Data Science Training Institutes in Bangalore

 

Introduction of Data Science

•             What is Analytics?

•             Different types of Analytics.

•             Importance of Analytics in Business

•             Organizations sing Business Analytics

DATA & ANALYTICS

•             Data Dictionary Data Types

•             Data Handling

INTRODUCTION OF EXCEL ENVIRONMENT

•             Understanding of data calculations

•             in excel.

•             Formatting of data calculation

•             formatting.

•             MS Excel functions

•             Understanding about Sorting,

•             Filtering & Validation MS

•             Excel charts

•             Pivot Table

•             Understanding of Data Tools Panel.

•             Basics of Macro Recording

 

DASHBOARD DESIGNING IN EXCEL

•             Introduction to Dashboards

•             Trends ,and Scenario using charts

•             Advanced charting Techniques

•             Designing Sample Dashboard

•             using from controls Tips and Tricks

•             to enhance dashboard designing.

BASICS OF STATISTICS

•             Data types and its measures.

•             Random Varieties it’s application

•             with exercises. Probability

•             Applications with examples.

•             Probability distribution with example.

•             Various graphic representation with

•             data for analysis Continuous

•             probability distribution

•             Discrete probability distribution

•             Computing probability from

•             normal distribution Central limit

•             theorem for sampling.

INTRODUCTION TO DATA SCIENCE

•             What is Analytics and Data Science

•             Overview of Analytics and Data Science

•             Why is Analytics becoming popular now?

•             Application of Analytics in business

•             Analytics vs Data warehousing ,

•             MIS Reporting Various

•             Terminology in Analytics

•             Various Analytics Methodology

•             How Businesses are using the power of Analytics?

•             Various Analytics tools and their usage

BUSINESS STATICS AND APPLICATIONS

•             Sample V/S Population Probability Theory

•             Probability Distribution Concepts

•             Types of Distribution

•             Data Description – Numerical Measures of Central Tendency Data

•             Description – Numerical Measures of Variability

•             Inferential Statistics

•             Concepts of Hypothesis testing

•             Statistical Methods – Z/t –

•             tests , ANOVA , Correlations and

•             Chi Square

 

FUNDAMENTALS OF PYTHON

•             Installation of Python

•             Getting started with Python

•             History of Python Features of python

•             Variable Operators in Python

•             Reading and Writing data files to

•             PythonWorking with

•             Python data frames

•             Python Functions and Loops

•             Special utility functions

•             Merging and Sorting data

DATA IMPORTING/EXPORTING IN PYTHON

•             Concepts of Packages

•             Data Structure & Data Types

•             Importing Data from various sources

•             ( txt, dlm, excel, csv etc ) Database Input

•             Exporting data to various formats

•             Viewing Data

•             Variable & Value Labels

 

INTRODUCTION TO PYTHON

•             Introductory remark about python. A brief

•             History of python.

•             How python is different from other languages

•             Python versions.

•             Installing python

•             IDLE

•             How to execute python

•             Writing you first program

PYTHON BASICS

•             Python keywords and identifiers.

•             Python statements

•             Comments in python

•             Command line

•             arguments Getting use input

•             Exercise

NUMPY PACKAGE

•             What is Numpy? Importing Numpy

•             Numpy overview

•             Numpy Array creation and basic

•             operation

•             Numpy universal function

•             Selecting and retrieving data

•             Data slicing

•             Iterating Numpy Data Shape

•             Manipulation, Stacking and

•             Splitting Arrays

•             Copies and Views: no copy,

•             shallow copy, deep copy

•             Indexing : Arrays of indices,

•             Boolean Arrays

DATA MANIPULATION USING PANDAS

•             Data Alignment

•             Sorting and Ranking Summary

•             Statistics Missing Values

•             Merging data Concatenation

•             Combining DataFrames Pivot

•             Duplicates Binning

 

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

PYTHON ADVANCE: DATA MUGGING WITH PANDAS

•             Applying functions to data

•             Histogramming

•             String methods

•             Merge Data: Concat, Join and Append

•             Grouping and Aggregation

•             Reshaping

•             Analysing Data for missing values

•             Filling missing values: fill with constant

•             forward filling, mean Removing

•             Duplicates

•             Transforming Data

PYTHON ADVANCE: VISUALIZATION WITH MATPLOTLIB

•             Anatomy of a MatplotLib Plot

•             Matplotlib basic plots and it’s containers

•             A Matplotlib figure, it’s

•             components and

•             properties Axes and other

•             graphical objects

PYLAB AND PYPLOT

•             Data for Matplotlib Plots

•             What is a Subplot?

