Advanced Analytics Course

Why select this training course?

Advanced analytics is an umbrella term for methods that go beyond traditional data science methods. These methods are designed to extract more value from the data and enable businesses to develop a more realistic insight into the operations. Companies use advanced analytics to better understand their customers, predict future outcomes, and make more informed decisions. It is being used to implore practically any question that data can answer. It is typically performed by a data scientist and can take many forms. Advanced analytics is relatively young and evolving and often involves a combination of advanced fields, such as machine learning, computer science, statistics, and economics.

Why has the use of advanced analytics gained traction?

It is an invaluable resource to any business, enabling an organization to get greater performance from its data assets. Firms need robust analytics capacity to properly collect and analyze data, spot trends and patterns, and ultimately raise revenue. Advanced analytics also has the potential to solve complex business problems that traditional business intelligence (BI) reporting cannot. For example, advanced analytics explores a company’s sales data to find patterns that indicate when a salesperson is most likely to close a sale. It can also identify when a lead is no longer profitable or is likely to become a customer, which allows a business to reduce its marketing spend without losing revenue.

What are different business processes that can boost by the use of advanced analytics?

From marketing analytics to supply chain optimization, advanced analytics supports various business processes across the organization. Marketing has always been about making decisions with limited information. Today, the pace of change and the complexity of the digital landscape require marketing organizations to make even more data-driven decisions with limited data sets. The availability of advanced analytics has allowed marketers to test various hypotheses and create customized, targeted marketing campaigns that avoid costly mistakes. Applying analytics to supply chains has resulted in several benefits for businesses. One of the primary uses of advanced analytics is to help an organization predict future demand for its products or services. This allows an organization to create a more active supply chain.

Rcademy’s Advanced Analytics Course is a forward-looking program that equips students with the tools and techniques needed to become data-driven scientists and managers. The course is designed for candidates interested in data science who want to learn how to apply it to their future careers. The course will provide participants with the analytical tools and techniques they need to unlock the power of data and transform their organizations.

Who should attend?

Rcademy’s Advanced Analytics Course is ideal for data-oriented individuals as well as enthusiasts such as:

• Data Managers
• Business Analysts
• Business architects
• Project manager
• IT professionals
• Graduates and Scholars
• Entrepreneurs

What are the course objectives?

Rcademy’s Advanced Analytics Course is aimed at the following objectives:

• To gain capacities beyond traditional analysis techniques to interpret business information
• To efficiently forecast future opportunities and potential threats by leveraging data science
• To overcome the limitations of traditional business intelligence techniques
• To get more adept at decision-making by effectively analyzing essential business information
• To obtain more value from data assets
• To learn various tools such as data mining, data visualization, text mining, and many more
• To optimize the organization’s resources and operations and gain a competitive advantage over its peers through innovative solutions
• To help organizations better adapt to the ever-changing and competitive environment
• To enable the companies to utilize advanced analytics techniques to timely detect risky outcomes and take precautionary steps accordingly

How will this course be presented?

• Interactive sessions
• Use of case studies
• Management games
• Learning preparation of reports, charts, graphs
• Real-time exercises
• Problem-solving and Group discussion sessions

What are the topics covered in this course?

Module 1: Introduction to Advanced Analytics

• Using data science beyond traditional analytics
• Subfields of Analytics
• Advanced Analytics as a valuable resource
• Advanced Analytics vs business intelligence
• The functionality of Advanced Analytics
• Application of Advanced Analytics in sales, marketing, HR, and finance
• Benefits of Advanced Analytics

Module 2: Descriptive Modelling

• Aspects of descriptive modeling
• Segmentation and clustering
• Data aggregation and data mining
• Common Applications of descriptive modeling
• Steps involved in descriptive analytics

Module 3: Predictive Analytics

• Predictive vs descriptive analytics
• Social network analysis, text analysis
• Applications of predictive analytics
• Impact of Big Data
• Regression techniques
• Machine learning techniques

Module 4: Text Analytics

• Steps involved in text analytics
• Text extraction and text classification
• Creating visuals of results
• Natural language Processing
• Preparing unstructured text
• A common application of text analytics

Module 5: Multimedia Analytics

• Moving beyond tabular data
• Multimedia data
• Multimedia with Visual Analytics
• Content-based classification

Module 6: Data Infrastructure

• What is data infrastructure
• Features of strong data infrastructure
• Data infrastructure options
• Poor data infrastructure
• Understanding the data pipeline

Module 7: Deep Learning 

• Neural networks
• Generative methodologies
• Deep belief network
• Machine learning vs deep learning
• Unsupervised learning

Module 8: Applications of Analytics

• Managerial analytics
• Customer-facing analytics
• Operational analytics
• Risk detection and risk management
• Business Analytics
• End user analytics

Module 9: Advanced Analytics Techniques

• Pattern matching
• Forecasting
• Semantic Analysis
• Visualization
• Sentiment analysis
• Cluster and multivariate Analysis

Module 10: Analytics Team

• Organizing analytics team
• Centralized vs decentralized analytics team
• Attracting analytics talent
• Retaining analytics talent