DATA SCIENCE TRAINING IN BANGALORE

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

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

The following topics are covered here

Module 1: Machine Learning Introduction

• What is Machine Learning
• Applications of Machine Learning
• Machine Learning vs Artificial Intelligence
• Machine Learning Languages and platforms
• Machine Learning vs Statistical Modelling

Module 2: Machine Learning Algorithms

• Popular Machine Learning Algorithms
• Clustering, Classification and Regression
• Supervised vs Unsupervised Learning
• Application of Supervised Learning Algorithms
• Application of Unsupervised Learning Algorithms
• Overview of modeling Machine Learning Algorithm : Train , Evaluation and Testing.
• How to choose Machine Learning Algorithm?

Module 3: Supervised Learning

• Simple Linear Regression : Theory, Implementing in Python (and R), Working on use case.
• Multiple Linear Regression : Theory, Implementing in Python (and R), Working on use case.
• K-Nearest Neighbors : Theory, Implementing in Python (and R), KNN advantages, Working on use case.
• Decision Trees : Theory, Implementing in Python (and R), Decision |Tree Pros and Cons, Working on use case.

Module 4: Unsupervised Learning

• K-Means Clustering: Theory, Euclidean Distance method.
• K-Means hands on with Python (and R)
• K-Means Advantages & Disadvantages

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.

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

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.

1. 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

1. Introduction to R

• What is R?

• Why R?

• Installing R

• R environment

• How to get help in R

• R Studio Review

1. R Packages

• Data Types

• Variable Vectors

• Lists

• Environment Setup

• Array

• Matrix

• Data Frames

• Factors

• Loops

• Functions

• Packages

• In-Built Datasets

1. R Basics

2. Importing data

3. Manipulating data

4. Statistics Basics

5. Error metrics

6. Machine Learning

7. Supervised Learning

8. Unsupervised Learning

9. Machine Learning using R

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