Training on Data Science & Data Analytics

Module 1: Introduction to Data Science & Analytics

• Overview of data science and data analytics

• Understanding the data analytics lifecycle

• Types of data: structured, semi-structured, unstructured

• Key concepts: descriptive, diagnostic, predictive, prescriptive analytics

• Case Study: Data-driven decision-making in organizations

• Practical Exercise: Exploring datasets

Module 2: Data Collection & Data Cleaning

• Data sources and data acquisition methods

• Data cleaning and preprocessing techniques

• Handling missing values, outliers, and duplicates

• Data transformation and normalization

• Practical Lab: Cleaning and preparing a dataset for analysis

Module 3: Exploratory Data Analysis (EDA)

• Understanding data distributions and patterns

• Summary statistics and correlation analysis

• Visualizing data trends and relationships

• Detecting anomalies and insights

• Practical Exercise: Performing EDA using Python/R

Module 4: Statistical Analysis & Hypothesis Testing

• Descriptive and inferential statistics

• Probability distributions and sampling methods

• Hypothesis testing and significance levels

• Confidence intervals and regression basics

• Practical Lab: Applying statistical tests to datasets

Module 5: Introduction to Predictive Modeling

• Overview of predictive analytics and machine learning

• Regression analysis (linear and logistic)

• Classification techniques

• Model evaluation metrics

• Practical Exercise: Building a simple predictive model

Module 6: Advanced Predictive Analytics & Machine Learning

• Decision trees, random forests, and ensemble methods

• Clustering and segmentation techniques

• Time series analysis and forecasting

• Model optimization and validation

• Practical Lab: Implementing machine learning algorithms

Module 7: Data Visualization & Storytelling

• Principles of effective data visualization

• Charts, dashboards, and reporting techniques

• Storytelling with data for decision-makers

• Tools: Power BI, Tableau, matplotlib, seaborn

• Practical Exercise: Creating a data visualization dashboard

Module 8: Big Data Analytics & Tools

• Overview of big data concepts and ecosystems

• Introduction to Hadoop, Spark, and cloud-based analytics

• Data pipelines and workflow automation

• Integrating big data with analytics tools

• Practical Lab: Analyzing large datasets using Python/Spark

Module 9: Data Governance, Ethics & Security

• Data privacy regulations and compliance (GDPR, etc.)

• Data quality management and documentation

• Ethical use of data

• Securing sensitive data and access control

• Practical Exercise: Implementing data governance policies

Module 10: Capstone Project & Analytics Strategy

• Applying learned skills in a real-world scenario

• Developing an end-to-end analytics solution

• Presenting insights and recommendations

• Best practices for analytics-driven decision-making

• Emerging trends in data science and analytics