Overview:

These short-term courses in data analytics teach the fundamentals of data analysis, extraction, manipulation, and interpretation so that you may draw conclusions and meet specific data goals you set out for yourself.

To acquire a job as a data engineer or database administrator, data analyst, or financial analyst after graduating from a data analytics certification or diploma school is simple: just enroll in the programme and pass the exams. Businesses or students who wish to start their own firm might benefit from data analytics courses, since it has several uses in getting customer insight.

In this Data Analyst certification programme, you’ll learn how to use SQL databases, the R and Python languages, how to construct data visualisations, and how to use statistics and predictive analytics in a business context.

Key Learning’s:

  • Recognize how to address analytical difficulties in real-life situations.
  • Define realistic goals for analytics initiatives.
  • Working with many forms of data
  • Recognize the value of data visualisation in making better business decisions and increasing ROI.
  • Understand and use analytics charts, graphs, and tools to generate meaningful insights.
  • Create a framework for analytics adoption and identify emerging data analytics trends.

Course Modules

  • Introduction
  • Data Analytics: Importance
  • Digital Analytics: Impact on Accounting
  • Data Analytics Overview
  • Types of Data Analytics
  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Data Analytics: Amazon Example
  • Data Analytics Benefits: Decision-making
  • Data Analytics Benefits: Cost Reduction
  • Data Analytics Benefits: Amazon Example
  • Data Analytics: Other Benefits
  • Key Takeaways
  • Introduction
  • Terminologies in Data Analytics – Part One
  • Terminologies in Data Analytics – Part Two
  • Types of Data
  • Qualitative and Quantitative Data
  • Data Levels of Measurement
  • Normal Distribution of Data
  • Statistical Parameters
  • Key Takeaways
  • Introduction
  • Data Visualization
  • Understanding Data Visualization
  • Commonly Used Visualizations
  • Frequency Distribution Plot
  • Swarm Plot
  • Importance of Data Visualization
  • Data Visualization Tools – Part One
  • Data Visualization Tools – Part Two
  • Languages and Libraries in Data Visualization
  • Dashboard Based Visualization
  • BI and Visualization Trends
  • BI Software Challenges
  • Key Takeaways
  • Introduction
  • The Data Science Domain
  • Data Science, Data Analytics, and Machine Learning – Overlaps
  • Data Science Demystified
  • Data Science and Business Strategy
  • Successful Companies Using Data Science
  • Travel Industry
  • Retail
  • E-commerce and Crime Agencies
  • Analytical Platforms Across Industries
  • Key Takeaways
  • Introduction
  • Data Science Methodology
  • From Business Understanding to Analytic Approach
  • From Requirements to Collection
  • From Understanding to Preparation
  • From Modeling to Evaluation
  • From Deployment
  • Key Takeaways
  • Introduction
  • Analytics for Products or Services
  • How Google Uses Analytics
  • How Linkedin Uses Analytics
  • How Amazon Uses Analytics
  • Netflix: Using Analytics to Drive Engagement
  • Netflix: Using Analytics to Drive Success
  • Media and Entertainment Industry
  • Education Industry
  • Healthcare Industry
  • Government
  • Weather Forecasting
  • Key Takeaways
  • Introduction
  • Case Study: EY
  • Customer Analytics Framework
  • Data Understanding
  • Data Preparation
  • Modeling
  • Model Monitoring
  • Latest Trends in Data Analytics
  • Graph Analytics
  • Automated Machine Learning
  • Open Source AI
  • Key Takeaways