Why Attend this Course?
- What can I do as a business analyst and when do I need to start learning specialized data analytics skills?
- This course examines several depictions of data analytics and defines boundaries that can define the need for specialized skills
- Many analysts start their analysis by opening EXCEL.
- There is a process that should be followed for data analytics projects. This will be introduced and utilized during class.
- Recommendations are typically buried in misunderstood charts and tables upon tables of data.
- This course introduces four principles for data visualizations – graphics that help find the unexpected and communicate data as insights.
What Makes this Course Stand Apart?
- The course is constructed around common data analytics competencies.
- A case study approach links together the various topics discussed.
- Hands-on exercises challenge participants with issues faced during typical analytics projects.
- Covers the very latest ‘thinking’ in data analytics.
What You Will Learn
Upon completion of this course you will be able to:
- Distinguish between different types of data analytics projects.
- Understand the key challenges of data analytics projects.
- Define the key attributes of companies that compete on analytics.
- Use a portfolio of examples to recognize opportunities within your company.
- Critique visualizations as a means of improving their communication abilities.
- Business Analyst (IT and non-IT)
- Data Quality Analyst
- Database Administrators
- Project Leaders
- Systems Analyst
- Data Analyst
The structure of the courses assumes no prior experience in statistics or data analytics.
Outline for Fundamentals of Data Analytics
1. Big Data
- Big Data/Social Media Data/Industrial Data
- Volume, Velocity, Variety & Variability
- Challenges & Opportunities with Big Data
2. Data Analytics
- Types of Analytics Projects with real world examples
- Common Competencies and Skills
- A Process for Analytics
- Common Challenges
- Data Mining Techniques (An Overview)
3. Data Visualization
- Pre-attentive Processing
- Data Types and Visual Attributes
- Critiquing Visualizations for Improvement