Python for Data Science provides a robust framework for analyzing and visualizing data using libraries like Pandas, NumPy, and Matplotlib. It offers tools for statistical analysis, machine learning, and data manipulation. Python’s ecosystem supports efficient data processing and predictive modeling, enabling insights across various industries. The language’s simplicity and versatility make it a popular choice for data science professionals.

• Basic knowledge of Python programming will be beneficial. 
• A fundamental understanding of data science concepts, including statistics and data manipulation, is also helpful. 
• Familiarity with basic computer operations and data analysis tools will enhance learning and application in Python for Data Science.

Module 1: Introduction to Python for Data Science

• Overview of Python
  • Why Python for Data Science?
  • Installation: Anaconda, Jupyter Notebook, or standalone Python.
  • Setting up your development environment.
• Python Basics
  • Variables, data types, and operators.
  • Conditional statements (if, else, elif) and loops (for, while).
  • Functions, lambda functions, and modules.

Module 2: Python Libraries for Data Science

• Core Libraries
  • NumPy: Arrays, broadcasting, and mathematical functions.
  • Pandas: DataFrames, series, indexing, and data manipulation.
  • Matplotlib and Seaborn: Data visualization basics.
• Specialized Libraries
  • SciPy: Statistical and scientific computation.
  • Statsmodels: Statistical modeling.
  • Scikit-learn: Machine learning tools.
  • TensorFlow and PyTorch: Deep learning frameworks (introductory level).

Module 3: Data Manipulation with Pandas

• DataFrame Basics
  • Reading and writing data (CSV, Excel, JSON).
  • Inspecting and cleaning data.
• Data Operations
  • Filtering, sorting, and grouping.
  • Aggregation, joins, and merges.
  • Handling missing values.
• Advanced Techniques
  • Pivot tables and reshaping data.
  • MultiIndex and hierarchical data.

Module 4: Data Visualization

• Matplotlib Basics
  • Plotting line graphs, bar charts, and scatter plots.
  • Customizing plots (titles, labels, legends, colors).
• Seaborn for Statistical Visualization
  • Pair plots, heatmaps, and violin plots.
  • Customizing styles and themes.
• Advanced Visualization
  • Plotly for interactive charts.
  • Geospatial data visualization with GeoPandas.

Module 5: Statistics for Data Science

• Descriptive Statistics
  • Measures of central tendency (mean, median, mode).
  • Measures of dispersion (variance, standard deviation).
• Inferential Statistics
  • Probability distributions (normal, binomial, Poisson).
  • Hypothesis testing (t-tests, chi-square tests, ANOVA).
• Correlation and Regression
  • Pearson and Spearman correlation.
  • Linear regression basics.

Module 6: Exploratory Data Analysis (EDA)

• Data Exploration
  • Identifying patterns, trends, and anomalies.
  • Detecting outliers and dealing with them.
• Data Transformation
  • Feature scaling (normalization, standardization).
  • Encoding categorical variables.
• Automated EDA Tools
  • Sweetviz and Pandas Profiling for quick insights.

Module 7: Machine Learning with Python

• Introduction to Machine Learning
  • Supervised vs. unsupervised learning.
  • Steps in building a machine learning model.
• Supervised Learning
  • Regression (Linear, Logistic).
  • Classification (Decision Trees, Random Forests, SVM).
• Unsupervised Learning
  • Clustering (K-Means, DBSCAN).
  • Dimensionality reduction (PCA, t-SNE).
• Model Evaluation
  • Train-test split, cross-validation.
  • Metrics: Accuracy, precision, recall, F1-score.

Module 8: Advanced Machine Learning

• Feature Engineering
  • Creating and selecting features.
  • Handling multicollinearity and interaction terms.
• Hyperparameter Tuning
  • Grid search and random search.
  • Advanced optimization techniques (Bayesian optimization).
• Introduction to Deep Learning
  • Neural networks basics.
  • TensorFlow and Keras for model building.

Module 9: Working with Big Data

• Introduction to Big Data
  • Overview of big data technologies.
  • Working with large datasets in Python.
• PySpark Basics
  • Introduction to Apache Spark and PySpark.
  • Handling RDDs and DataFrames.
• Integration
  • Using Python with Hadoop and SQL databases.

Module 10: Data Science Project Workflow

• Problem Definition
  • Understanding the business context.
  • Defining objectives and success criteria.
• Data Wrangling
  • Data collection and cleaning.
  • Exploratory data analysis.
• Model Building
  • Training, tuning, and evaluating models.
• Deployment
  • Model serialization with pickle or joblib.
  • Creating APIs using Flask or FastAPI.

Module 11: Python in Specialized Data Science Areas

• Natural Language Processing (NLP)
  • Text cleaning, tokenization, and vectorization.
  • Sentiment analysis and topic modeling.
• Time Series Analysis
  • Autoregressive models (ARIMA, SARIMA).
  • Forecasting with Python.
• Computer Vision
  • Image processing with OpenCV.
  • Basics of CNNs using TensorFlow/Keras.

Module 12: Data Science Tools and Platforms

• Version Control
  • Using Git for project collaboration.
• Cloud Platforms
  • Deploying models on AWS, GCP, or Azure.
• Docker and Kubernetes
  • Packaging and deploying data science applications.
• AutoML
  • Introduction to AutoML tools (H2O.ai, Google AutoML).

40 Days (also available fast track course with short term duration)

• Flexible Schedules
• Live Online Training

• Training by highly experienced and certified professionals
• No slideshow (PPT) training, fully Hand-on training
• Interactive session with interview QA’s
• Real-time projects scenarios & Certification Help
• 24 X 7 Support