Course description
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.
Upcoming start dates
Suitability - Who should attend?
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
Outcome / Qualification etc.
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
Training Course Content
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
Request info
Rcademy
Rcademy is a global training and consultation organisation set out to bridge the gap between you now and what you can be in the near future. We are facilitators of knowledge impartation. Our team of established and experienced training enthusiasts...