Beginning Data Science

Beginning Data Science

What exactly does the term “Data Science” entail? What should I do if I want to have a solid technical understanding without getting too deep into programming? How can I manage a Data Science team without any experience in programming or mathematics?

These are some of the concerns that this course attempts to solve. This 3 day course is designed to give the learner a superficial technical knowledge and skills to tackle data science from a managerial perspective. This is also a stepping course for going deeper into programming for data science.

Course Outcome

The primary focus area of this course will be to prepare the learner for:

• Choosing a Data science team
• Managing a data science team
• Development methodologies
• Understanding of how each tools pertaining to data science works including:
  • Machine learning
  • Artificial Intelligence
  • Big Data
  • Infrastructure
• Basic understanding of Data Science using:
  • KNIME
  • R
• Understanding of technical system requirements and what to look for
• What Big Data actually looks like
• What ML/AI usage and implementation is
• Hands on demo of implementing a Data Science Project

Prerequisites

• PC / Laptop
• Webcam
• Stable internet connection
• Basic understanding of what Data Science is
• Dual Screens
• Ability to install KNIME, R and Oracle VirtualBox

Course Outline

The topics covered may look intimidating, but this course will not go deep into the technicalities. Rather, the objective is to prepare the learner to know what each of the tools and technologies do, how they work, what they look like in actuality and understand the skills needed for undertaking projects that require those technologies.

1. Data
   1. Data Source
   2. Acquisition
   3. Data Preparation
   4. Data Cleaning
   5. Tools
2. Tools
   1. R / Python (either one)
   2. KNIME
   3. Tableau / Power BI (either one)
3. Machine Learning
   1. Models
   2. Implementation
   3. Prediction
   4. Visualization
4. Artificial Intelligence
   1. NLP
   2. Image Processing
5. Big Data
   1. Hadoop
   2. Yarn
   3. Linux
   4. Hive
   5. Kafka
   6. Spark
6. Development
   1. Front end
   2. Backend
   3. Methodologies
   4. Team
   5. Agile
   6. Waterfall
   7. Delivery
   8. Implementation