Data Science – CareerDispatch.com

Advanced Data Science & AI Applications (Track2)

CDsuper

Number of lessons: 46
Foundations for Data Science (Prerequisite Track1)

CDsuper

Number of lessons: 47

Chapter 1 – Python for Data Science
- Lesson 1 – Chapter 1 Overview:
- Lesson 1.1 – Introduction to Python
- Lesson 1.2 – Control Flow & Loops
- Lesson 1.3 – Functions & Modules
- Lesson 1.4 – Working with Lists, Dictionaries & Tuples
Hands-On Project:
- Hands-On Project: Building a Data Analysis Pipeline
- Predictive Modeling for Finance: Analyzing Transaction Patterns
- Enhanced Hands-On Project: Improving Predictive Accuracy & Fraud Detection Modeling
Chapter 2 – Math & Statistics for Data Science
- Lesson 2 – Introduction to Math & Statistics for Data Science
- Lesson 2.1 – Descriptive Statistics
- Hands-On Project: Predictive Modeling for Healthcare Analytics
- Lesson 2.2 – Probability Theory & Bayes’ Theorem
- Lesson 2.2.1 – Refining Bayesian Model Accuracy & Adding Interactive Probability Dashboards
- Lesson 2.3 – Hypothesis Testing for Data Science
- Lesson 2.3.1 – Fully Integrated Lesson 2.3 – Hypothesis Testing for AI, Finance & Healthcare with Machine Learning Automation
- Lesson 2.4 – Linear Algebra & Matrix Operations
- Lesson 2.4.1 – Vector and Matrix Manipulations
- Lesson 2.4.2 – Applying Matrix Transformations to Datasets
Hands-On Project
- Implement Statistical Analysis on Real-World Datasets
- Refining Predictive Accuracy & Extending Data Visualization for Fraud Detection
Chapter 3 – Data Wrangling & Preprocessing
- Lesson 3.1 – Data Cleaning Techniques
- Lesson 3.1.1 – Real-Time Data Cleaning Automation & Outlier Detection for Fraud Prevention
- Lesson 3.1.2 – Real-Time Data Cleaning Automation & Outlier Detection for Fraud Prevention (extended)
- Lesson 3.2 – Feature Engineering & Transformation
- Lesson 3.2.1 – Scaling, Normalization, Encoding Categorical Variables
- Lesson 3.2.2 – Creating New Features for Better Model Performance
- Lesson 3.4 – Data Formats: CSV, JSON, Excel, Databases
- Lesson 3.4.1 – Importing & Exporting Structured Data
- Lesson 3.4.2 – Querying Datasets Using SQL
Hands-On Project:
- Project: Clean and preprocess financial transaction datasets
- Refining & Enhancing Real-Time Fraud Prevention with AI-Powered Alerts & Interactive Dashboards
Chapter 4 – Exploratory Data Analysis (EDA)
- Lesson 4 – Exploratory Data Analysis (EDA)
- Lesson 4.1 – Data Visualization with Matplotlib & Seaborn
- Lesson 4.1.1 – Creating Plots, Histograms, Scatterplots
- Lesson 4.1.2 – Customizing Visualization Aesthetics
- Lesson 4.2 – Correlation Analysis & Feature Selection
- Lesson 4.2.1 – Identifying Relationships Between Features
- Lesson 4.2.2 – Feature Selection for Predictive Modeling
- Lesson 4.3 – Anomaly Detection & Pattern Recognition
- Lesson 4.3.1 – Detecting Fraudulent Transactions & Rare Events
- Lesson 4.3.2 – Using Clustering and Statistical Methods
- Lesson 4.4 – Automating EDA with Python Libraries
- Lesson 4.4.1 – Pandas Profiling for Automated Reports
- Lesson 4.4.2 – Pandas Profiling for Automated Reports
Hands-On Project:
- Project: Perform EDA on a customer behavior dataset
- Refining Real-Time Fraud Prevention with AI-Powered Alerts
- Enhancing Interactive Customer Behavior Dashboards

Advanced Data Science & AI Applications (Track2)

Foundations for Data Science (Prerequisite Track1)

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