Data Science with Python Online Training

• What is data science and why is it so important?
• Applications of data science
• Various data science tools
• Data Science project methodology
• Tool of choice-Python: what & why?

• Installation of Python framework and packages: Anaconda & pip
• Writing/Running python programs using Spyder Command Prompt
• Working with Jupyter notebooks
• Creating Python variables
• Numeric , string and logical operations
• Data containers : Lists , Dictionaries, Tuples & sets

• Writing for loops in Python
• While loops and conditional blocks
• List/Dictionary comprehensions with loops
• Writing your own functions in Python
• Writing your own classes and functions

• Need for data summary & visualization
• Summarising numeric data in pandas
• Summarising categorical data
• Group wise summary of mixed data
• Basics of visualisation with ggplot & Seaborn
• Inferential visualisation with Seaborn
• Visual summary of different data combinations

• Need for data summary & visualization
• Summarising numeric data in pandas
• Summarising categorical data
• Group wise summary of mixed data
• Basics of visualisation with ggplot & Seaborn
• Inferential visualisation with Seaborn
• Visual summary of different data combinations

Machine Learning Basics

• Converting business problems to data problems
• Understanding supervised and unsupervised learning with examples
• Understanding biases associated with any machine learning algorithm
• Ways of reducing bias and increasing generalisation capabilites
• Drivers of machine learning algorithms
• Cost functions
• Brief introduction to gradient descent
• Importance of model validation
• Methods of model validation
• Cross validation & average error

Generalized Linear Models in Python

• Linear Regression
• Regularisation of Generalised Linear Models
• Ridge and Lasso Regression
• Logistic Regression
• Methods of threshold determination and performance measures for classification score models

Tree Models using Python

• Introduction to decision trees
• Tuning tree size with cross validation
• Introduction to bagging algorithm
• Random Forests
• Grid search and randomized grid search
• ExtraTrees (Extremely Randomised Trees)
• Partial dependence plots

Boosting Algorithms using Python

• Concept of weak learners
• Introduction to boosting algorithms
• Adaptive Boosting
• Extreme Gradient Boosting (XGBoost)

Support Vector Machines (SVM) & kNN in Python

• Introduction to idea of observation based learning
• Distances and similarities
• k Nearest Neighbours (kNN) for classification
• Brief mathematical background on SVM/li>
• Regression with kNN & SVM

Unsupervised learning in Python

• Need for dimensionality reduction
• Principal Component Analysis (PCA)
• Difference between PCAs and Latent Factors
• Factor Analysis
• Hierarchical, K-means & DBSCAN Clustering

Artificial Neural Networks in Python

• Introduction to Neural Networks
• Single layer neural network
• Multiple layer Neural network
• Back propagation Algorithm
• Neural Networks Implementation in Python

Text Mining in Python

• Gathering text data using web scraping with urllib
• Processing raw web data with BeautifulSoup
• Interacting with Google search using urllib with custom user agent
• Collecting twitter data with Twitter API
• Naive Bayes Algorithm
• Feature Engineering with text data
• Sentiment analysis

Version Control using Git and Interactive Data Products

• Need and Importance of Version Control
• Setting up git and github accounts on local machine
• Creating and uploading GitHub Repos
• Push and pull requests with GitHub App
• Merging and forking projects
• Introduction to Bokeh charts and plotting
• Examples of static and interactive data products