Introduction to Data Analytics Course for Beginners

Course Content

Introduction to Data Analytics

• Lesson 01 Course Introduction
  • Course Introduction
• Lesson 2 - Data Analytics Overview
  • 2.01 Introduction
  • 2.02 Data Analytics - Importance
  • 2.03 Digital Analytics: Impact on Accounting
  • 2.04 Data Analytics Overview
  • 2.05 Types of Data Analytics
  • 2.06 Descriptive Analytics
  • 2.07 Diagnostic Analytics
  • 2.08 Predictive Analytics
  • 2.09 Prescriptive Analytics
  • 2.10 Data Analytics - Amazon Example
  • 2.11 Data Analytics Benefits Decision-Making
  • 2.12 Data Analytics Benefits: Cost Reduction
  • 2.13 Data Analytics Benefits: Amazon Example
  • 2.14 Data Analytics: Other Benefits
  • 2.15 Key Takeaways
• Lesson 3 - Dealing with Different Types of Data
  • 3.1 Introduction
  • 3.2 Terminologies in Data Analytics - Part One
  • 3.3 Terminologies in Data Analytics - Part Two
  • 3.4 Types of Data
  • 3.5 Qualitative and Quantitative Data
  • 3.6 Data Levels of Measurement
  • 3.7 Normal Distribution of Data
  • 3.8 Statistical Parameters
  • 3.09 Key Takeaways
• Lesson 4 - Data Visualization for Decision making
  • 4.1 Introduction
  • 4.2 Data Visualization
  • 4.3 Understanding Data Visualization
  • 4.4 Commonly Used Visualizations
  • 4.5 Frequency Distribution Plot
  • 4.6 Swarm Plot
  • 4.7 Importance of Data Visualization
  • 4.8 Data Visualization Tools - Part One
  • 4.9 Data Visualization Tools - Part Two
  • 4.10 Languages and Libraries in Data Visualization
  • 4.11 Dashboard Based Visualization
  • 4.12 BI and Visualization Trends
  • 4.13 BI Software Challenges
  • 4.14 Key Takeaways
• Lesson 5 - Data Science, Data Analytics, and Machine Learning
  • 5.01 Introduction
  • 5.02 The Data Science Domain
  • 5.03 Data Science, Data Analytics, and Machine Learning - Overlaps
  • 5.04 Data Science Demystified
  • 5.05 Data Science and Business Strategy
  • 5.06 Successful Companies Using Data Science
  • 5.7 Travel Industry
  • 5.8 Retail
  • 5.09 E-commerce and Crime agencies
  • 5.10 Analytical Platforms across Industries
  • 5.11 Key Takeaways
• Lesson 6 - Data Science Methodology
  • 6.01 Introduction
  • 6.02 Data Science Methodology
  • 6.03 From Business Understanding to Analytic Approach
  • 6.04 From Requirements to Collection
  • 6.05 From Understanding to Preparation
  • 6.06 From Modeling to Evaluation
  • 6.07 From Deployment to Feedback
  • 6.08 Key Takeaways
• Lesson 7 - Data Analytics in Different Sectors
  • 7.01 Introduction
  • 7.02 Analytics for Products or Services
  • 7.03 How Google Uses Analytics
  • 7.4 How LinkedIn Uses Analytics
  • 7.05 How Amazon Uses Analytics
  • 7.6 Netflix- Using Analytics to Drive Engagement
  • 7.7 Netflix- Using Analytics to Drive Success
  • 7.08 Media and Entertainment Industry
  • 7.09 Education Industry
  • 7.10 Healthcare Industry
  • 7.11 Government
  • 7.12 Weather Forecasting
  • 7.13 Key Takeaways
• Lesson 8 - Analytics Framework and Latest trends
  • 8.1 Introduction
  • 8.2 Case Study: EY
  • 8.3 Customer Analytics Framework
  • 8.4 Data Understanding
  • 8.5 Data Preparation
  • 8.6 Modeling
  • 8.7 Model Monitoring
  • 8.8 Latest Trends in Data Analytics
  • 8.9 Graph Analytics
  • 8.10 Automated Machine Learning
  • 8.11 Open Source AI
  • 8.12 Key Takeaways

What do I need to do to unlock my certificate?

To obtain the Introduction to Data Analytics course certification, you must:

• Complete the online self-learning course, and
• Complete the course-end assessment with a minimum score of 80%

FAQs

• What is Data Analytics?

  Data analytics is one of the major areas to explore in this digitalized world. To learn data analytics, you need to have an in-depth understanding of SQL, Python or R programming, data visualization techniques, statistics, regression, and more. While you can get started on your own, it is recommended to take this training program and learn data analytics from industry experts. You’ll get up-to-date knowledge of the field and know about the strategies and best practices that are actually followed by companies.

• What is Data Analysis?

  Data analysis is a process of inspecting, cleaning, transforming and modeling data to discover useful information and support decision making to achieve business goals. There are various data analysis qualitative and quantitative methods and analytical or statistical tools used to extract the useful information and translate them into insights to make better business decisions,most of which are covered in this program.

• Data Analysis vs Data Analytics

  Data analysis is the process of cleaning, transforming, and modeling data to find meaningful insights and make better decisions. Data analytics is a broad term that involves many diverse types of data analysis.

• What are the different types of Data Analytics?

  There are four different types of data analytics:

  • Descriptive
  • Diagnostic
  • Predictive
  • Prescriptive