Three Pillars of Data Science: Big Data Analytics, Data Management & Data Storytelling with the Rock Stars of Data (3 Day Training)
To find out more about future scheduled public learning sessions like this one on July 11-13, 2017 in Ortigas or to set-up an in-house training, send an e-mail to firstname.lastname@example.org
Learning Session Description
Everyone is rushing to hire Data Scientists and build Data Science Teams. However, many businesses in the Philippines are finding out that this is easier said than done.
According to Big Data Expert Bernard Marr, “To build a strong, successful big data and Data Science team, organizations should focus less on finding candidates who are “perfect” on paper and direct their efforts more toward putting together a group that is greater than the mere sum of its parts.”
A Data Science team, carefully constructed with the right set of dedicated professionals, can prove to be an asset to any organization. It’s a fact that success of any project is dictated by the expertise of its resources and data science is no exception to this golden rule of thumb. Professionals with diversified skill-sets are required to successfully negotiate the challenges of a complex big data project.
For your Data Science project to be on the right track, you need to ensure that the team has skilled professionals capable of playing several essential roles including a business analyst, a date warehouse expert and a data storyteller. The presence of these three experts, working together for a common goal, will result in the ability for your business to handle a variety of big data challenges.
This seminar will involve hands-on use of common big data analytics tools such as Excel, Tableau and SQL. Having at least a basic understanding of data science will be helpful in order for attendees to get the most value out of the 3 day training.
Learning Session Objectives
- Apply Cutting Edge Technologies to Organize, Interpret, and Summarize Big Data in Your Business
- Understand the components of The Data Value Chain 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
- Develop a Deeper Understanding of How Best To Deploy Analytics and Data Science Techniques Across an Organization
In this session, your organization will be able to use:
- A framework for asking the right questions, allowing the ability to link analytics to business strategy
- Specific skills to effectively frame the problem you’re addressing to uncover key opportunities and drive growth
- How to simply and quickly amplify decision making by separating the signal from the noise
- Guidelines on best practices for linking visuals together into a cohesive story that concludes with actionable recommendations
- Applied examples regarding trend analysis, sampling, confounding variables and the statistical aspects behind data storytelling
In this session, your participants will be able to:
- Learn the best practices for organizing, summarizing, and interpreting data to bring out patterns in data that were not apparent at first glance
- Create complete data stories and know how to present these stories in an engaging way to upper management
- Generate statistically robust data driven insights that inform decision making in the business setting
- Build stakeholder support for project initiatives using data
- Know how to select appropriate chart types for a given dataset (e.g. When to use a bar chart versus a pie chart, a line graph versus a bubble chart)
Who Should Attend
- Business professionals who are involved in day-to-day analysis of data.
- 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.
- Executives and managers who want to create more engaging and impressive presentations with Excel and PowerPoint
- Professionals already working in analyst positions, including (but not limited to) positions involved with data preparation, data analytics, digital and marketing analytics, customer and market analysis
Day One: Big Data Analytics and the Business Analyst
Definition of Business Analyst. This role existed long before big data, and people in this role continue to perform an important function. They have intimate knowledge of your industry and your company, and analyze business-level data to produce actionable insights. Business analysts work with front-end BI tools related to the core business and interact with the higher management of an organization. They further analyze business-level data provided by the data storytelling experts to find out insights related to the organization’s core business interests.
Section One – Effectively Framing Big Data Projects
- Learn How to Successfully Define Business Questions,
- Understand How to Write Effective Business Requirements,
- Uncover the Most Important KPIs in Your Business,
- Optimize Your Use of MS Excel For Big Data Analytics,
- Learn How to Build Better Management Reports.
Section Two – 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 Visual to See All the Big Data in Your Business,
- Recognize When to Employ Descriptive, Predictive or Prescriptive Analytics,
- Establish Clear Objectives for Big Data Projects
Section Three – Empowering Data- Driven Decision-Making
- Articulate the Importance of Accurately Interpreting Data,
- Understand how to Mitigate and Analyze Risk, Uncertainty, And Probability,
- Deliver Findings from Big Data to Drive Decisions Within Your Organization.
- Apply a Process to Present Big Data Clearly to Management
- Keys to Empowering Others with Your Business Knowledge
Section Four – Using Business Intelligence Tools
- An Overview of BI Tools used for Big Data Projects,
- Tableau Public Demonstration,
- Discuss the Concepts of Data Visualization and Data Storytelling,
- Build A Business Dashboard Prototype,
- Tips on How to Make Your Data and Analysis More Enchanting
Day Two – Engineering Data with a Data Warehouse Master
Definition of Data Engineer. The data engineer is concerned with the capture, storage, and processing of the data itself. The role of a data engineer is at the base of the pyramid. Data engineers constitute the foundation of a data science project and they are trusted with the responsibility of capturing, storing and processing the relevant data. Data Collection, Data Warehousing, Data Transformation and Data Analysis – these are typically handled by a team of data engineers. They ensure that all the raw data points are captured and processed correctly. The processed data is then handed over to the next group of people, the machine learning experts, for taking it further.
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.
Day Three – Data Storytelling with a Data Science Expert
Definition of a Data Storyteller. This person is 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.
Section Nine – An Introduction to Data Storytelling
- Knowing your audience
- Preparing your data
- Choosing the right visual
- Telling the story
Section Ten – 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)
Section Elven – Technical Differences Between Data Storytelling vs. Data Visualization
- Trustworthy Data Management
- Organized Data Structure
- The Right Data Choice
- Appropriate Data Visuals
Section Twelve – Adding the Narrative to Your Data Story
- Setting – Where and When the data takes place?
- Plot – Why the data is important?
- Characters – Who is impacted by the data?
- Conflict – What does problem does the data solve?
- Theme – What is the ultimate takeaway?
Minimum Hardware and Software Requirements
- Laptop with Intel Core i3 and 4GB RAM.
- Windows OS with Excel 2007 or greater.
- ODBC and database connections will be provided during class.
Learning Investment for 3-day Intensive Training:
P 18,000.00 + VAT
(Pay the full amount on or before June 10, 2017)
Group Rate (Minimum of 5)
P19,500.00 + VAT
P 21,900.00 + Vat
(starting June 11, 2017)
All investments includes: 3-day Data Science Training with the most in-demand Analytics and Data Science experts in the Philippines, complete with Training Materials, AM/PM Snacks, Lunch, Venue and Certificates.
Case Studies and Exercises
Case studies and group exercises will be used throughout the three-day class. In these activities, the group is divided into teams. Each team will analyze datasets using the principals learned in the various learning sessions. These exercises will also use elements from the case studies as we progress from finding data, to conducting analysis on the data and finally presenting the data.
Analytics and Data Science Training – DMAIPH offers a wide range of analytics centric training solutions for professionals and students via public, in-house, on-site, and academic settings. We tailor each training event to meet the unique needs of the audience. If you need empowerment and skills enhancement to optimize the use of analytics in your organization, we are here to help. Contact DMAIPH now at email@example.com or connect with me directly to set up a free consultation to learn which of our DMAIPH analytics training solutions is best for you.