How to think like a data scientist
- Created By raju2006
- Last Updated December 26th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
This session addresses these questions among others, it discusses the basics of data science and the Data scientist mindset involved in problem solving using data.
Long Description:
Data scientists are quickly becoming some of the most sought-after professionals. What makes a data scientist’s approach to things so special and why are organizations willing to pay unusually high salaries for their services. What is it about data scientists thinking that is so valuable? What problems lend themselves to data science and how does a data scientist tackle them? This session addresses these questions among others, it discusses the basics of data science and the mindset involved in problem solving using data.
Course Code/Duration:
BDT50 / 2 Days
Learning Objectives:
After this course, you will understand:
- What is Data Science?
- What does a data scientist do exactly?
- Can anyone become a data scientist?
- How does an organization derive value from Data Science?
- What does a Data Science problem look like? (+ examples)
- What tools does a data scientist use to tackle such a problem?
- What Data Science methodologies come into play?
- What questions does a data scientist ask of the data?
- How does a data scientist collaborate with other professionals?
- How does a data scientist communicate the results of a project?
- How does a data scientist evolve as a professional?
- How can one learn more about Data Science work and the Data Science mindset.
- Basic Programming knowledge preferred
- This course is designed for anyone interested to get started with the domain of Machine Learning and Artificial Intelligence including Data Analysts, Data Engineers, DevOps Engineer, Database Professional, Software Engineers, or Quality Assurance Engineers.
- This course is designed for anyone interested to get started with the domain of Machine Learning and Artificial Intelligence including Data Analysts, Data Engineers, DevOps Engineer, Database Professional, Software Engineers, or Quality Assurance Engineers.
Course Outline:
- Course Introduction
- Installing Anaconda
- Overview of Data Science
- The Difference Between Business Analytics (BI), Data Analytics and Data Science
- Data Scientist and other related roles
- The Data Science Process
- Understand the Data Science process to apply to ML use cases
- Understand the relation between Data Engineering and Data Science
- Data Science use cases in Industry
- Identifying a problem and asking good questions
- Data Science Toolkit
- Essential Python Data Science Libraries
- Numpy
- Pandas
- Matplotlib
- Data Exploration
- Describe
- Merging
- Grouping
- Evaluating Features
- Effective communication for Data Scientist
- Advance as Senior Data Scientist
- Do and Don’t for a successful Data Scientist
Structured Activity/Exercises/Case Studies:
- Milestone Project 1: Perform Exploratory Data Analysis
- Milestone Project 2: Apply machine learning algorithms, select and refine the best model.
Training material provided:
Yes (Digital format)
