DATA SCIENCE WITH R PROGRAMMING COURSE

Python for Data Science

The following topics are covered here

Module 1 - Introduction to Data Science with Python

Installing Python Anaconda distribution

Python native Data Types

Basic programing concepts

Python data science packages overview

Module 2 - Python Basics: Basic Syntax, Data Structures

Python Objects

Math & Comparision Operators

Conditional Statement

Loops

Lists, Tuples, Strings, Dictionaries, Sets

Functions

Exception Handling

Module 3 - Numppy Package

Importing Numpy

Numpy overview

Numpy Array creation and basic operations

Numpy Universal functions

Selecting and retrieving Data

Data Slicing

Iterating Numpy Data

Shape Manupilation

Stacking and Splitting Arrays

Copies and Views : no copy, shallow copy , deep copy

Indexing : Arrays of Indices, Boolean Arrays

Module 4 - Pandas Package

Importing Pandas

Pandas overview

Object Creation : Series Object , DataFrame Object

View Data

Selecting data by Label and Position

Data Slicing

Boolean Indexing

Setting Data

Module 5 - Python Advanced: Data Mugging with Pandas

Applying functions to data

Histogramming

String Methods

Merge Data : Concat, Join and Append

Grouping & Aggregation

Reshaping

Analysing Data for missing values

Filling missing values: fill with constant, forward filling, mean

Removing Duplicates

Transforming Data

Module 6 - Python Advanced: Visualization with MatPlotLib

Importing MatPlotLib & Seaborn Libraries

Creating basic chart : Line Chart, Bar Charts and Pie Charts

Ploting from Pandas object

Saving a plot

Object Oriented Plotting : Setting axes limits and ticks

Multiple Plots

Plot Formatting : Custom Lines, Markers, Labels, Annotations, Colors

Satistical Plots with Seaborn

Module 7 - Exploratory Data Analysis: Case Study

Statistics for Data Science

The following topics are covered here

Module 1: Introduction to Statistics

Two areas of Statistics in Data Science

Applied statistics in business

Descriptive Statistics

Inferential Statistics

Statistics Terms and definitions

Type of Data

Quantitative vs Qualitative Data

Data Measurement Scales

Module 2: Harnessing Data

Sampling Data, with and without replacement

Sampling Methods, Random vs Non-Random

Measurement on Samples

Random Sampling methods

Simple random, Stratified, Cluster, Systematic sampling.

Biased vs unbiased sampling

Sampling Error

Data Collection methods

Module 3: Exploratory Analysis

Measures of Central Tendencies

Mean, Median and Mode

Data Variability : Range, Quartiles, Standard Deviation

Calculating Standard Deviation

Z-Score/Standard Score

Empirical Rule

Calculating Percentiles

Outliers

Module 4: Distributions

Distribtuions Introduction

Normal Distribution

Central Limit Theorem

Histogram - Normalization

Other Distributions: Poisson, Binomial et.,

Normality Testing

Skewness

Kurtosis

Measure of Distance

Euclidean , Manhattan and Minkowski Distance

Module 5: Hypothesis & computational Techniques

Hypothesis Testing

Null Hypothesis, P-Value

Need for Hypothesis Testing in Business

Two tailed, Left tailed & Right tailed test

Hypothesis Testing Outcomes : Type I & II erros

Parametric vs Non-Parametric Testing

Parametric Tests , T - Tests : One sample, two sample, Paired

One Way ANOVA

Importance of Parametric Tests

Non Parametric Tests : Chi-Square, Mann-Whitney, Kruskal-Wallis etc.,

Which Test to Choose?

Ascerting accuracy of Data

Module 6: Correlation & Regression

Introduction to Regression

Type of Regression

Hands on of Regression with R and Python.

Correlation

Weak and Strong Correlation

Finding Correlation with R and Python

Machine Learning Associate

Machine Learning Expert

The following topics are covered here

Module 1: Advanced Machine Learning Concepts

Tuning with Hyper parameters.

Popular ML algorithms,

Clustering, classification and regression,

Supervised vs unsupervised.

Choice of ML algorithm

Grid Search vs Random search cross validation

Module 2: Principle Component Analysis (PCA)

Key concepts of dimensionality reduction

PCA theory

Hands on coding.

case study on PCA

Module 3: Random Forest - Ensemble

Key concepts of Randon Forest

Hands on coding.

Pros and cons.

case study on Random Forest

Module 4: Support Vector Machine (SVM)

Key concepts of Support Vector Machine.

Hands on coding.

Pros and Cons.

case study on SVM

Module 5: Natural Language Processing (NLP)

Key concepts of NLP.

Hands on coding.

Pros and Cons.

Text Processing with Vectorization

Sentiment analysis with TextBlob

Twitter sentiment analysis

Module 6: Naïve Bayes Classifier

Key concepts of Naive Bayes.

Hands on coding.

Pros and Cons

Naïve Bayes for text classification

New articles tagging

Module 7: Artificial Neural Network (ANN)

Basic ANN network for Regression and Classification

Hands on coding.

