Data Science

● Python – Variables and data types
● Python – Data Structures in Python
● Python – Functions and methods
● Python – If statements
● Python – Loops
● Python – Python syntax essentials
● Python – Writing/Reading/Appending to a file
● Python – Common pythonic errors
● Python – Getting user Input
● Python – Stats with python
● Python – Module Import
● Python – List and Multidimensional lists
● Python – Reading from CSV
● Python – Multi Line Print
● Python – Dictionaries
● Python – Built in functions
● Python – Built in Modules

Python Numpy – Introduction
● Python Numpy – Creating an Array
● Python Numpy – Reading Text Files
● Python Numpy – Array Indexing
● Python Numpy – N-Dimensional Arrays
● Python Numpy – Data Types
● Python Numpy – Array Math
● Python Numpy – Array Methods
● Python Numpy – Array Comparison and Filtering
● Python Numpy – Reshaping and Combining Arrays

●Python Pandas – Introduction
●Introduction to Data Structures
●Python Pandas – Series
●Python Pandas – DataFrame
●Python Pandas – Basic Functionality
●Python Pandas – Descriptive Statistics
●Python Pandas – Indexing and Selecting Data
●Python Pandas – Function Application
●Python Pandas – Reindexing
●Python Pandas – Iteration
●Python Pandas – Sorting
●Python Pandas – Working with Text Data
●Python Pandas – Options and Customization
●Python Pandas – Missing Data
●Python Pandas – GroupBy
●Python Pandas – Merging/Joining
●Python Pandas – Concatenation
●Python Pandas – IO Tools
●Python Pandas – Dates Conversion

Introduction to R Programming
● Importance of R
● Data Types and Variables in R
● Operators in R
● Conditional Statements in R
● Loops in R
● R script and Functions in R
● Building Web Application using Rshinny

● Install SQL packages and Connecting to DB
● Basics of SQL DB, Primary key, Foreign Key
● SELECT SQL command, WHERE Condition
● Retrieving Data with SELECT SQL command and WHERE Condition to Pandas
Data frame
● SQL JOINs
● Left Join, Right Joins, Multiple Joins

● What is Machine Learning
● Types of Machine Learning
● Applications of Machine Learning
● Supervised vs Unsupervised learning
● Classification vs Regression
● Training and testing Data
● features and labels

● Introduction
● Introducing the form of simple linear regression
● Estimating linear model coefficients
● Interpreting model coefficients
● Using the model for prediction
● Plotting the “least squares” line
● Quantifying confidence in the model
● Identifying “significant” coefficients using hypothesis testing and pvalues
● Assessing how well the model fits the observed data
● Extending simple linear regression to include multiple predictors
● Comparing feature selection techniques: R-squared, p-values, crossvalidation
● Creating “dummy variables” (using pandas) to handle categorical
predictors

● Refresh your memory on how to do linear regression in scikit-learn
● Attempt to use linear regression for classification
● Show you why logistic regression is a better alternative for
classification
● Brief overview of probability, odds, e, log, and log-odds
● Explain the form of logistic regression
● Explain how to interpret logistic regression coefficients
● Demonstrate how logistic regression works with categorical features
● Compare logistic regression with other models

● Introduction
● Tuning parameters
● Kernel
● Regularization
● Gamma
● Margin
● Classification Example

● Introduction
● Working Example

● Introduction
● Unsupervised Learning
● K-Means Algorithm
● Optimization Objective
● Random Initialization
● Choosing the number of clusters

● Introduction
● Working Example

● Introduction
● Cost Function
● Backpropagation Algorithm
● Working Example

● Introduction to NLTK
● Stop words
● Stemming
● Lemmatization
● Named entity recognition
● Text classification
● Sentiment analysis

● Python Project -Introduction
● Python Project -Housing Data Set
● Python Project -Understand the problem
● Python Project -Hypothesis Generation
● Python Project -Get Data
● Python Project -Data Exploration
● Python Project -Data Pre-Processing
● Python Project -Feature Engineering
● Python Project -Model Training
● Python Project -Model Evaluation

● Introduction to the Data and Machine Learning on Google Cloud
● Recommending Products using Cloud SQL and Spark
● Predict Visitor Purchases Using BigQuery ML
● Deriving Insights from Unstructured Data using Machine Learning
● Summary