Data Science with Python Training Syllabus (45 Days) – KIT Training Point

Total Duration: 45 Days
Total Hours: 90 Hours (2 Hours per Day)

Module 1: Introduction to Data Science & Python (Day 1-5 | 10 Hours)

• What is Data Science?
  • Introduction to Data Science, its applications, and importance in various industries
  • The Data Science Life Cycle: Data Collection, Data Cleaning, Model Building, and Deployment
• Setting Up Python for Data Science
  • Installing Python and Setting up the Environment (Anaconda, Jupyter Notebook)
  • Introduction to Python Libraries: NumPy, Pandas, Matplotlib, Seaborn
  • Python Basics: Variables, Data Types, Functions, Loops, and Conditionals
• Hands-on Practice
  • Writing simple Python scripts for data manipulation and analysis

Module 2: Python Libraries for Data Science (Day 6-10 | 10 Hours)

• NumPy for Data Science
  • Introduction to NumPy Arrays and Matrix Operations
  • Array Slicing, Indexing, and Mathematical Functions
  • Random Number Generation and Linear Algebra in NumPy
• Pandas for Data Science
  • DataFrames: Creating, Inspecting, and Manipulating DataFrames
  • Data Cleaning: Handling Missing Values, Duplicates, and Data Types
  • Aggregation and Grouping Data with Pandas
• Hands-on Practice
  • Practice exercises using NumPy and Pandas to manipulate datasets

Module 3: Data Visualization with Python (Day 11-15 | 10 Hours)

• Introduction to Data Visualization
  • Importance of Data Visualization in Data Science
  • Choosing the Right Type of Visualization
• Matplotlib and Seaborn
  • Creating Line, Bar, Pie, and Scatter Plots with Matplotlib
  • Advanced Visualizations with Seaborn (Heatmaps, Boxplots, Pairplots)
• Hands-on Practice
  • Visualizing Data Using Matplotlib and Seaborn for insights

Module 4: Exploratory Data Analysis (EDA) (Day 16-20 | 10 Hours)

• What is EDA?
  • Overview of EDA: Understanding Data Characteristics
  • Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
• Techniques for EDA
  • Univariate and Multivariate Analysis
  • Identifying Patterns and Relationships in Data
  • Correlation and Covariance Analysis
• Hands-on Practice
  • Conducting EDA on Real-World Datasets using Python Libraries

Module 5: Data Preprocessing and Feature Engineering (Day 21-25 | 8 Hours)

• Data Cleaning
  • Identifying and Handling Missing Values, Outliers, and Noise
  • Encoding Categorical Data (Label Encoding, One-Hot Encoding)
  • Data Transformation: Scaling, Normalization
• Feature Engineering
  • Feature Selection and Feature Extraction
  • Creating New Features from Existing Data
  • Reducing Dimensions (PCA)
• Hands-on Practice
  • Data Preprocessing and Feature Engineering on a Dataset

Module 6: Introduction to Machine Learning (Day 26-30 | 8 Hours)

• Overview of Machine Learning
  • Supervised vs Unsupervised Learning
  • Classification vs Regression Problems
• Machine Learning Algorithms
  • Linear Regression, Logistic Regression
  • Decision Trees, Random Forests, and K-Nearest Neighbors (KNN)
  • K-Means Clustering
• Hands-on Practice
  • Building and Evaluating Simple ML Models Using Python (Scikit-learn)

Module 7: Model Evaluation and Tuning (Day 31-35 | 8 Hours)

• Evaluating Model Performance
  • Metrics: Accuracy, Precision, Recall, F1 Score, AUC-ROC Curve
  • Overfitting vs Underfitting: Bias-Variance Tradeoff
• Model Tuning
  • Hyperparameter Tuning with Grid Search and Random Search
  • Cross-Validation Techniques
• Hands-on Practice
  • Evaluating and Tuning Machine Learning Models in Python

Module 8: Advanced Machine Learning Algorithms (Day 36-40 | 8 Hours)

• Ensemble Methods
  • Random Forests and Gradient Boosting Machines (GBM)
  • XGBoost and LightGBM
• Support Vector Machines (SVM)
  • Understanding SVMs for Classification Tasks
  • Kernel Functions and Hyperplanes
• Hands-on Practice
  • Implementing Advanced ML Algorithms on Real-World Datasets

Module 9: Deep Learning Introduction (Day 41-45 | 8 Hours)

• Introduction to Deep Learning
  • What is Deep Learning?
  • Neural Networks: Layers, Activation Functions
• TensorFlow and Keras Basics
  • Building a Neural Network with TensorFlow and Keras
  • Introduction to CNN (Convolutional Neural Networks)
• Hands-on Practice
  • Building a Basic Neural Network and a Convolutional Neural Network (CNN) for Image Classification

Capstone Project (Day 45)

• Final Assessment
  • Practical Exam: Solve a Data Science Problem from Data Collection to Model Deployment
  • Present Results: Data Visualizations, Model Performance, and Insights
• Project Presentation
  • Present your findings to the class and receive feedback
  • Prepare a detailed project report and deliverables