Data Analytics & Business Intelligence

A warm welcome to the Data Analytics & Business Intelligence course by Uplatz.

Uplatz brings this detailed course on Data Analytics & Business Intelligence to help you get familiar with the concepts and application of business analysis, the tools & technologies involved in business analytics domain, and finally to get you skilled in applying the same to practical business scenarios.

If you wish to make a successful career in this super trending area of data analytics and business intelligence commanding huge pay packages, then this is perfectly the right course for you to get you started. Besides going into detail of understanding data and performing meaningful analytics on it, this Business Intelligence & Data Analytics course also introduces you briefly to the concepts of machine learning and data science.

Data gathering, storage, and knowledge management are combined with data analysis in business intelligence systems to evaluate and transform complex data into meaningful, actionable information that can be used to support more effective strategic, tactical, and operational insights and decision-making. An organization's business intelligence environments are made up of a number of technologies, applications, processes, strategies, products, and technological architectures that are used to gather, analyze, display, and disseminate internal and external business data.

The use of methodologies such as data mining, predictive analytics, and statistical analysis in order to analyze and transform data into useful information, identify and anticipate trends and outcomes, and ultimately make smarter, data-driven business decisions, is referred to as business intelligence and analytics, a data management solution and business intelligence subset.

A business intelligence platform allows companies to take use of their current data architecture and construct unique business intelligence apps that allow analysts to query and view data. Self-service analytics is supported by modern business intelligence solutions, making it simple for end users to build their own reports. Users may connect to a variety of data sources, including NoSQL databases, Hadoop systems, cloud platforms, and traditional data warehouses, using simple user interfaces mixed with flexible business intelligence backend software to produce a unified picture of their heterogeneous data.

This Business Intelligence and Data Analytics course aims to produce a select group of skilled individuals who are cross-trained in business process analysis, technology management, and technically skilled in all aspects of data science, such as predictive modelling, analytical reporting, GIS mapping, segmentation analysis, and data visualization along with machine learning & deep learning. Students will acquire the skills to integrate cutting edge information and analytics technologies with best practices and applied business methods.

The Business Intelligence & Analytics course combines analytical and professional skills to help you become the type of manager who questions assumptions and makes evidence-based choices based on facts. You'll learn new skills that will help you improve your goods, services, and strategies while leading your firm through markets that are always changing due to technological advancements. Machine learning, language processing, data mining, data modeling including predictive modeling, optimization, NoSQL, NLP are among the topics covered in the curriculum, which are at the forefront of the data revolution. Classes go beyond the fundamentals of understanding data and using it for making business decisions, to addressing essential business ideas.

Data Analytics & Business Intelligence - course curriculum

1. Introduction to BI Concepts, Examples, and Applications

2. Introduction to Predictive Modeling

3. Introduction to NoSQL

4. Hierarchical Clustering

5. Introduction to Salesforce

6. Introduction to NLP

7. Introduction to Apache Server

8. Business Intelligence deep-dive

9. Data Warehousing

10. Types of Data

11. Mobile BI

12. Real-time BI

13. Data Analytics

14. Data Analytics vs. Business Analysis

15. Embedded Analytics

16. Collection Analytics

17. Survival Analytics

18. Machine Learning Techniques

19. Geospatial Predictive Analytics

20. Cohort Analysis

21. Data Mining

22. Anomaly Detection

23. Statistically Sound Associations

24. Cluster Analysis

25. DB Scan

26. Regression Models

27. Extraction-based Summarization

28. Machine Learning in BI

29. Machine Learning vs. BI

30. How ML can make BI better

31. Understanding Data Warehousing

32. Understanding Data Mart

33. Understanding Data Dimensions

34. Understanding Data Vault Modeling

35. Understanding Links and Satellites