Data Science with Python Course in Mysuru Overview


Data Science with Python involves using the Python programming language to extract insights and knowledge from data through various techniques, including data manipulation, statistical analysis, machine learning, and visualization. Python’s versatility and rich ecosystem of libraries make it a popular choice for data scientists.

Basics of Python:

  • Variables, data types, operators
  • Control structures (if-else, loops)
  • Functions and modules

    • Advanced Python:

  • List comprehensions, lambda functions
  • Object-oriented programming in Python
  • Error handling (exceptions)

    • NumPy:

  • Arrays, array operations
  • Indexing, slicing, broadcasting

    • Pandas:

  • Series, DataFrame basics
  • Data manipulation (filtering, sorting, merging)
  • Handling missing data, reshaping data

    • Matplotlib:

  • Line plots, scatter plots, histograms
  • Customizing plots: labels, colors, annotations

    • Seaborn:

  • Statistical visualization
  • Advanced plots: pair plots, heatmaps

    • Descriptive Statistics:

  • Measures of central tendency, variability
  • Distribution plots: box plots,
  • Pearson correlation coefficient
  • Heatmaps for correlation visualization

    • Hypothesis Testing:

  • T-tests, chi-square tests
  • ANOVA (Analysis of Variance)

    • Regression Analysis:

  • Linear regression: simple, multiple
  • Model evaluation: R-squared, adjusted R-squared

    • Supervised Learning:

  • Linear regression, logistic regression
  • Decision trees, random forests

    • Unsupervised Learning:

  • Clustering: K-means clustering, hierarchical clustering
  • Dimensionality reduction: PCA (Principal Component Analysis)

    • Cross-Validation:

  • K-fold cross-validation
  • Stratified cross-validation

    • Evaluation Metrics:

  • Accuracy, precision, recall, F1-score
  • ROC-AUC curve for binary classification

    • Text Preprocessing:

  • Tokenization, stemming, lemmatization
  • Stopword removal, text normalization

    • Basic NLP Tasks:

  • Text classification using Naive Bayes, SVM
  • Sentiment analysis using NLTK, Scikit-learn

    • Neural Networks Basics:

  • Perceptrons, activation functions (sigmoid, tanh, ReLU)
  • Feedforward neural networks

    • Deep Learning Frameworks:

  • TensorFlow: Basics of tensors, building neural networks
  • Keras: High-level neural networks API for TensorFlow

    • Apache Spark:

  • Introduction to distributed computing
  • Spark RDDs and DataFrames for data processing

    • Model Deployment:

  • Flask: Building web APIs for model deployment
  • Docker: Containerization for deploying models

    • Time Series Analysis:

  • ARIMA models for forecasting
  • Seasonal decomposition

    • Recommendation Systems:

  • Collaborative filtering, content- based filtering
  • Matrix factorization techniques

    • Data Ethics:

  • Bias, fairness, transparency in AI models
  • Privacy concerns in data handling