Data Analytics with Python Course Overview

12 Questions

What type of analytics helps forecast trends based on current events?

Predictive analytics

What is a key reason why Python is chosen for data analysis?

Its simplicity, free and open-source nature, extensive library, and usability

What is the primary focus of data scientists?

Developing advanced algorithms and machine learning to create data products

What is the purpose of understanding different types of data measurement?

To choose the appropriate analytical tools for analysis

What type of analytics examines why certain events happened?

Diagnostic analytics

What skills are required to become a data analyst?

Knowledge of mathematics, technology, and business/strategy acumen

What is the main focus of the Course on Data Analytics with Python by Prof.Ramesh Anbanandam?

Practical examples

Which of the following is not a learning objective of the Course on Data Analytics with Python?

Learning about the history of data analytics

What programming language is used for practical applications in the Course on Data Analytics with Python?

Python

Which level of data is not covered in the Course on Data Analytics with Python?

Boolean

What is the main difference between data analysis and data analytics?

Data analysis focuses on past events, while data analytics focuses on future events

Who teaches the Course on Data Analytics with Python?

Prof.Ramesh Anbanandam

Study Notes

Course on Data Analytics with Python by Prof. Ramesh Anbanandam at IIT Roorkee focuses on practical examples over repetitive software use, aiming to make students comfortable with analytics in their careers and personal lives.
The course emphasizes the importance of choosing the right methodology and understanding the fundamental concepts behind data analytics tools.
Learning objectives include defining data and its importance, understanding data analytics types, explaining the significance of analytics in today's business environment, and clarifying the interrelation between statistics, analytics, and data science.
The course utilizes Python for practical applications and covers the four levels of data: nominal, ordinal, interval, and ratio.
Variables are characteristics of entities capable of taking different values, measurements assign numbers to variable attributes, and data are recorded measurements.
Data can be generated by humans, machines, or their combination, and it adds value to businesses by facilitating the development of data products like recommendation engines and insights.
Data is crucial for making better decisions, identifying reasons for underperformance, benchmarking performance, improving operations, and understanding consumers and markets.- Data analysis involves studying past events to understand why and how they happened, while data analytics focuses on predicting future events based on past data.
Descriptive analytics provides a summary view of facts and figures, while diagnostic analytics examines why certain events happened.
Predictive analytics helps forecast trends based on current events, using techniques like regression analysis.
Prescriptive analytics indicates the best course of action to optimize outcomes, utilizing tools like optimization models and decision analysis.
The demand for data analysts and data scientists is increasing, with many companies looking for qualified individuals in this field.
Skills required to become a data analyst include knowledge of mathematics, technology, and business/strategy acumen.
Data analysts focus on analyzing data within a specific business domain, while data scientists work on advanced algorithms and machine learning to create data products.
Python is chosen for data analysis due to its simplicity, free and open-source nature, extensive library, and usability across various applications including data science, machine learning, and artificial intelligence.
Different levels of data measurement include nominal, ordinal, interval, and ratio scales, each with specific characteristics and usefulness in data analysis.
Understanding the different types of data measurement helps in choosing the appropriate analytical tools for analysis, whether parametric or non-parametric tests.