Data Science using Python

Data Science using Python

Course Description

1. Introduction to Python

  • Python Essentials – Lists, Functions and Packages and NumPy
  • Visualization – Matplotlib and Seaborn
  • Dictionaries and Pandas
  • Loops, Control Flow and Filtering

2. Python Data Science Toolbox

  • Creating new functions
  • Lambda functions and error handling
  • List comprehensions and generators

3. Data Preparation in Python

  • Importing Data in Python – Flat Files, API or Web
  • Tidying, Combining and Cleaning Data for Analysis

4. Data Manipulation using Pandas

  • Data Ingestion
  • Exploratory Data Analysis
  • Time Series Data Analysis
  • Data Transformation
  • Indexing and Reshaping Data
  • Rearranging and Grouping Data
  • Concatenating and Merging Data

5. Statistics essentials in Python

  • EDA using Graphical Methods
  • EDA using Quantitative Methods
  • Discrete and Continuous Variables
  • Parameter Estimation
  • Confidence Intervals
  • Hypothesis Testing

6. Python Environment Setup and Essentials

  • Conda Essentials
  • Installing Packages
  • Working with environments

7. Supervised Learning using Scikit

  • Classification
  • Regression
  • Model Tuning
  • Data Preprocessing – Missing Data Handling, Data Imputation, Centering and Scaling

8. Unsupervised Learning

  • Clustering – K-means, Hierarchical Clustering and t-SNE
  • Dimension Reduction – Principal Component Analysis

9. Tree-Based Models in Python

  • Classification and Regression Trees (CART)
  • Random Forests
  • Boosting
  • Model Tuning
  • Bias-Variance Tradeoff – Understanding the concept of overfitting and under-fitting

10. Deep Learning in Python

  • Introduction to Deep Learning and Neural Networks
  • Optimizing Neural Network Model using Back Propagation
  • Using Keras to build deep learning models
  • Tuning Keras models

11. Deep Learning using PyTorch

  • Introduction to PyTorch
  • Artificial Neural Networks
  • Convolutional Neural Networks

12. Natural Language Processing using Python

  • Introduction to NLP
  • Model Building using NLP Libraries – Scikit
  • Fine tuning using Grid Search

13. Python Integration with Hadoop MapReduce and Spark

  • Hadoop Core Components
  • Python Integration with HDFS using Hadoop Streaming
  • Python Integration with Spark using PySpark



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