Data Analytics 3.0: Big Data Analytics, Data Value chain and Data Visualization on June 5-7, 2018 (9AM – 5PM) to be held at Discovery Suites, ADB Ave., Ortigas Center, Pasig.
Attendees will learn how to identify the right data, how data can be efficiently stored, then transformed into a friendly form for analysis, and finally how data analysis can yield insights.
This training will also lightly touch on each aspect of data identification, collection, storage, transformation, and analysis and involve hands-on use of common data management and analysis tools such as Excel, SQL and in depth learning of the tool “Tableau”, this is also designed for those with little to no prior experience with these tools.
LEARNING SESSION OBJECTIVES:
- Apply cutting edge technologies to organize, interpret, and summarize Big Data in your business.
- Create a process to analyze data and identify patterns not apparent at first glance
- Understand the components of The Data Value Chain: Ingestion, Storage, Transformation, Analysis – and how they are all important to deriving value from data.
- Learn database manipulation and processing basics using the Structured Query Language (SQL)
- Connect a data analysis tool such as MS Excel or Tableau to a database to be able to perform analysis on processed and stored data
IN THIS SESSION, YOUR ORGANIZATION WILL BE ABLE TO USE:
- Specific skills to effectively frame the problem you’re addressing to uncover key opportunities and drive growth
- Critical marketing steps of orientation necessary before engaging tools and technology
- How to simply and quickly amplify decision making by separating the signal from the noise
- A framework for asking the right questions, allowing the ability to link analytics to business strategy
IN THIS SESSION, YOUR PARTICIPANTS WILL BE ABLE TO:
- Learn the best practices for organizing, summarizing, and interpreting quantitative data
- Create a repeatable process for analyzing your data
- Shorten the time between analysis and action to avoid “analysis paralysis”
- Know how to get from hard data to well-reasoned conclusions
WHO SHOULD ATTEND:
- Business Analysts, Data Analysts and other Analytics Professionals
- Business professionals who are involved in day-to-day analysis of data.
- Data analysts who are already performing analysis using spreadsheets but struggle with manual data processing.
- Managers of analysts or staff who spend a significant amount of their time collecting, analyzing and reporting data.
- IT and Development Staff that work closely with business leaders and decision-makers.
KEY TOPICS: DAY ONE
SECTION ONE – Big Data—It’s Not Just Size That Matters
- Understand the 3 T’s of Analytics: Talent, Technique and Technology.
- Describe the importance of effectively, analyzing big data in Business today.
- Develop a Data Map to analyze the Big Data in your Business.
- Recognize when to employ Descriptive, Predictive or Prescriptive Analytics.
- Establish clear objectives when analyzing Big Data.
SECTION TWO – Assess Your Current Analytics Culture
- Define What Is an Analytics Centric Culture.
- Describe the issues and trends in today’s analytics field.
- Discover how to find the most important KPIs.
- Learn how to build better management reports.
- Optimize your use of MS Excel for Big Data Analytics
SECTION THREE – Using Business Intelligence Tools
- An overview of BI Tools.
- Discuss the Concept of Data Visualization.
- Build A Business Dashboard Prototype.
- Apply the key elements of Data Storytelling
SECTION FOUR – Interpreting Your Data and Analysis
- Articulate the importance of accurately interpreting Data.
- Determine how to validate your data analysis.
- Mitigate and analyze Risk, Uncertainty, And Probability.
- Spot patterns and trends through Statistical Analysis.
- Use findings from Big Data to Drive Decisions within your Organization.
KEY TOPICS: DAY TWO
SECTION FIVE – Presenting the Data Value Chain and Databases
- Discuss the components of The Data Value Chain and the various users and roles involved in transforming data to value: Database and ETL engineers, Data analysts, Business users.
- Learn about basic data architecture and the role of databases in processing data.
- Understand the basics of databases, tables and views.
- Learn about the Structured Query Language (SQL) and SELECT statements.
SECTION SIX – Data Processing with SQL
- Discuss the additional value that can be derived from using SQL for Data Processing.
- Go into detail on various ways of processing and preparing data using SQL.
- Learn about aggregates, conditions, how to join tables, and run queries within queries.
SECTION SEVEN – Accessing SQL Tables with Excel
- Learn about Open Database Connectivity (ODBC) and how Excel uses ODBC to connect to external data sources.
- Discover how SQL tables and views can be read by Excel into instant Pivot Tables and Pivot Graphs.
- Understand how changes in database table or view via SQL Inserts, Deletes, and Updates are reflected on Excel.
SECTION EIGHT – Performing analysis of SQL-based data using Excel
- Learn about how SQL data can be dissected using the Data Analysis functions in Excel.
- Talk about form tools and macros that can automate manual reporting.
- Discuss tips for reporting and sharing the results of your analysis.
- Understand how data can be prepared for exceptional storytelling.
KEY TOPICS: DAY THREE
At the end of this course, you will be able to:
- Connect to your data.
- Edit and save a data source.
- Understand Tableau terminology.
- Use the Tableau interface / paradigm to effectively create powerful visualizations.
- Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations.
- Represent your data using the following visualization types:
- Cross tabs
- Geographic maps
- Heat maps
- Tree maps
- Pie charts and bar charts
- Dual axis and combined charts with different mark types
- Highlight Tables
- Scatter Plots
- Build dashboards to share visualizations.
Tableau – Fundamentals
- Connecting to data
- Simplifying and sorting your data
- Organizing your data
- Slicing your data by date
- Using multiple measures in a view
- Showing the relationship between numerical values
- Mapping data geographically
- Viewing specific values
- Customizing your data
- Analyzing data with quick table calculations
- Showing breakdowns of the whole
- Highlighting data with reference lines
- Making your views available
Definition of a Data Storyteller. These experts are equally statistically minded and artistically gifted, with experience in programming and building data models that can be visualized and turned into data stories. Data Storytellers must have excellent communication and presentation skills that’s are used in translating data analyses into layman’s terms in order to influence a business decision or action. Their job is to ensure that the derived information is not only well researched and accurate, but also understandable and easily used to explain what the data says in a compelling way.
Overview of the Four Keys to Data Storytelling
- Knowing your audience
- Preparing your data
- Choosing the right visual and designing it well
- Telling the story
The Key Elements (D.R.A.P.S.) of Data Storytelling
- Visualization is the fastest bandwidth channel for transferring high dimensional information into the human brain (Direct)
- Visualization uncovers cause and effect relationships (Relationships)
- Visualization grabs attention (Attention)
- Visualization uncovers hidden patterns (Patterns)
- Visualization separates data structure from data noise (Structure)