INTRODUCTION TO BIG DATA ANALYTICS
- Understanding the big data landscape
- Role of data scientists in different industries
- Exploring the data science workflow.
DATA COLLECTION AND PREPROCESSING
- Data sources and types
- Data cleaning and quality assessment
- Data transformation and feature engineering
EXPLORATORY DATA ANALYSIS
- Descriptive statistics and data distribution
- Data visualization techniques
- Identifying patterns and outliers
STATISTICAL ANALYSIS FOR DATA SCIENCE
- Introduction to basic statistical concepts
- Hypothesis testing and p-values
- Correlation and causation.
INTRODUCTION TO MACHINE LEARNING
- Supervised vs. unsupervised learning
- Overview of popular machine learning algorithms
- Model selection and evaluation
PREDICTIVE ANALYTICS WITH REGRESSION
- Linear regression and its applications
- Logistic regression for classification
- Model evaluation metrics
DATA VISUALIZATION AND COMMUNICATION
- Principles of effective data visualization
- Creating plots and charts using Python libraries
- Storytelling with data.
INTRODUCTION TO BIG DATA TECHNOLOGIES
- Overview of Hadoop and MapReduce
- Introduction to Apache Spark and distributed computing
- Handling big data challenges
ETHICS AND PRIVACY IN DATA ANALYTICS
- Ethical considerations in data collection and analysis
- Data privacy regulations and compliance
- Responsible data handling practices
- Applying learned concepts to solve a real-world data challenge.
- Data analysis, interpretation, and presentation
- Collaborative team project.