•             Modifying size of figures

•             Plotting routines with pyplot

•             Customizing your pyplot

•             Deleting an Axes

•             Setting up Plot Title, Axes Labels,

•             Legend,

•             Layout Showing, Saving and

•             Closing your Plot

•             Save a Plot to an image file and

•             pdf file

•             Use cla(), clf() or close

 

PREDICTIVE MODELLING CONCEPTS

•             How to use Predictive Modelling in

•             R Linear Regression

•             Logistic Regression Model Selection

•             Scoring

•             Predictive Modelling Techniques

•             ( Decision Trees , CART , Neural Networks ,

•             Deep Learning )

•             Different phases of Predictive Modelling

•             Business Case Study ( Predictive Modelling )

 

CORRELATION AND LINEAR REGRESSION

•             Correlation

•             Simple Linear Regression Multiple

•             Linear Regression

•             Building Linear Regression Model Model

•             Diagnostic and Validation Working on

•             Case Study

LOGISTIC REGRESSION

•             Moving from Linear to Logistic Regression

•             Model assumptions and odds ration

•             Building Logistic Regression Model

•             Validation of Logistic Regression Model

•             Model assessment and gains table

•             ROC curve and KS statistics Case Study

•             Time Series Forecasting

•             What are time-series? Need for forecasting

•             Trends , Seasons , Cycles

•             Basic Techniques – Averages,

•             Smoothening etc.

•             Advanced Techniques – AR Models, ARIMA etc.

•             Case Study

INTRODUCTION TO MACHINE LEARNING

•             What is Machine Learning.

•             History of Machine

•             Learning How artificial

•             intelligence relates to

•             machine learning

•             Data science vs Machine

•             Learning Fundamentals of

•             Machine Learning

MACHINE LEARNING CONCEPTS

•             Branches of Machine Learning

•             Different phases of Machine

•             learning modeling

•             Data preparation for

•             modelling Train test split

•             Evaluation of the model

SUPERVISED LEARNING

•             Classification

•             KNN

•             Logistic

•             Regression

•             Naïve

•             Bayes SVM

•             Decision Tree

•             Regression

•             Linear

•             Regression SVR

•             KNN Ridge

•             Regression

•             Model Complexity

•             Generalization

•             Performance

•             Connection between Model

•             complexity and Generalization

•             Performance Importance of feature

•             Scaling Regularization

•             Cross validation for model evaluation

EVALUATION

•             Understanding of Evaluation and model

•             selection methods Optimize the

•             performance of Machine Learning models

ADVANCED SUPERVISED MACHINE LEARNING CONCEPTS

•             Ensemble Learning

•             Learning critical problem of

•             data leakage in machine

•             learning and how to detect

•             and avoid it

UNSUPERVISED LEARNING

•             K-Means clustering

•             Recommendation Engines

UNSUPERVISED LEARNING – CLUSTERING

•             K-Means Clustering

•             Document retrieval : A case study in

•             clustering and measuring similarity

•             Un Recommending Products

UNSUPERVISED LEARNING – DEEP LEARNING

•             Meaning and importance of deep learning

•             Artificial Neural Networks

•             Introduction to Tensorflow

REINFORCEMENT LEARNING

•             INTRODUCTION

•             Reinforcement Learning

•             Overview

•             Markov Decision Process (MDP)

•             MDP - Finding Optimal Policy

•             Example of an MDP and

•             Bellman Equations

TEXT MINING

•             Introduction to Text Mining concepts

•             Finding patterns in text : text mining , text as

•             a graph Natural Language Processing (NLP)

•             Sentiment Analysis with R Word Cloud

•             analysis using R

•             Application of Social media analytics

•             Collecting twitter data with twitter API

•             Feature engineering with text data

•             Fine tuning the models using hyper

•             parameters, grid search, piping etc.

•             Case Study