Data Analytics IN DATA ANALYTICS ( S-DA-112 )

Month 1: Introduction to Python and Basic Data Analysis

Week 1: Python Programming Basics

• Topics:
  • Introduction to Python programming
  • Variables, data types, and basic operators
  • Control structures: if, elif, else, loops (for, while)
  • Functions and scope
• Assignments:
  • Write simple Python programs using loops and conditionals
  • Solve basic math problems with Python

Week 2: Data Structures in Python

• Topics:
  • Lists, tuples, sets, and dictionaries
  • Operations and methods on each data structure
  • Nested data structures
  • Comprehensions (list, set, dictionary)
• Assignments:
  • Create and manipulate data structures
  • Solve data structure-related problems

Week 3: Introduction to Libraries (NumPy and Pandas)

• Topics:
  • NumPy arrays and matrix operations
  • Pandas Series and DataFrames
  • Basic data manipulations with Pandas (filtering, sorting, grouping)
  • Reading and writing data (CSV, Excel)
• Assignments:
  • Practice with NumPy array operations
  • Perform basic data analysis using Pandas on sample datasets

Week 4: Data Cleaning and Preprocessing

• Topics:
  • Handling missing data (NaN values)
  • Removing duplicates and handling outliers
  • Data transformations (scaling, encoding categorical variables)
  • String operations and regular expressions
• Assignments:
  • Clean a dataset with missing values, duplicates, and outliers

Month 2: Data Visualization and Exploratory Data Analysis (EDA)

Week 5: Introduction to Data Visualization

• Topics:
  • Introduction to Matplotlib and Seaborn
  • Plotting line plots, bar charts, histograms, scatter plots
  • Customizing plots (labels, titles, legends)
• Assignments:
  • Create various types of plots to visualize data distributions and trends

Week 6: Advanced Data Visualization

• Topics:
  • Box plots, heatmaps, and pair plots
  • Visualizing correlations and relationships between variables
  • Customizing visualizations with styles and themes
• Assignments:
  • Create advanced visualizations for a given dataset (e.g., heatmap of correlations)

Week 7: Exploratory Data Analysis (EDA)

• Topics:
  • Descriptive statistics: mean, median, mode, variance, and standard deviation
  • Data visualization for EDA (distribution, trends, outliers)
  • Using Pandas profiling and other tools for EDA
• Assignments:
  • Perform EDA on a provided dataset and summarize key insights

Week 8: Feature Engineering

• Topics:
  • Creating new features from existing data
  • Feature scaling (Min-Max, Standardization)
  • One-hot encoding, Label encoding
  • Handling categorical and numerical features
• Assignments:
  • Perform feature engineering on a dataset (create new features, scale data)

Month 3: Statistical Analysis and Hypothesis Testing

Week 9: Introduction to Statistics for Data Analysis

• Topics:
  • Descriptive and inferential statistics
  • Measures of central tendency and dispersion
  • Probability theory and distributions
• Assignments:
  • Solve statistical problems using Python

Week 10: Hypothesis Testing and Confidence Intervals

• Topics:
  • Null and alternative hypothesis
  • T-tests, Z-tests, and Chi-square tests
  • Confidence intervals and significance levels
• Assignments:
  • Perform hypothesis tests on sample datasets

Week 11: Correlation and Regression Analysis

• Topics:
  • Pearson and Spearman correlation
  • Simple linear regression
  • Multiple linear regression
• Assignments:
  • Perform correlation analysis and simple linear regression on real-world data

Week 12: Advanced Statistical Methods

• Topics:
  • ANOVA (Analysis of Variance)
  • Logistic regression
  • Time series analysis basics
• Assignments:
  • Conduct ANOVA and logistic regression analysis on datasets

Month 4: Machine Learning Fundamentals

Week 13: Introduction to Machine Learning

• Topics:
  • Supervised vs Unsupervised learning
  • Overview of machine learning algorithms
  • Scikit-learn library for machine learning
• Assignments:
  • Implement a simple classification model using Scikit-learn

Week 14: Supervised Learning - Classification Algorithms

• Topics:
  • Decision trees, K-Nearest Neighbors, Naive Bayes
  • Model evaluation metrics (accuracy, precision, recall, F1-score)
  • Cross-validation
• Assignments:
  • Implement classification algorithms (e.g., Decision Trees, KNN) on datasets

Week 15: Supervised Learning - Regression Algorithms

• Topics:
  • Linear regression, Ridge/Lasso regression
  • Evaluating regression models (MSE, RMSE, R-squared)
  • Hyperparameter tuning with GridSearchCV
• Assignments:
  • Implement and evaluate regression models on datasets

Week 16: Model Evaluation and Selection

• Topics:
  • Overfitting and underfitting
  • Bias-variance tradeoff
  • Model evaluation techniques (ROC curve, AUC)
• Assignments:
  • Evaluate and select the best model for a given dataset

Month 5: Unsupervised Learning and Deep Dive into Machine Learning

Week 17: Unsupervised Learning - Clustering

• Topics:
  • K-means clustering
  • Hierarchical clustering
  • DBSCAN and other clustering techniques
• Assignments:
  • Apply clustering algorithms to group data points based on patterns

Week 18: Unsupervised Learning - Dimensionality Reduction

• Topics:
  • Principal Component Analysis (PCA)
  • t-SNE (t-Distributed Stochastic Neighbor Embedding)
  • Feature extraction and selection techniques
• Assignments:
  • Apply PCA to reduce dimensionality and visualize high-dimensional data

Week 19: Deep Learning Introduction (Optional)

• Topics:
  • Neural Networks basics
  • Introduction to TensorFlow and Keras
  • Simple feedforward neural network (FFNN) for classification
• Assignments:
  • Build a basic neural network for classification using Keras

Week 20: Model Deployment and Automation

• Topics:
  • Introduction to Flask for deploying models
  • Model saving and loading (Pickle, Joblib)
  • Automating data pipelines with Python
• Assignments:
  • Deploy a machine learning model using Flask or FastAPI

Month 6: Capstone Project and Real-World Applications

Week 21: Capstone Project - Problem Definition and Dataset Collection

• Topics:
  • Define the problem and gather relevant datasets
  • Preprocessing and cleaning the dataset
  • Setting up the project environment
• Assignments:
  • Select and collect the dataset for your capstone project

Week 22: Capstone Project - EDA and Feature Engineering

• Topics:
  • Conduct thorough exploratory data analysis
  • Perform feature engineering and preprocessing
• Assignments:
  • Complete the EDA and feature engineering phase for your project

Week 23: Capstone Project - Model Building and Evaluation

• Topics:
  • Build machine learning models (classification or regression)
  • Evaluate models and fine-tune hyperparameters
• Assignments:
  • Build, evaluate, and tune models for your project

Week 24: Capstone Project - Finalization and Presentation

• Topics:
  • Finalize model selection and presentation
  • Create a project report and visualizations
  • Prepare and present the final project
• Assignments:
  • Submit the final project report and code
  • Present the project to peers/mentors

To start learning Python programming, here are some basic requirements:

1. Basic Computer Knowledge: Understanding how to navigate your operating system, using a text editor, and installing software packages is essential.
2. Access to Python: Install Python (from python.org) and an Integrated Development Environment (IDE) like PyCharm or Visual Studio Code to write and run your code.
3. Problem-Solving Mindset: An analytical approach to breaking down problems into smaller steps that can be translated into code.