PPT - Python for Data Science.pptx
- 1. THE FUTURE STARTS HERE PYTHON FOR DATA SCIENCE BRIDGING DIMENSIONS CONVERT RAW DATA INTO ACTIONABLE INSIGHTS SUPPORTED BY
- 2. WHY LEARN DATA SCIENCE? • The demand for data science professionals is at an all-time high, and the supply hasn’t been able to catch up, leading to an acute shortage of talent • As more and more companies move to the cloud, the need for data science and engineering talent will further increase • Rapid growth and demand in digital transformation and advanced analytics have led to a surge in demand for data science professionals in key technology areas such as artificial intelligence
- 4. HOW MUCH DATA IS GENERATED? Data is being created all the time without us even noticing it. Much of what we do every day now happens in the digital realm, leaving an ever- increasing digital trail that can be measured and analyzed. Netflix Subscribers Stream 77,160 Hours of Video UBER Passengers take 694 Rides Facebook Users Like 4,166,667 Posts Twitter Users Send 347,222 Tweets YouTube Users Upload 300 Hours of new video Instagram Users Like 1,736,111 Photos Pinterest Users Pin 9,722 Images Apple Users Download 51,000 Apps Reddit Users CAst 18,327 Votes Amazon Receives 4,310 Unique Visitors Vine Users Play 1,041,666 Videos Tinder Users Swipe 590,278 Times Snapchat Users Share 284,722 Snaps Buzzfeed Users View 34,150 Videos Skype Users Make 110,040 Calls Every Minute of the Day
- 5. THERE IS A RISING DEMAND FOR DATA ANALYTICS PROFESSIONALS IN SINGAPORE
- 8. PYTHON FOR DATA SCIENCE Assessment 1 Hour 33 Hours 6 Hours F2F lectures E-learning Quizzes and Exercises
- 9. PROGRAM OVERVIEW LU1 Analyse Business Objectives LU2 Design, Implement and Debug LU3 Compose Solutions based on Business Objective
- 10. TOPICS COVERED Computational problem-solving using python. Data and expressions, arithmetic operators. 01 Iterative control (while/for statements), and input error checking. 03 Control structures, including relational, membership and Boolean operators Selection control (if statements) 02 Concept of objects in programming. functions & Lists Object Oriented Programming 04 Modules and modular design - Developing a mini project application in python 05
- 11. LEARNING OUTCOME Python for Data Science Evaluate and choose the best display types, visualization tools and techniques to identify the story in data. 01 Introduction to Data Visualization 03 Configuring Data Environment 02 Types of Data and Displays 04
- 12. LEARNING OUTCOME Python for Data Science PRACTICAL OUTCOME BASED ON INDUSTRY REQUIREMENTS LO1 Analyse business objectives to identify relevant information and select appropriate programming language to solve the problem. LO2 Design, implement and debug simple program by implementing the necessary algorithmic constructs and techniques LO3 Compose and articulate programming solutions based on a business objective.
- 13. THE FUTURE STARTS HERE PYTHON FOR DATA SCIENCE Thank You for taking the First Step. Convert raw data into actionable information SUPPORTED BY THANK YOU