Data Analytics Workshop

This deck was used at the AIESEC Port Harcourt EdTech free virtual event for youths and young adult across Port Harcourt city, Nigeria. AIESEC is a global platform for young people to explore and develop their leadership potential.

Content.

During the workshop, I introduced participants to data analysis and how it fits to the broader role of data science. We talked about;
- The components that make up the field of data science.
- The 3 arcs of data science in data engineering, data analytics, and machine learning.
- The stages of data analytics.
- An example of a data science process in an organization; where we went through a standard movement of data from source to action or decision-making.
- Traditional analytics tools.
- We saw various data analytics tools in action such as; Microsoft Excel, Google Analytics, Tableau, Data Studio, and using Python programming.
- The hierarchy of skills required for data analysis.
- Flavours of data analytics.
- Next steps and where to get started.

Transcript

1. Data Analytics Workshop
2. Agenda Introduction to Data Analytics Data Science. Components of Data Science 3 Arcs of Data Science. See Data Analytics in Action. (Workshop.) Stages of Data Analytics. Where to go from here? Example of a Data Analytics Process in an Organization. Different Flavours of Data Analytics. Data Analytics Tools. Skills needed to become a Data Analytics.
3. A Little Bit About Myself... Stephen Oladele - Data Science Consultant. - Technical Reviewer, Packt Publishing Co. - Lead Volunteer, Port Harcourt School Of AI.
4. You take lessons from past experiences and make future or present deductions based on the lessons. Introduction to Data Analytics Data Science. Data Science is something you already do. Before talking about Analytics, let’s discuss Data Science.
5. Components of Data Science. Data Science/ Statistics Technology = Insights for decision-making. + + Data Science is the extraction of insights from data through the use of computer algorithms and other scientific methods with the end goal of making reasonable deductions, conclusions, and/or effective predictions.
6. 3 Arcs of Data Science Data Analytics Machine Learning Data Engineering
7. Stages of Data Analytics Analytics Stages Questions to Ask • Descriptive • What happened? • Exploratory • What is going on? • Explanatory • Why did it happen? • Predictive • What will happen? • Prescriptive • How do I take advantage? • Experimental • How well will it work?
8. Example of a Data Analytics Process in an Organization.
9. • Descriptive • Exploratory • Exploratory Predictive Prescriptive Prescriptive Experimental Experimental Action/Dec. Data Source from website. Data Source from social media. Data Source from mobile app. Data Warehouse. Extract Transform Load pipeline or ETL pipeline.
10. Traditional Analytics Tools • TABLEAU/Power BI • MICROSOFT EXCEL • SAS • IBM SPSS • QLIK • Python/R • SQL • And so on...
11. Data Analytics in Action
12. Skills required to be a Data Analyst • Statistics ◦ Descriptive and inferential stats. • Data Understanding ◦ Data Ethics and Security • MICROSOFT EXCEL • Python • SQL • And THEN Other Useful Tools.
13. Flavours of Data Analytics • Pricing Analytics • Social Media Analytics • Business Analytics • People Analytics • Learning Analytics • Real-Estate Analytics
14. Where to go from here? 1. Data Science & Analytics Career Paths & Certifications: First Steps (LinkedIn Learning) 2. Excel Fundamentals for Data Analysis (Coursera)
15. QUESTIONS?
16. THANKS FOR ATTENDING! https://www.linkedin.com/in/ stephenoladele/ @nerdCyberArtist Links to presentation, and profile;