Data Science & ML Training

Course Overview

Become a Data Scientist and unlock insights from data using Python, Machine Learning, and AI. Master data analysis, visualization, statistical modeling, and deep learning to solve real-world business problems.

Data Science training at SourceKode covers the complete data science pipeline from data collection to model deployment. Learn Python libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and build a portfolio of industry-relevant projects.

Why Learn Data Science?

• Hottest Career: #1 job in America (Glassdoor)
• Highest Salaries: Data Scientists earn ₹8-30 LPA
• Universal Demand: Every industry needs data insights
• Future-Proof: AI/ML is the future of technology
• Problem Solving: Use data to drive business decisions
• Research Opportunities: Academia and R&D roles
• Freelance Potential: High-paying consulting projects

What You’ll Learn

• Python for Data Science: Pandas, NumPy, Matplotlib
• Statistics & Math: Probability, hypothesis testing, linear algebra
• Machine Learning: Supervised, unsupervised, ensemble methods
• Deep Learning: Neural networks, TensorFlow, Keras
• Data Visualization: Tableau, PowerBI, Seaborn
• Big Data: Spark basics, handling large datasets
• Deployment: Flask APIs, cloud deployment

Course Syllabus (100+ Hours)

Module 1: Python Programming (15 hours)

• Python basics and data structures
• NumPy for numerical computing
• Pandas for data manipulation
• Data cleaning and preparation

Module 2: Data Visualization (10 hours)

• Matplotlib and Seaborn
• Interactive plots with Plotly
• Tableau fundamentals
• PowerBI basics
• Storytelling with data

Module 3: Statistics & Mathematics (15 hours)

• Descriptive statistics
• Probability distributions
• Hypothesis testing
• Correlation and regression
• Linear algebra essentials
• Calculus basics for ML

Module 4: Machine Learning (30 hours)

• Supervised Learning:
  • Linear/Logistic Regression
  • Decision Trees, Random Forest
  • SVM, Naive Bayes
  • KNN, Gradient Boosting
• Unsupervised Learning:
  • K-Means Clustering
  • Hierarchical Clustering
  • PCA (dimensionality reduction)
  • Association Rules
• Model Evaluation:
  • Train-test split, cross-validation
  • Metrics: Accuracy, Precision, Recall, F1, ROC-AUC
  • Confusion matrix
  • Hyperparameter tuning

Module 5: Deep Learning (20 hours)

• Neural network fundamentals
• TensorFlow and Keras
• CNN for image classification
• RNN and LSTM for sequences
• Transfer learning
• Model optimization

Module 6: Natural Language Processing (8 hours)

• Text preprocessing
• Sentiment analysis
• Word embeddings (Word2Vec, GloVe)
• Text classification

Module 7: Time Series Analysis (6 hours)

• ARIMA models
• Forecasting techniques
• Seasonality and trends

Module 8: Deployment & Tools (6 hours)

• Flask API for models
• Docker basics
• Cloud deployment (AWS, Azure)
• Git and version control
• Jupyter notebooks and Google Colab

Major Projects

1. Customer Churn Prediction (Classification)
2. House Price Prediction (Regression)
3. Image Classification (Deep Learning)
4. Sentiment Analysis (NLP)
5. Sales Forecasting (Time Series)
6. Recommendation System (Collaborative Filtering)

Career Opportunities

Data Science offers the highest-paying tech roles:

• Data Scientist - Average: ₹8-20 LPA
• Machine Learning Engineer - Average: ₹10-25 LPA
• Data Analyst - Average: ₹5-12 LPA
• AI Engineer - Average: ₹12-30 LPA
• Research Scientist - Average: ₹15-35 LPA

Companies Hiring

• Tech Giants: Google, Microsoft, Amazon, Meta
• Indian Startups: Ola, Swiggy, Zomato, CRED
• Analytics: Mu Sigma, Fractal Analytics, Latentview
• E-commerce: Flipkart, Amazon India
• Finance: Banks, fintech companies
• Consulting: McKinsey, BCG, Deloitte

Prerequisites

• Required: Basic Python (covered in course)
• Recommended: 12th grade mathematics
• Helpful: Statistics basics (taught in course)
• Analytical mindset and problem-solving skills