Simple linear regression

• Multiple regression
• Training and testing model

Introduction to Python

• 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 Practice assignment

Iterative Operations & Functions in Python

• 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

Data Handling in Python using NumPy & Pandas

• Introduction to NumPy arrays, functions & properties Introduction to Pandas & Data frames
• Importing and exporting external data in Python Feature engineering using Python

Data Science & Machine Learning in Python

• 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 generalization capabilities 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
• Regularization of Generalized 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
• Support Vector Machines (SVM) & kNN in Python
• Introduction to idea of observation based learning Distances and similarities k Nearest Neighbors (kNN) for classification Brief mathematical background on SVM 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

Text Mining in Python

• Gathering text data using web scraping with url lib
• Processing raw web data with Beautiful Soup
• 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

Introduction

• What is Machine learning?
• Overview about sci-kit learn and tensorflow
• Types of ML
• Some complementing fields of ML
• ML algorithms
• Machine learning examples

NumPy Arrays

• Creating multidimensional array
• NumPy-Data types
• Array attributes
• Indexing and Slicing
• Creating array views and copies
• Manipulating array shapes
• I/O with NumPy

Working with Pandas

• Installing pandas
• Pandas data frames
• Pandas Series
• Data aggregation with Pandas Data Frames
• Concatenating and appending Data Frames
• Joining Data Frames
• Handling missing data

Python Regular Expressions

• What are regular expressions?
• The match Function
• The search Function
• Matching vs searching
• Search and Replace
• Extended Regular Expressions
• Wildcard

Python Oracle Database Access

• Install the cx_Oracle and other Packages
• Create Database Connection
• CREATE, INSERT, READ, UPDATE and DELETE Operation
• DML and DDL Operation with Databases
• Performing Transactions
• Handling Database Errors
• Disconnecting Database

Regression based learning

• Simple regression
• Multiple regression
• Logistic regression
• Predicting house prices with regression

Clustering based learning

• Definition
• Types of clustering
• The k-means clustering algorithm

Data mining

• Introducing data mining
• Decision Tree
• Affiity Analysis
• Clustering

Introducing matplotlib

• Bar Charts
• Line Charts
• Scatter plots
• Bubble charts

Working with openCV

• Setting up opencv
• Loading and displaying images
• Applying image filters
• Tracking faces
• Face recognition

Inferential statistics

• Standard error
• Confidence interval
• Hypothesis testing
• p-value

Sample vs. population mean

• Variance of population

Probability and statistics

• Probability density functions
• Poisson Process
• Law of large numbers
• Normal distribution
• Sampling distribution
• Mean
• Variance of Bernoulli
• Regression

Data Science Course Curriculum

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