Python with Data Science Course

Python with Data Science Course Outline

Duration: 40–50 Hours
Level: Beginner to Advanced
Delivery Mode: Online / Offline
Target Audience: Aspiring Data Scientists, Analysts, Developers, and IT Professionals
Prerequisites: Basic programming knowledge is recommended but not mandatory

Module 1: Introduction to Python for Data Science

• Introduction to Python Programming
• Python Setup and IDEs (Jupyter Notebooks, VS Code)
• Understanding Python Syntax and Data Types
• Control Flow (if-else, loops)
• Functions and Lambda Functions
• Working with Python Modules and Libraries

Module 2: Python Data Structures and Libraries

• Working with Lists, Tuples, Sets, and Dictionaries
• Introduction to NumPy: Arrays, Indexing, and Operations
• Pandas for Data Manipulation: Series and DataFrames
• Importing Data: CSV, Excel, and SQL databases
• Data Cleaning: Handling Missing Data, Duplicates, and Errors
• Data Transformation and Aggregation in Pandas

Module 3: Data Visualization in Python

• Introduction to Data Visualization Concepts
• Matplotlib Basics: Line Plots, Histograms, Bar Charts
• Seaborn for Advanced Visualizations: Heatmaps, Box Plots, Pair Plots
• Plotly for Interactive Visualizations
• Customizing Plots: Titles, Labels, Legends, and Axes
• Creating Dashboards with Plotly and Dash

Module 4: Introduction to Statistics and Probability

• Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
• Probability Distributions: Normal, Binomial, Poisson
• Hypothesis Testing and Confidence Intervals
• P-Values and Statistical Significance
• Introduction to Sampling and Sampling Methods

Module 5: Exploratory Data Analysis (EDA)

• Understanding the EDA Process
• Visual and Statistical Techniques for EDA
• Identifying Trends, Outliers, and Patterns in Data
• Feature Engineering and Feature Selection
• Correlation Analysis and Covariance
• Using Pandas Profiling for Automated EDA

Module 6: Machine Learning with Python

• Introduction to Machine Learning: Supervised vs Unsupervised Learning
• Scikit-learn for Building Models
• Linear Regression and Logistic Regression
• Decision Trees, Random Forests, and KNN
• Model Evaluation: Accuracy, Precision, Recall, F1 Score, AUC
• Cross-validation and Hyperparameter Tuning

Module 7: Advanced Machine Learning Techniques

• Support Vector Machines (SVM)
• K-Means Clustering and Hierarchical Clustering
• Principal Component Analysis (PCA) for Dimensionality Reduction
• Ensemble Learning: Boosting, Bagging, and Stacking
• Neural Networks and Introduction to Deep Learning (with TensorFlow)

Module 8: Natural Language Processing (NLP) with Python

• Introduction to NLP and Text Data
• Text Preprocessing: Tokenization, Lemmatization, and Stopword Removal
• Word Embeddings: Word2Vec and GloVe
• Sentiment Analysis using Python
• Topic Modeling with LDA (Latent Dirichlet Allocation)
• Named Entity Recognition (NER) with SpaCy

Module 9: Time Series Analysis and Forecasting

• Time Series Data and Its Components
• Time Series Decomposition: Trend, Seasonality, Residuals
• Stationarity and Differencing
• ARIMA Model for Time Series Forecasting
• Exponential Smoothing and Prophet Library
• Evaluation Metrics for Time Series Forecasting

Module 10: Deep Learning Basics (Optional)

• Introduction to Neural Networks and Backpropagation
• Using TensorFlow and Keras for Deep Learning
• Building a Basic Neural Network for Classification
• Convolutional Neural Networks (CNN) and Image Recognition
• Recurrent Neural Networks (RNN) and LSTM for Sequential Data
• Training, Testing, and Evaluating Deep Learning Models

Module 11: Capstone Project and Industry Use Cases

• End-to-End Data Science Project: From Data Collection to Model Deployment
• Industry Use Cases in Finance, Healthcare, Marketing, etc.
• Model Deployment on Cloud Platforms (Optional)
• Presentation of Results and Interpretation of Insights
• Preparing for Data Science Job Roles: Interview Tips and Case Studies