Fundamentals of Data Science
- Created By raju2006
- Last Updated December 20th, 2023
- Overview
- Prerequisite
- Audience
- Audience
- Curriculum
Description:
This course provides a thorough introduction to data science. You will have the opportunity to explore real-world applications and understand why data science has become such an integral part of business and academia. We will discuss the data science process and the various tools used to perform data exploration, analysis, and modeling.
Long Description:
Embark on your journey into the fundamentals of data science with our comprehensive training. Explore real-world applications that highlight the critical role data science plays in both business and academia. Delve into the data science process and discover the essential tools used for data exploration, analysis, and modeling. Kickstart your learning journey by installing Anaconda, empowering you with Python and R programming languages. Through hands-on exercises, you'll gain practical insights into data science, including data exploration, analysis, modeling, and visualization. Uncover the significance of data science across various industries. Join us and master the "Fundamentals of Data Science" today.
Course Code/Duration:
BDT2 / 1 Day
Learning Objectives:
After this course, you will be able to:
- Install Anaconda on personal computer.
- Understand the Data Science Field.
- Become familiar with Descriptive and Inferential Statistics and statistical analysis.
- Learn primary tools used for data science in Python including Pandasand Scikit-learn.
- Learn how to perform exploratory data analysis.
- Learn the importance of data cleaning.
- Utilize common Machine Learning algorithms such as Linear and Logistic Regression.
- Solidify understanding by completing hands-on exercises and milestones.
- Walk through 2 data science projects.
- Understand the big picture and the importance of data science in learning from data.
- Basic Programming Knowledge
- Developers, Business Analysts who want to start a career in or want to learn about the exciting domain of Data Science and Machine Learning, Non-technical professionals who want to start a career in Machine Learning.
- Developers, Business Analysts who want to start a career in or want to learn about the exciting domain of Data Science and Machine Learning, Non-technical professionals who want to start a career in Machine Learning.
Course Outline:
- Course Introduction
- Install Anaconda
- Review the Essentials of Python
- Overview of Data Science
- The Difference Between Business Analytics (BI), Data Analytics and Data Science
- Descriptive Statistics Fundamentals
- Central Tendency
- Mean
- Median
- Mode
- Spread of the Data
- Variance
- Standard Deviation
- Range
- Relative Standing
- Percentile
- Quartile
- Inter-quartile Range
- Inferential Statistics Fundamentals
- Data Distributions
- Normal Distribution
- Uniform Distribution
- The Data Science Process
- Define the Problem
- Get the Data
- Explore the Data
- Clean the Data
- Model the Data
- Communicate the Findings
- Feature Selection
- Data Cleaning
- Dropping Rows
- Imputing Missing Values
- Data Transformation
- Binary Encoding
- One-Hot Encoding
- Standardization
- Normalization
- Machine Learning Overview
- Introduction to Pandas
- Milestone 1: Use Pandas to perform data analysis on a real-world dataset.
- Introduction to Convolutional Neural Networks (CNN)
- Data Exploration
- Describe
- Merge
- Group
- Feature Evaluation
- Feature Engineering
- Milestone 2: Perform exploratory data analysis and feature engineering
- Test/Train Split
- Model Training
- Basic Machine Learning Implementation
- Linear Regression
- Logistic Regression
- Support Vector Machine
- Decision TreeBasic Machine Learning Implementation
- Milestone 3: Perform an end-to-end project of the data science process.
- Conclusion: Next steps.
Structured Activity/Exercises/Case Studies:
- Milestone Project 1: Use Pandas to perform data analysis on a real-world dataset.
- Milestone Project 2: Perform exploratory data analysis and feature engineering
- Milestone Project 3: Perform an end-to-end project of the data science process.
Training material provided:
Yes (Digital format)
