Module 1: Introduction to Big Data & Analytics (Weeks 1-2)
What is Big Data? Importance & Applications
Characteristics of Big Data (Volume, Variety, Velocity, Veracity, Value)
Traditional vs. Big Data Processing
Introduction to Data Science and Artificial Intelligence
Challenges in Big Data Handling
Hands-on Activities:
Case studies on Big Data applications
Setting up a cloud environment for Big Data processing
Module 2: Data Engineering & Storage Technologies (Weeks 3-4)
Understanding Data Warehousing & Data Lakes
Distributed Storage: HDFS (Hadoop Distributed File System)
Data ingestion tools: Apache Kafka, Apache Sqoop, Apache Flume
NoSQL Databases: MongoDB, Cassandra, HBase
Cloud Storage Solutions: AWS S3, Google Cloud Storage, Azure Blob Storage
Hands-on Activities:
Installing & using Hadoop for distributed storage
Setting up MongoDB & Cassandra
Module 3: Python Programming for Data Analytics (Weeks 5-7)
Introduction to Python
Data Types, Loops, and Functions
File Handling in Python
Working with Pandas & NumPy for Data Processing
Object-Oriented Programming (OOP) concepts
Introduction to API Integration
Hands-on Activities:
Writing Python scripts for data manipulation
Data extraction from APIs (e.g., Twitter, OpenWeather)
Module 4: SQL & Database Management for Big Data (Weeks 8-9)
Introduction to Relational Databases
SQL Basics: Select, Insert, Update, Delete
Database Normalization & Indexing
Advanced SQL Queries (Joins, Subqueries, Aggregations)
Connecting SQL with Python
Hands-on Activities:
Building and querying a MySQL/PostgreSQL database
Performing ETL (Extract, Transform, Load) operations
Module 5: Data Cleaning & Preprocessing (Weeks 10-11)
Handling Missing & Duplicate Data
Handling Categorical Data (One-Hot Encoding, Label Encoding)
Data Scaling & Normalization
Outlier Detection & Removal
Feature Engineering
Hands-on Activities:
Cleaning and preparing a real-world dataset
Module 6: Data Visualization & Business Intelligence (Weeks 12-13)
Introduction to Data Visualization
Tools: Tableau, Power BI, Matplotlib, Seaborn
Creating Dashboards & Reports
Interactive Visualizations with Plotly & Dash
Hands-on Activities:
Building Tableau dashboards for insights
Developing interactive Power BI reports
Module 7: Machine Learning for Big Data (Weeks 14-16)
Introduction to Machine Learning
Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, Random Forest
Unsupervised Learning: K-Means Clustering, PCA
Model Evaluation & Performance Metrics
Hands-on Activities:
Training and testing machine learning models on real-world datasets
Module 8: Deep Learning & Neural Networks (Weeks 17-19)
Introduction to Deep Learning
Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN, LSTMs)
Implementing Deep Learning Models with TensorFlow & Keras
Hands-on Activities:
Training an image classifier using CNNs
Implementing sentiment analysis using RNNs
Module 9: Big Data Frameworks (Hadoop & Spark) (Weeks 20-22)
Introduction to the Hadoop Ecosystem
Understanding MapReduce
Introduction to Apache Spark
Spark Components: RDDs, DataFrames, Spark SQL, Spark MLlib
Real-Time Data Processing with Spark Streaming
Hands-on Activities:
Running a Spark application for data processing
Implementing a real-time analytics dashboard
Module 10: Web Scraping & Data Extraction (Weeks 23-24)
Introduction to Web Scraping
Web Scraping with BeautifulSoup & Scrapy
Automating Data Collection with Selenium
ETL (Extract, Transform, Load) Processes
Hands-on Activities:
Scraping e-commerce data for analysis
Module 11: Big Data Security & Privacy (Weeks 25-26)
Data Governance & Compliance (GDPR, CCPA)
Security in Big Data Processing
Encryption & Access Control in Big Data
Ethical Considerations in Data Analytics
Hands-on Activities:
Implementing data security best practices
Final Capstone Project & Career Development (Weeks 27-30)
Capstone Project: End-to-End Big Data Solution
Resume Building & Portfolio Development
Freelancing & Entrepreneurship in Data Analytics
Job Search Strategies
Final Deliverables & Certification
Module 1: Introduction to Freelancing
- What is freelancing?
- Benefits and challenges of freelancing
- Common freelancing fields and industries
- Overview of freelancing platforms (Upwork, Fiverr, Freelancer, Toptal, etc.)
- Setting realistic expectations
Module 2: Identifying Your Skills & Niche
- Assessing your skills and strengths
- Choosing a profitable niche
- Developing in-demand skills
- Tools and software for freelancers
Module 3: Creating a Strong Freelance Profile
- Crafting a compelling profile
- Writing an effective bio and summary
- Selecting the right portfolio samples
- Setting your pricing strategy
- Optimizing your profile for visibility
Module 4: Finding & Winning Clients
- How to search for freelance jobs
- Writing proposals that stand out
- Effective communication with clients
- Negotiation skills and pricing strategies
- Red flags to watch out for
Module 5: Delivering Quality Work & Client Management
- Managing client expectations
- Handling feedback and revisions
- Meeting deadlines and managing workload
- Building long-term client relationships
- Dealing with difficult clients
Module 6: Financial Management for Freelancers
- Setting rates and pricing models
- Invoicing and payment methods
- Tracking income and expenses
- Tax considerations for freelancers
- Managing finances for stability
Module 7: Productivity & Time Management
- Creating a productive workspace
- Time management strategies
- Avoiding procrastination and burnout
- Balancing multiple projects effectively
- Using project management tools (Trello, Asana, etc.)
Module 8: Marketing Yourself as a Freelancer
- Personal branding strategies
- Building a professional website/portfolio
- Leveraging social media for freelancing
- Networking and collaboration opportunities
- Content marketing and blogging for exposure
Module 9: Expanding & Scaling Your Freelance Business
- Transitioning from freelancer to agency
- Outsourcing and hiring team members
- Increasing rates and service offerings
- Diversifying income streams
- Creating passive income as a freelancer
Module 10: Legal & Ethical Aspects of Freelancing
- Contracts and agreements
- Intellectual property rights
- Handling disputes and conflicts
- Ethical freelancing practices
- Protecting yourself from scams
Final Project & Certification
- Completing a real-world freelance project
- Reviewing and refining skills
- Receiving feedback and certification