Data Science Training

Data Science Training in Chennai

Tlead  Computer Education  is a brand and providing quality Data science Training Institute in Chennai  through online and classroom to students in world wide. This data science course in Chennai  includes the concepts and required tools through out the entire data science pipeline, by asking appropriated queries to making interference and publishing results. After completing your data science training with our project, you will apply the learned skills by building a data product using real world data.

To take data science training you require some programming experience in any language and working knowledge of mathematics up to algebra helps to understand concepts easily.

Why should you take data science training?

You may any kind of professional you can’t escape from big data science.To manage large number of data, data scientists are needed who are the most trained professionals.Processing data gives power to companies to study,research and analyze to improve services.The Tlead is one best data scientist training institute in Hyderabad has real time faculty with years of experience.A new report from McKinsey Global Institute (MGI) estimates that “big data analytics could increase annual GDP in retail and manufacturing in US by up to $325 billion by 2020. By 2018, US will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts who can handle big data”.This study tells itself the requirement for data science professionals.

Introduction to Data Science

-Get Inspired

-Data Science life cycle

-Different types of Data Science  Tasks

 

Business Statistics

-Probability Refresher

-Descriptive Statistics

-Measures of Central Tendency

-Measures of Spread

-Distribution

-Different Types of Distributions

-Normal,Binomial and Poisson

-Probability Density Functions

-Probability Density Functions

-Characteristics of Normal Distribution Sampling

-Sampling Distribution

-Inferential Statistics

-Hypothesis Tesing(T-test,chi-square)

-Analysis of Variance

-Measures of Relationship

-Correlation,Covariance,Associations

and odds Ratio

 

Introduction to R-Programming

-R and R-Studio Installation

-Data types and Data Structures

-Arithematic,Logical operations

-Conditional Statements

-Loops

-Packages and Functions in R

-Data Frame Operations

-Getting Data into R From Flat Files

-Connecting to Databases

-Data  Inspection and Manipulation

-Data Wrangling and Data Munging

Practice Exercises

Supervised Learning

-Steps in supervised learning

-Regression and Classification

-Training,validation and Testing

-Measures of Performance

-R-Square,Rmse For Regression

-Confusion Matrix

-Accuracy,Precision and Recall

-F-1 Score

-Sensitivity And Specificity

-Roc And Auc

 

Linear Regression

-Simple Linear Regression

-Cost Functions

-Sum of Least Squares

-Variable Selection

-Model Development And Improvement

-Mode Validation And Diagnostics

-Gradient Descent Approach

 

Classification Logistic Regression

-Variable Selection Methods

-Forward,Backward and Stepwise

-Model Development and Validation

-Measurements of Accuray

-Interpretation And Implementation

 

Decision Trees

-Rule Based Learning

-Construction Of Rules

-Decision Nodes VS Leaf Nodes

-Choosing Variables For Decision Nodes

-Measures of Impurity

-Entropy,Gini Index And Information Gain

-Overfitting And Pruning

 

Tex Mining

-Unstructured Data

-Text Analytics

-Cleaning Text Data

-Tokenization

-Pre Processing

-Word Counts and Word Clouds

-Sentiment Analysis

-Text Classification

-Distance Measures

-Natural Language Processing(NLP)

 

Introduction To Deep Learning

Probabilistic Methods Introduction

-Naive Bayes

-Joint And Condition Probabilities

-Classification using Naive Bays Approach

 

Support Vector Machines

-Maximum Margin Classifier

-Support Vector Classifier

-Support Vector Machines

-Kernels-Linear And Non Linear

 

Neural Networks

-Network  Topology

-Feed Forward and Back Propagation Models

 

Association Rules

-Market Basket Analysis

-APRIORI

 

Recommender Systems

-Matrix Factorization

-Collabrorative Filtering

-User Based Collaborative Filtering

-Item Based Collaborative Filtering

 

Exploratory Data Analysis And Visualization

-Summary Statistics

-Data Distributions

-Data Transformations

-Outlier Detection And Management

-Charts and Graphs

-One Dimensional Charts

-Histogram

-Barchart

-Box Plots

-Two Dimensional

-Scatter Plots

-Bar Charts(Stacks and Dodge)

-Box Plots

-Multi-Dimensional Plots

-Inference and Variable Selection

-Fancy Charts -Bubble Charts, Word Clouds Etc.

 

Data Pre-Processing

-Data Types and Conversions

-Bining  And Normalization

-Min-Max Scaling

-Imputation

-Dimensionality Reduction

 

Bagging And Random Forest

-Resampling Methods

-Resampling Methods without  Replacement

-Resampling Methods with Replacement

-Random Forests

 

Boosting

-Adaboost

-Gradient Boosting -GBM

-Extreme Gradient Boosting -Xgboost

 

Cross Validation

-Leave one out Cross validation

-K-Fold Cross Validation

-Cross Validation Usage

-Bias And Variance

 

Unsupervised Learning

Clustering(Segmentation)

 

-Hierarchical  clustering

-K-means Clustering

-Cluster profiling

 

Dimensionality Reduction  Techniques

-Principal Components analysis

-Singular Value Decomposition(svd)

 

Factor Analysis

Optimization

-Simplex Method

-Integer  Programming

-Introduction To Game Theory

 

Forecasting

-Time Series

-Components of Time Series

-Trend ,seasonality, Randomness

-Addictive And Multiplicative

-Moving Averages

-Exponential Smoothing

-Arima,Arimax

-Arch and Garch

 

Introduction to Python for Data Science

-Python programming Introduction

-Data types and Data Structures

-Control Statements

-Functions

-User defined Functions

-Python Packages

-Numpy, Pandas ,Matplotlib

Machine Learning In Python

-Scikit-Learn

-User Cases And Assignments

Introduction to Big Data  Analytics

-Hadoop:  Distributed File System

-Mapreduce

-Hive and HBase

-Spark sql,Spark Mllib

Mangodb Connection

Machine Learning  wth Spark

-Spark context and Hive Context

-Dataframes on Spark

-Scala introduction

-Sparkr

-Pyspark

-Machine Learning use Cases on Spark

Proof of Concepts And use Cases

-Deploying Models On Production

-Machine Learning on Cloud  Platforms

-Aws And Microsoft Azure

 

Capstone Project

100% Training & Placement Company