Prerequisites
- Basic Understanding of Computer Science: Familiarity with programming and statistics (beneficial but not mandatory).
- Interest in Data Analytics: A keen passion for analyzing data trends and solving real-world problems.
- Willingness to Learn Python and R: Basic programming skills help, but the program is designed to support beginners.
Course outline
- 1
Lesson 1: Foundations of Data Science
- 1.1 Introduction to Data Science
- 1.2 Data Science Life Cycle
- 1.3 Applications of Data Science
- 2
Lesson 2: Foundations of Statistics
- 2.1 Basic Concepts of Statistics
- 2.2 Probability Theory
- 2.3 Statistical Inference
- 3
Lesson 3: Data Sources and Types
- 3.1 Types of Data
- 3.2 Data Sources
- 3.3 Data Storage Technologies
- 4
Lesson 4: Programming Skills for Data Science
- 4.1 Introduction to Python for Data Science
- 4.2 Introduction to R for Data Science
- 5
Lesson 5: Data Wrangling and Preprocessing
- 5.1 Data Imputation Techniques
- 5.2 Handling Outliers and Data Transformation
- 6
Lesson 6: Exploratory Data Analysis (EDA)
- 6.1 Introduction to EDA
- 6.2 Data Visualization
- 7
Lesson 7: Generative AI Tools for Deriving Insights
- 7.1 Introduction to Generative AI Tools
- 7.2 Applications of Generative AI
- 8
Lesson 8: Machine Learning
- 8.1 Introduction to Supervised Learning Algorithms
- 8.2 Introduction to Unsupervised Learning
- 8.3 Different Algorithms for Clustering
- 8.4 Association Rule Learning with Implementation
- 9
Lesson 9: Advance Machine Learning
- 9.1 Ensemble Learning Techniques
- 9.2 Dimensionality Reduction
- 9.3 Advanced Optimization Techniques
- 10
Lesson 10: Data-Driven Decision-Making
- 10.1 Introduction to Data-Driven Decision Making
- 10.2 Open Source Tools for Data-Driven Decision Making
- 10.3 Deriving Data-Driven Insights from Sales Dataset
- 11
Lesson 11: Data Storytelling
- 11.1 Understanding the Power of Data Storytelling
- 11.2 Identifying Use Cases and Business Relevance
- 11.3 Crafting Compelling Narratives
- 11.4 Visualizing Data for Impact
- 12
Lesson 12: Capstone Project - Employee Attrition Prediction
- 12.1 Project Introduction and Problem Statement
- 12.2 Data Collection and Preparation
- 12.3 Data Analysis and Modeling
- 12.4 Data Storytelling and Presentation
Materials
All necessary course materials are included.
System requirements
Minimum technical expectations for the online learning environment. Your IT team can use this as a checklist.
Internet connectivity
Cable, Fiber, DSL, or LEO Satellite (i.e. Starlink) internet with speeds of at least 10mb/sec download and 5mb/sec upload are recommended for the best experience.
While cellular hotspots may allow access to our courses, users may experience connectivity issues by trying to access our learning management system. This is due to the potential high download and upload latency of cellular connections. Therefore, it is not recommended that students use a cellular hotspot as their primary way of accessing their courses.
Hardware
- CPU: 1 GHz or higher
- RAM: 4 GB or higher
- Resolution: 1280 x 720 or higher. 1920x1080 resolution is recommended for the best experience.
Speakers / Headphones Microphone for Webinar or Live Online sessions.
Operating system
Windows 7 or higher. Mac OSX 10 or higher. Latest Chrome OS Latest Linux Distributions.
While we understand that our courses can be viewed on Android and iPhone devices, we do not recommend the use of these devices for our courses. The size of these devices do not provide a good learning environment for students taking online or live online based courses.
Web browser
Latest Google Chrome is recommended for the best experience. Latest Mozilla FireFox Latest Microsoft Edge Latest Apple Safari.
Recommended software
Office suite software (Microsoft Office, OpenOffice, or LibreOffice) PDF reader program (Adobe Reader, FoxIt) Courses may require other software that is described in the above course outline.
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