Advanced Data Analysis Certification Course

Why select this training course?

Data is the new oil. Businesses worldwide generate record amounts of data daily; in many cases, they don’t know what to do with it. That’s a huge missed opportunity. In the past, businesses could rely on their customers to provide information. With the onset of the internet, businesses have captured a wealth of additional data about their customers and operations. This data has become a valuable resource for businesses, yet it is often underutilized. Businesses have long understood the importance of data, with the largest companies in the world spending millions of dollars on information management every year. But data has become so abundant that companies can no longer afford to store and analyze it; they need to find ways to make the most of it to drive growth. That means going beyond the traditional approach of storing and analyzing data. It implies turning data into actionable information that can be used to make business decisions.

Why learn advanced data analysis?

As more and more companies realize the importance of becoming data-driven businesses to survive in the modern economy, the demand for advanced data analysis is on the rise. And why not? The ability to use data to make informed decisions can have a significant impact on a company’s bottom line. As data analytics becomes more sophisticated, the ability to turn data into actionable information will become even more critical. Data scientists are in high demand, with salaries of $200,000 and up being common. Data analysts can perform basic data analysis but lack the background to add significant value and command high salaries. Meanwhile, those with the proper training and experience can find salaries in the high six figures or even higher.

What does advanced data analysis entail?

Data analysis is the process of using data to extract information that can be used to make business decisions. It encompasses various techniques, including data mining, machine learning, and data visualization. Companies use data analytics to better understand their customers, identify trends, and improve their operations.

Rcademy’s comprehensive Advanced Data Analyst Certification Course has been compiled to provide fundamentals of the essential data analysis solutions presently relevant in the industry. Data analysis is a field that offers a variety of career opportunities, but it’s also a field that requires a significant amount of education and training. This course is designed for enthusiastic individuals looking to establish themselves in a data-driven organization who want to learn more about data analysis but don’t have time for a full-time degree. By the end of the course, you’ll have the knowledge and resources needed to get started in data science, no matter where you are in your career.

Who should attend?

Rcademy’s Advanced Data Analysis Certification 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 Data Analysis Certification Course is aimed at the following objectives:

• To inculcate enhanced and quick decision-making through the use of Advanced Data Analysis
• To learn to use advanced data analysis for detecting risk and fraud
• To gain accuracy and efficiency in forecasting
• To take advantage of Advanced Data Analysis for automating business operations
• To gain better insights into analytical and business scenarios with the help of Advanced Data Analysis tools
• To transform the organization into a proactive unit instead of a reactive enterprise by being ready beforehand through Advanced Data Analysis
• To gain knowledge of different types of analysis available to perform on business situations
• To deal with data acquisition efficiency and enhance access to data
• To gain hands-on knowledge of various data analysis techniques and their applicability in daily life
• To use Advanced Data Analysis for not only identifying business opportunities but also detecting potential threats in the market
• To gain a competitive edge over others in the market with Rcademy’s Data Analysis Certification Course

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: Types of data analysis

• Descriptive analysis
• Predictive analysis
• Diagnostic analysis
• Exploratory analysis
• Use cases of Advanced Data Analysis
• Phases of Data Analysis

Module 2: Big Data

• Big data platform
• Nature of data
• Intelligent data analysis
• Analysis vs reporting
• Mining data streams

Module 3: Machine Learning Techniques

• Generative learning
• Classification methods: Linear discriminant analysis
• Markov model
• Neural networks

Module 4: Database Revolution

• System architecture
• Relational database
• Transaction management
• Data warehouse and data mining
• Distributed database patterns
• Non-relational distributed database

Module 5: Graph Analysis

• Large-scale graph
• Co-occurrence graphs
• Graph data structures
• Social networking algorithms
• V-graph representation

Module 6: Data Acquisition

• OLTP and OLAP
• Data similarity and dissimilarities
• Data extraction process
• Data cleaning
• Data transformation
• Data reduction

Module 7: Advanced Data Analysis Techniques

• Regression analysis
• Classification analysis: decision tree
• Supervised learning
• Clustering analysis
• Unsupervised learning
• Association rule mining
• Anomaly detection analysis
• Application of techniques in business problem solving

Module 8: Deep Learning

• Convolutional neural networks
• Memory-augmented neural networks
• Deep reinforcement learning
• Markov decision process
• Application of deep learning in business

Module 9: Data Analysis with Excel

• Data consolidation
• What-if analysis
• Importing and exporting data
• Creating the data model
• Pivot table
• Defining key performance indicators

Module 10: Data Visualisation

• Charts and plots
• Multivariate data visualization
• Visualization techniques: pixel, geometric, icon-based, hierarchical visualization
• Visualization tools

Module 11: Web Analytics

• Basics of web intelligence
• Qualitative analysis: site visits, surveys, questionnaires
• URLs, cookies, navigation reports, search analytics
• SEO
• Google Analytics