Data Science using Python

Complete data science training with python for data analysis

Data Science using Python

Complete data science training with python for data analysis

Course Learning Path

  • Python Essentials – Lists, Functions and Packages and NumPy
  • Visualization – Matplotlib and Seaborn
  • Dictionaries and Pandas
  • Loops, Control Flow and Filtering
  • Creating new functions
  • Lambda functions and error handling
  • List comprehensions and generators
  • Importing Data in Python – Flat Files, API or Web
  • Tidying, Combining and Cleaning Data for Analysis
  • Data Ingestion
  • Exploratory Data Analysis
  • Time Series Data Analysis
  • Data Transformation
  • Indexing and Reshaping Data
  • Rearranging and Grouping Data
  • Concatenating and Merging Data
  • EDA using Graphical Methods
  • EDA using Quantitative Methods
  • Discrete and Continuous Variables
  • Parameter Estimation
  • Confidence Intervals
  • Hypothesis Testing
  • Conda Essentials
  • Installing Packages
  • Working with environments
  • Classification
  • Regression
  • Model Tuning
  • Data Preprocessing – Missing Data Handling, Data Imputation, Centering and Scaling
  • Clustering – K-means, Hierarchical Clustering and t-SNE
  • Dimension Reduction – Principal Component Analysis
  • Classification and Regression Trees (CART)
  • Random Forests
  • Boosting
  • Model Tuning
  • Bias-Variance Tradeoff – Understanding the concept of overfitting and under-fitting
  • Introduction to Deep Learning and Neural Networks
  • Optimizing Neural Network Model using Back Propagation
  • Using Keras to build deep learning models
  • Tuning Keras models
  • Introduction to PyTorch
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Introduction to NLP
  • Model Building using NLP Libraries – Scikit
  • Fine tuning using Grid Search
  • Hadoop Core Components
  • Python Integration with HDFS using Hadoop Streaming
  • Python Integration with Spark using PySpark

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