Introduction:
Data analytics is the practice of gathering and processing data in order to extract actionable information to help you make informed decisions. Organizations of all types are more data driven today than ever before, and the need for data analysts grows with it. Data analytics courses help you gain the information and skills you need for this exciting field.
- Are you aspiring to delve into the Data Analytics domain?
- Are you a Business Analyst with a need to upskill in Analytics?
- Are you a Financial Analyst looking at pivoting into Analytics?
- Do you use data at your place of work and want to upskill?
- Do you want to learn how to transform raw data into actionable insights?
- Are you a tech professional that is required to create compelling stories?
- Are you a newcomer or aspiring professional wishing to pivot into tech?
Course Modules:
1.Introduction to Data Analytics
1.1 What is Data Analytics
1.2 Importance of Data Analytics
1.3 Key Components of Data Analytics.
1.4 How Autonomous Vehicles use it.
2. Types of Data Analytics
2.1 Descriptive Data Analytics
2.2 Predictive Data Analytics
2.3 Diagnostic Analytics
2.4 Prescriptive Analytics
3. Phases of Data Analytics – Data Sourcing
3.1 Defining the Research Objective
3.2 Identifying the Data needed
3.3 Link research Questions to Business Goals
3.4 Case Study
4. Phases of Data Analytics – Data Analysis
41. Data Processing
4.2 Data Exploration
4.3 Performing Data Analysis
4.4 Data Analysis Techniques
5. Data Interpretation and Reporting
5.1 Creating Effective Data Report
5.2 Validate Needs of Stakeholders
5.3 Interpretation and Insights
5.4 Communicating Results
6.Data Visualization
6.1 Visualization Techniques
6.2 Visualization tools overview
6.3 Visualization best practices
6.4 Case Studies
7. Data to aid Decision-making
7.1 Leveraging data driven insights
7.2 Recommend Actions.
7.3 Data Strategy.
8. Final Assessment.
By the end of this course, participants will break away from the spreadsheet by developing a foundational understanding of data science tools, processes, and models.
Use business analytics and data science to make better decisions that lead to organizational success.
Identify and avoid common mistakes while interpreting datasets, metrics, and visualizations.
Create a data-driven framework for your organization and yourself; develop hypotheses and insights; and identify data and missing components.
