ABOUT THE COURSE: This curriculum is developed to help participants appreciate data science and train them to perform data science activities Intended audience: B.Com, B.Sc. graduates, B.Tech/B.E graduates, management studies and working professionals Pre-requisites: Some exposure to high school mathematics and knowledge of programming basics (preferred) Learning Objectives: • Introduce Python as a programming language for data science • Introduce the statistical foundations required for data science • Introduce data visualization techniques • Introduce machine learning algorithms Learning Outcomes: • Describe a flow process for data science problems (Remembering) • Classify data science problems into standard typology (Comprehension) • Develop Python codes for data science solutions (Application) • Correlate results to the solution approach followed (Analysis) • Assess the solution approach (Evaluation) Components of the course: • Videos will be released on a weekly basis • Online live discussions addressing the participants’ doubts, will be held at regular intervals • Each module will have an assignment that must be completed to proceed with other modules • Participants can post their queries on a question and answer forum • Final exam: Participants will be assessed through a combination of command-based Python questions, theoretical, interpretation aspects of the course and solving practical capstone case study using Python

ABOUT THE COURSE: This curriculum is developed to help participants appreciate data science and train them to perform data science activities Pre-requisites: Some exposure to high school mathematics and knowledge of programming basics (preferred) Learning Objectives: 1. Introduce Python for advanced machine learning 2. Introduce cutting-edge machine learning algorithms 3. Introduce case studies to provide an application perspective for the techniques learnt Learning Outcomes: • Describe a flow process for data science problems (Remembering) • Classify data science problems into standard typology (Comprehension) • Develop Python codes for data science solutions (Application) • Correlate results to the solution approach followed (Analysis) • Assess the solution approach (Evaluation) Components of the course: • Videos will be released on a weekly basis • Online live discussions addressing the participants’ doubts, will be held at regular intervals • Each module will have an assignment that must be completed to proceed with other modules • Participants can post their queries on a question and answer forum • Final exam: Participants will be assessed through a combination of command-based Python questions, theoretical, interpretation aspects of the course and solving practical capstone case study using Python

ABOUT THE COURSE: This curriculum is developed to help participants appreciate data science and train them to perform data science activities Intended audience: B.Com, B.Sc. graduates, B.Tech/B.E graduates, management studies and working professionals Pre-requisites: Some exposure to high school mathematics and knowledge of programming basics (preferred) Learning Objectives: • Introduce Python as a programming language for data science • Introduce the statistical foundations required for data science • Introduce data visualization techniques • Introduce machine learning algorithms Learning Outcomes: • Describe a flow process for data science problems (Remembering) • Classify data science problems into standard typology (Comprehension) • Develop Python codes for data science solutions (Application) • Correlate results to the solution approach followed (Analysis) • Assess the solution approach (Evaluation) Components of the course: • Videos will be released on a weekly basis • Online live discussions addressing the participants’ doubts, will be held at regular intervals • Each module will have an assignment that must be completed to proceed with other modules • Participants can post their queries on a question and answer forum • Final exam: Participants will be assessed through a combination of command-based Python questions, theoretical, interpretation aspects of the course and solving practical capstone case study using Python

ABOUT THE COURSE: This curriculum is developed to help participants appreciate data science and train them to perform data science activities Pre-requisites: Some exposure to high school mathematics and knowledge of programming basics (preferred) Learning Objectives: 1. Introduce Python for advanced machine learning 2. Introduce cutting-edge machine learning algorithms 3. Introduce case studies to provide an application perspective for the techniques learnt Learning Outcomes: • Describe a flow process for data science problems (Remembering) • Classify data science problems into standard typology (Comprehension) • Develop Python codes for data science solutions (Application) • Correlate results to the solution approach followed (Analysis) • Assess the solution approach (Evaluation) Components of the course: • Videos will be released on a weekly basis • Online live discussions addressing the participants’ doubts, will be held at regular intervals • Each module will have an assignment that must be completed to proceed with other modules • Participants can post their queries on a question and answer forum • Final exam: Participants will be assessed through a combination of command-based Python questions, theoretical, interpretation aspects of the course and solving practical capstone case study using Python

Are you at the right hands in your journey for transforming data to knowledge and stay ahead of your peers? Relax!! You are with the team that has the 1st ever global certification program on Supply Chain Analytics. Skills you gain will help to interpret real-world issues with Analytics mindset. Course info : This course introduces to Analytics in Supply Chain. Suitable for working professionals / Postgraduate / Undergraduate students with or without prior work experience. You’ll learn the opportunities to leverage Analytics • Digital Certificate on completion of all modules • 100% online course • Self-paced learning • Beginner level • Approximately 1 month to complete with 2 hours / week What will you learn?  Supply Chain design for strategic advantage  Components of Supply Chain Analytics  Types of Analytics Skills you will gain  Supply Chain Fundamentals  Supply Chain concepts  Supply Chain cost Faculty profile: https://in.linkedin.com/in/venkateshdubai