Data Analytics & Data Science: 5-Day Intensive Bootcamp

Course Overview

Master data analytics and data science in 5 intensive days. Learn Python, SQL, machine learning, Tableau, Power BI, and AI tools. Transform from beginner to job-ready with hands-on projects using real-world datasets.

📅 Day 1: Python & Data Analytics Fundamentals

Morning Session (9:00 AM - 12:30 PM)

• Data Analytics Introduction: Types of analytics, career paths, 2025 trends

• Python Fundamentals: Variables, data types, functions, control flow

• Essential Libraries: NumPy arrays, Pandas DataFrames, data manipulation

• Hands-on: Python programming exercises

Afternoon Session (2:00 PM - 5:30 PM)

• SQL for Analytics: SELECT, JOINs, GROUP BY, aggregations

• Data Cleaning: Handling missing values, outliers, data transformation

• Exploratory Data Analysis (EDA): Statistical summaries, data profiling

• Lab Project: Real dataset analysis with Python and SQL

📅 Day 2: Statistics & Data Visualization

Morning Session (9:00 AM - 12:30 PM)

• Descriptive Statistics: Mean, median, variance, distributions

• Inferential Statistics: Hypothesis testing, p-values, confidence intervals

• Python Visualization: Matplotlib, Seaborn, Plotly

• Hands-on: Statistical analysis and visualization exercises

Afternoon Session (2:00 PM - 5:30 PM)

• Tableau: Dashboards, calculated fields, interactive visualizations

• Power BI: DAX, reports, AI-powered insights

• Data Storytelling: Best practices for business communication

• Lab Project: Building interactive BI dashboards

📅 Day 3: Machine Learning Fundamentals

Morning Session (9:00 AM - 12:30 PM)

• ML Introduction: Supervised vs unsupervised learning, workflow

• Regression Models: Linear, polynomial, ridge, lasso regression

• Feature Engineering: Encoding, scaling, selection techniques

• Model Evaluation: MSE, RMSE, R-squared, cross-validation

• Hands-on: House price prediction project

Afternoon Session (2:00 PM - 5:30 PM)

• Classification Algorithms: Logistic regression, decision trees, random forests, SVM

• Evaluation Metrics: Confusion matrix, precision, recall, ROC-AUC

• Hyperparameter Tuning: Grid search, random search

• Lab Project: Customer churn prediction

📅 Day 4: Advanced ML & Deep Learning

Morning Session (9:00 AM - 12:30 PM)

• Unsupervised Learning: K-means, hierarchical clustering, PCA

• Time Series Analysis: ARIMA, seasonal decomposition, forecasting

• Ensemble Methods: Bagging, boosting, XGBoost, LightGBM

• Hands-on: Customer segmentation and sales forecasting

Afternoon Session (2:00 PM - 5:30 PM)

• Deep Learning Basics: Neural networks, TensorFlow, Keras

• Natural Language Processing: Text processing, sentiment analysis, transformers

• Computer Vision: CNN introduction, image classification

• Lab Project: Sentiment analysis on customer reviews

📅 Day 5: Big Data, MLOps & Career Development

Morning Session (9:00 AM - 12:30 PM)

• Big Data Technologies: Hadoop, Spark, PySpark basics

• Cloud Platforms: AWS (SageMaker), Azure ML, Google Cloud AI

• MLOps: Model deployment, Docker, Flask APIs, experiment tracking

• Hands-on: Deploying ML model as REST API

Afternoon Session (2:00 PM - 5:30 PM)

• Generative AI for Data Science: ChatGPT, AutoML, AI-powered analytics

• Data Ethics: Bias, privacy, responsible AI

• Capstone Project: End-to-end data science project presentation

• Career Guidance: Portfolio building, certifications, job search strategies

• Course Completion & Certification

🎯 Learning Outcomes

✅ Master Python, SQL, Pandas for data analysis
✅ Create visualizations with Tableau, Power BI, Python
✅ Build ML models: regression, classification, clustering
✅ Implement deep learning and NLP solutions
✅ Deploy models using MLOps best practices
✅ Build professional data science portfolio

👥 Who Should Attend?

• Career switchers entering data analytics/science

• Business analysts upgrading skills

• Software developers transitioning to ML

• Students pursuing data careers

• Professionals seeking data-driven decision-making skills

Prerequisites: Basic computer skills, no programming experience needed

🛠️ Tools & Technologies

Languages: Python, SQL, R (intro)
ML Libraries: Scikit-learn, TensorFlow, Keras, PyTorch
BI Tools: Tableau, Power BI
Big Data: Spark, PySpark, Hadoop
Cloud: AWS, Azure, GCP
MLOps: MLflow, Docker, Git

🏆 Certification & Features

✨ Professional Certificate in Data Analytics & Data Science
✨ 40+ hours intensive hands-on training
✨ 15+ real-world projects with datasets
✨ Expert instructors with industry experience
✨ Lifetime access to course materials
✨ 90-day post-training mentorship
✨ Career support: resume review, mock interviews
✨ Small batch sizes (15-20 students)

📊 Industry Demand

• 📈 11.5 million data science jobs by 2026

• 📈 Average Salary: $75K-$160K (role dependent)

• 📈 35% annual growth in job postings

• 📈 Python #1 skill in 69% of data jobs