Data Science and Analytics
Introduction to data analysis, visualization, and interpretation.
Application of data science in decision-making.
Course Study Plan
Month 1: Introduction to Data Science and Analytics
Week 1-2: Overview of Data Science
Definition and scope of data science
Roles and responsibilities of a data scientist
Introduction to key concepts and terminologies
Week 3-4: Data Exploration and Visualization
Basics of data exploration
Descriptive statistics
Data visualization using tools like Matplotlib and Seaborn
Week 5-6: Introduction to Analytics Tools
Overview of analytics tools (e.g., Jupyter Notebooks)
Hands-on exercises for basic data manipulation
Introduction to Python programming for data science
Month 2: Data Cleaning and Preprocessing
Week 1-2: Data Cleaning Techniques
Handling missing data
Outlier detection and treatment
Data imputation methods
Week 3-4: Feature Engineering
Creating new features from existing data
Handling categorical variables
Feature scaling and normalization
Week 5-6: Exploratory Data Analysis (EDA)
Advanced data visualization techniques
Correlation analysis
Extracting insights from data through EDA
Month 3: Statistical Analysis and Hypothesis Testing
Week 1-2: Fundamentals of Statistics
Probability distributions
Central limit theorem
Confidence intervals and hypothesis testing
Week 3-4: Statistical Tests
t-tests, chi-square tests, and ANOVA
Understanding p-values and significance
Practical application of statistical tests
Week 5-6: Analytics in Business Decision-Making
Applying statistical insights to business scenarios
Case studies on data-driven decision-making
Guest lectures from industry professionals
Month 4: Machine Learning Fundamentals
Week 1-2: Introduction to Machine Learning
Types of machine learning (supervised, unsupervised, and reinforcement learning)
Model selection and evaluation metrics
Bias-variance tradeoff
Week 3-4: Supervised Learning Algorithms
Linear regression, logistic regression
Decision trees and ensemble methods
Model interpretation and evaluation
Week 5-6: Unsupervised Learning Algorithms
Clustering algorithms (k-means, hierarchical)
Dimensionality reduction techniques (PCA)
Real-world applications and use cases
Month 5: Advanced Topics in Data Science
Week 1-2: Time Series Analysis
Basics of time series data
Forecasting techniques
Seasonality and trend analysis
Week 3-4: Natural Language Processing (NLP)
Introduction to NLP
Text processing and feature extraction
Sentiment analysis and text classification
Week 5-6: Big Data and Data Science in the Cloud
Overview of big data technologies (Hadoop, Spark)
Cloud platforms for data science (AWS, Azure)
Hands-on exercises on cloud-based data analytics
Month 6: Capstone Project and Certification
Week 1-2: Capstone Project Planning
Defining the scope and objectives of the capstone project
Selecting datasets and project topics
Forming project teams (if applicable)
Week 3-4: Project Implementation
Hands-on coding and analysis for the capstone project
Regular check-ins and feedback sessions
Troubleshooting and problem-solving
Week 5-6: Project Presentation and Certification
Final project presentations
Evaluation by instructors and industry experts
Certification awarded upon successful completion