Pros and Cons

Case study on ANN, MLP

Module 8: Tensorflow overview and Deep Learning Intro

Tensorflow work flow demo

Introduction to deep learning.

Tableau Foundation

Module 1: Tableau Introduction

Tableau Interface

Dimensions and measures

Filter shelf

Distributing and publishing

Module 2: Connecting to Data Source

Connecting to sources, Excel, Data bases, Api , Pdf

Extracting and interpreting data.

Module 3: Visual Analytics

Charts and plots with Super Store data

Module 4: Forecasting

Forecasting time series data

Data Science Business Concepts

Module 1: Understanding Business Case

Components of Business Case.

ROI calculation techniques.

Scoping

Module 2: Writing Data Science Business Case

Defining Business opportunity.

Translating to Data Science problem.

Creating project plan

Module 3: Benefits Analysis

Demonstrating break even and benefits analysis with Data Science Solutions.

IRR benefits analyis

Discounted Cash Flow

Module 4: Starting project, Setting up Team and closing

Initiating Project

Setting up the Team

Controling project delivery

Closing project.

Data Science With r training

Introduction to Data Science

What is Data Science?

What is Machine Learning?

What is Deep Learning?

What is Artificial Intelligence?

Data Analytics and its types

 Introduction to R

What is R?

Why R?

Installing R

R environment

How to get help in R

R Studio Review

R Packages

Data Types

Variable Vectors

Lists

Environment Setup

Array

Matrix

Data Frames

Factors

Loops

Functions

Packages

In-Built Datasets

R Basics

Importing data

Manipulating data

Statistics Basics

Error metrics

Machine Learning

Supervised Learning

Unsupervised Learning

Machine Learning using R

Tensorflow Training

Introduction to Deep Learning

What is a neural network?

Supervised Learning with Neural Networks - Python

How Deep Learning is different from Machine Learning

Overview of Machine Learning Concepts

What is Machine Learning?

Supervised Machine Learning algorithms

K-Nearest Neighbors (KNN) concept and application

Naive Bayes concept and application

Logistic Regression concept and application

Classification Trees concept and application

Unsupervised Machine Learning algorithms

Clustering with K-means concept and application

Hierarchial Clustering concept and application

TensorFlow Essentials

Representing tensors

Creating operators and excuting with sessions

Introduction Jupyter notebook for TensorFlow coding

TensorFlow variables

Visualizing data using TensorBoard

ML Algorithm - Linear Regression in TensorFlow

Regression problems

Linear regression applications

Regularization

Available datasets

Coding Linear Regression with TensorFlow - Case study

Deep Neural Networks in TensorFlow

Basic Neural Nets

Single Hidden Layer Model

Multiple Hidden Layer Model

Convolutional Neural Networks

Introduction to Convolutional Neural Networks

Input Pipeline

Introduction to RNN, LSTM, GRU

Reinforcement Learning in Tensorflow

Concept of Reinforcement Learning

Simple model applying Reinforcement Learning in TensorFlow

Hands on Deep Learning Application with TensorFlow

Example Application - Case study

Hands on building the Deep Learning application with TensorFlow

Introduction to TensorFlow

Installing TensorFlow using Docker

Installing Matplotlib

Hello World applicatin with TensorFlow

Basic Statistics

Basic Statistics and Exploratory Analysis

Descriptive summary statistics with Numpy

Summarize continous and categorical data

Outlier analysis

Machine Learning Introduction

Machine learning essentials

Data representation and features

Distance metrics

Supervised learning

Unsupervised learning

Reinforcement learning

Theano, Caffe, Torch, CGT, and TensorFlow

TensorFlow Essentials

Representing tensors

Creating operators and excuting with sessions

Introduction Jupyter notebook for TensorFlow coding

TensorFlow variables

Visualizing data using TensorBoard

ML Algorithm - Linear Regression in TensorFlow

Regression problems

Linear regression applications

Regularization

Available datasets

Coding Linear Regression with TensorFlow - Case study

ML Algorithm - Classification in TensorFlow

Classification problems

Using linear regression for classification

Using logistic regression (including multi-dimensional input)

Multiclass classifiers (such as softmax regression)

Hands on Classificatin with TensorFlow

ML Algorithm - Clustering in TensorFlow

Traversing files in TensorFlow

K-means clustering

Clustering using a self-organizing map

Simple Neural Networks in TensorFlow

Introduction to Neural Networks

Batch training

Variational, denoising and stacked autoencoders

Reinforcement learning

Concept of Reinforcement Learning

Simple model applying Reinforcement Learning in TensorFlow

Convolutional and Recurrent Neural Networks

Advantages and disadvantages of neural networks

Convolutional neural networks

The idea of contextual information

Recurrent neural networks

Real world predictive model - example

Case study - Stock Market Analsis with TensorFlow

Case study - Stock Market Analsis

Hands on Coding in TensorFlow