusiasts
• Aspiring Data Professionals
• Operations Managers
• IT Professionals

What are the Course Objectives?

On completion of the course, the participants will:

• Pick up the necessary skills in the leading programming languages and analytics software.
• Be conversant with not only the advanced data analysis techniques and methodologies but also their applications.
• Acquire valuable skills for making data as a tool for change and improvement.
• Familiarise themselves with clear communication of information to all the concerned parties.

How will this course be presented?

The course will be delivered through:

• Hands-on group activities, presentations and discussions.
• The set of latest digital materials and study tools to help self-preparation.
• The implementation of real-time data analytics and the practical application of real-time data in analytics.
• Teamwork and interactive group assignments to reinforce understanding and learning.
• Feedback from industry experts and instruction in skill refining.

What are the Topics Covered in this Course?

Module 1: Advanced Data Governance and Ethics

• Setting up a data governance framework.
• Roles and responsibilities in data governance.
• Implementing data stewardship for data quality.
• Data ethics in the age of big data and AI.
• Case studies on data breaches and their implications.
• Future trends and challenges in data governance.

Module 2: Qualitative Data Analysis

• Understanding qualitative data and its applications.
• Structuring and coding qualitative data.
• Thematic analysis and pattern recognition.
• Techniques for ensuring validity and reliability.
• Tools for qualitative data analysis.
• Presenting qualitative data findings.

Module 3: Data Wrangling and Cleaning

• Techniques for handling missing and inaccurate data.
• Data transformation and normalisation.
• Automation of data cleaning processes.

Module 4: Big Data Analytics

• Introduction to big data concepts and tools.
• Data mining techniques for big datasets.
• Predictive analytics and model building.
• Machine learning algorithms for data analysis.
• Natural language processing (NLP) in data analysis.
• Big data visualisation strategies.
• Data governance and ethics in big data.
• Real-time data processing and analytics.
• Big data in cloud environments and its scalability.

Module 5: Applied Analytics Using Excel

• Advanced Excel functions for data analysis.
• Pivot tables and PivotChart reports.
• Data modelling with Excel.
• Excel add-ins for analytics (Power Pivot, Power Query).
• Visual analytics with Excel.

Module 6: Data Analysis with SQL

• SQL commands for data retrieval and manipulation.
• Joining tables and combining queries.
• Aggregation functions and subqueries.
• Optimising SQL queries for performance.

Module 7: Business Intelligence and Data Visualisation

• Principles of business intelligence (BI).
• BI tools and platforms.
• Dashboards and reporting best practices.
• Implementing a BI strategy in organisations.
• Key BI trends and their applications.
• Visualisation tools and software (e.g., Tableau, Power BI).
• Tailoring reports to different audience needs.
• Storytelling with data.

Module 8: Advanced Analytics with R and Python

• Overview of R and Python in data analysis.
• Libraries and frameworks for statistical analysis and visualisation.
• Automating analysis workflows with scripts.
• Data manipulation with Pandas in Python.
• R for statistical modelling and hypothesis testing.
• Python and R integration for data analysis projects.
• Collaborative analytics with version control (e.g., Git).
• Packaging and sharing analytical models.
• Ethical implications of algorithm-based decisions.

Module 9: Decision Science and Risk Analysis

• Frameworks for decision-making with data.
• Risk assessment methodologies.
• Applying decision science to business strategy.

Module 10: Machine Learning for Data Analysis

• Overview of machine learning concepts.
• Supervised vs unsupervised learning methods.
• Implementing machine learning models for data analysis.

Module 11: Advanced Predictive Analytics

• Deploying predictive models into production environments.
• Model evaluation and tuning to improve accuracy.
• Advanced regression techniques, including logistic and ridge regression.
• Neural networks and deep learning applications in predictive analytics.
• Time Series forecasting for financial and market trend analysis.
• Predictive modelling for customer behaviour analysis.
• Ensemble learning techniques to combine multiple models.
• Best practices in predictive analytics project management.

Module 12: Data Strategy and Management

• Developing a data strategy aligned with business objectives.
• Data lifecycle management from collection to archiving.
• Building data warehouses and data marts for analytics.
• Implementing data quality assurance processes.
• Master data management (MDM) and its importance.
• Data privacy, security, and compliance in the age of GDPR.
• Data monetisation strategies.

Module 13: AI-Driven Analytics

• Fundamentals of artificial intelligence in data analysis.
• Machine learning pipelines and data workflows with AI.
• Integration of AI for enhanced descriptive analytics.
• Text analytics and sentiment analysis using AI.
• AI techniques for customer segmentation and personalisation.
• Ethical considerations and transparency in AI.
• Performance metrics for AI systems.

Module 14: Real-Time Analytics and IoT Data

• Internet of Things (IoT) and its impact on data analytics.
• Architecture for real-time data processing and analysis.
• Utilising event streaming platforms like Kafka and Azure Event Hubs.
• Analytics on time-series data from IoT sensors.
• Edge analytics and processing data on the device.
• Security and privacy concerns in IoT and real-time data.
• Case studies of IoT analytics in various industries.

Module 15: Cloud Data Analytics with AWS and Azure

• Navigating cloud data solutions on AWS and Azure platforms.
• Serverless data analytics architectures.
• Big data processing with AWS Kinesis and Azure Stream Analytics.
• Data warehousing with Amazon Redshift and Azure SQL Data Warehouse.
• ETL operations with AWS Glue and Azure Data Factory.
• Leveraging cloud AI services for advanced analytics.
• Cost management and optimisation strategies for cloud analytics.

Module 16: Applied Social Network Analysis

• Understanding the principles of social network theory.
• Analysis of social networks for marketing insights.
• Tools and techniques for visualising social networks.
• Measuring the influence and reach within a network.
• Community detection and analysis of group dynamics.
• Sentiment analysis within social network content.
• Privacy and ethical considerations in social network analysis.

Module 17: Geo-Spatial Data Analysis

• Principles of geographic information systems (GIS).
• Integrating location data with traditional data sets.
• Spatial analysis techniques and tools.
• Visualisation of geospatial data with heatmaps and choropleth maps.
• Application of geospatial analytics in urban planning, logistics, and retail.

Leveraging satellite and drone imagery for advanced analytics.