Data analysis is a topic that is important to everyone in business today. Data analysis is no longer the domain of technical specialists, statisticians, and data scientists. Everyone has access to data today, and we all analyze data as a routine part of our day-to-day jobs. Understanding the basics of analyzing data is important for every business professional today.
Data analysis is much more than applying statistics to data and creating charts and graphs. It is the practice of finding patterns in data and finding meaning in data. Several different data analysis methods and processes exist, each with distinct purpose and application. Underlying all of them, however, are the core activities needed to for data analysis that is purposeful, accurate, meaningful, and valuable: establishing analysis purpose with problem framing, acquiring the right data for analysis, exploring and understanding the data, preparing data for analysis, finding patterns and meaning, data visualization, and communicating findings and conclusions of analysis. Each of these activities has a role in efficiency, effectiveness, and business impact of data analysis. This course examines each of those activities and the skills needed to perform them well.
The course also looks at human factors in data analysis. Cultural influences are discussed a important considerations. Traits of a good analyst – curiosity, imagination, skepticism, and more – are explored along with collaboration and complementary thinking styles. This course provides a comprehensive look at the work of analyzing data. From introductory concepts through all of the technical activities, to the human side of data analysis, you’ll get complete coverage of data analysis fundamentals.
You will learn:
This course is geared towards:
- Data analysis concepts, applications, and processes
- Descriptive and inferential statistics concepts and their applications in data analysis
- Preparing for data analysis – project and problem framing
- Getting the right data for analysis – data searching and data acquisition
- Understanding the data – data exploration with profiling and visualization
- Data cleansing and data structuring
- Data improvement, enrichment, and formatting
- Statistical data analysis techniques
- Algorithmic data analysis techniques
- Data visualization and storytelling
- Data analyst traits and skills
- Business managers who are the de facto data analysts for most organizations
- Business and data analysts whose primary responsibility is finding meaning and insights in data
- Managers of organizations and teams with data analysis responsibilities
- Everyone who uses data for decision making in their day-to-day activities
- Anyone with data analysis responsibilities
- Anyone who has access to data and desire to get more value from the data
- Everyone with an interest in data analysis
Module 0: About the Course (10 mins)
Module 1: Introduction to Data Analysis ( 54 mins)
- What is Data Analysis?
- Kinds of Data Analysis?
Module 2. Statistics and Data Analysis (49 mins)
- Samples and Populations
- Descriptive Statistics
- Inferential Statistics
- A Statistical Problem Example
- Framing a Statistical Problem
- The Descriptive Statistics
- Drawing Inference
Module 3. Project Framing and Data Acquisition (37 mins)
- Project Framing
- Problem Framing
- Searching for Data
- Acquiring Data
Module 4. Data Exploration and Preparation (55 mins)
- Data Exploration – What and Why?
- Exploring with Data Profiling
- Data Profiling Overview
- Exploring with Data Visualization
- Data Cleansing & Structuring
- Data Transformations to Improve, Enrich, & Format
Module 5. Analyzing Data (50 mins)
- Cycles of Data Analysis
- Statistical Data Analysis
- Algorithmic Data Analysis
- Data Visualization
- Data Storytelling
Module 6. Human Factors and Data Analysis (36 mins)
- Data Analysis and Culture
- Data Analyst Traits and Skills
- Data Analysis and Data Literacy
Click –here- to download a more detailed outline of this course.
This exam tests knowledge and understanding of basic concepts, principles, and terminology of analytics.
You will be tested in these areas:
- The purpose and processes of data analysis
- Data patterns and data meaning
- Business applications of data analysis and analytics
- Descriptive statistics
- Inferential statistics
- Project framing and data acquisition
- Data exploration and data preparation
- Data analysis and applied statistics
- Data visualization and storytelling
- The human side of data analysis
Number of Questions: 25
Time Limit: 50 minutes
Passing Score: 70%
Once you pass the exam, you will receive a Certificate of Education documenting that you have demonstrated mastery of the topic. Further, the exam counts towards
Certified Information Management Professional (CIMP) designation in Information Management Foundations.
We recommend that you take detailed notes and review the course material multiple times before taking this exam.
Click here to learn more about CIMP exams.