Data Science Training in Hyderabad

This course is an introduction to Data Science and Statistics using the R programming language with Python training in Hyderabad.. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R and Python. If you’re new to Python, don’t worry – the course starts with a crash course. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems.

Genius IT provides comprehensive Data Science Course in Hyderabad with extensive statistical concepts, wide-ranging Machine Learning classes in Hyderabad and unlimited hands-on practice sessions in R and Python along with adequate placement support post completion. Later one may also opt for project internship programmer, to acquire multiple real-life project experience along with supporting project experience certificate, which helps strengthening the credential and assisting in placement further. Faculties at Genius IT are senior Data Scientists from the industry with extensive implementation experience and most of them are qualified from premium institutions like IIT, IIM, IIS, BITS-Pilani etc.

Introduction to Data Science

Introduction to Data Analytics

Introduction to Business Analytics

Understanding Business Applications

Data types and data Models

Type of Business Analytics

Evolution of Analytics

Data Science Components

Data Scientist Skillset

Univariate Data Analysis

Introduction to Sampling

Basic Operations in R Programming

Introduction to R programming

Types of Objects in R

Naming standards in R

Creating Objects in R

Data Structure in R

Matrix, Data Frame, String, Vectors

Understanding Vectors & Data input in R

Lists, Data Elements

Creating Data Files using R

Data Handling in R Programming

Basic Operations in R – Expressions, Constant Values, Arithmetic, Function Calls, Symbols

Sub-setting Data

Selecting (Keeping) Variables

Excluding (Dropping) Variables

Selecting Observations and Selection using Subset Function

Merging Data

Sorting Data

Adding Rows

Visualization using R

Data Type Conversion

Built-In Numeric Functions

Built-In Character Functions

User Built Functions

Control Structures

Loop Functions

Introduction to Statistics

Basic Statistics

Measure of central tendency

Types of Distributions

Anova

F-Test

Central Limit Theorem & applications

Types of variables

Relationships between variables

Central Tendency

Measures of Central Tendency

Kurtosis

Skewness

Arithmetic Mean / Average

Merits & Demerits of Arithmetic Mean

Mode, Merits & Demerits of Mode

Median, Merits & Demerits of Median

Range

Concept of Quantiles, Quartiles, percentile

Standard Deviation

Variance

Calculate Variance

Covariance

Correlation

Introduction to Statistics – 2

Hypothesis Testing

Multiple Linear Regression

Logistic Regression

Market Basket Analysis

Clustering (Hierarchical Clustering & K-means Clustering)

Classification (Decision Trees)

Time Series Analysis (Simple Moving Average, Exponential smoothing, ARIMA+)

Introduction to Probability

Standard Normal Distribution

Normal Distribution

Geometric Distribution

Poisson Distribution

Binomial Distribution

Parameters vs. Statistics

Probability Mass Function

Random Variable

Conditional Probability and Independence

Unions and Intersections

Finding Probability of dataset

Probability Terminology

Probability Distributions

Data Visualization Techniques

Bubble Chart

Sparklines

Waterfall chart

Box Plot

Line Charts

Frequency Chart

Bimodal & Multimodal Histograms

Histograms

Scatter Plot

Pie Chart

Bar Graph

Line Graph

Introduction to Machine Learning

Overview & Terminologies

What is Machine Learning?

Why Learn?

When is Learning required?

Data Mining

Application Areas and Roles

Types of Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement learning

Machine Learning Concepts & Terminologies

Steps in developing a Machine Learning application

Key tasks of Machine Learning

Modelling Terminologies

Learning a Class from Examples

Probability and Inference

PAC (Probably Approximately Correct) Learning

Noise

Noise and Model Complexity

Triple Trade-Off

Association Rules

Association Measures

Regression Techniques

Concept of Regression

Best Fitting line

Simple Linear Regression

Building regression models using excel

Coefficient of determination (R- Squared)

Multiple Linear Regression

Assumptions of Linear Regression

Variable transformation

Reading coefficients in MLR

Multicollinearity

VIF

Methods of building Linear regression model in R

Model validation techniques

Cooks Distance

Q-Q Plot

Durbin- Watson Test

Kolmogorov-Smirnof Test

Homoskedasticity of error terms

Logistic Regression

Applications of logistic regression

Concept of odds

Concept of Odds Ratio

Derivation of logistic regression equation

Interpretation of logistic regression output

Model building for logistic regression

Model validations

Confusion Matrix

Concept of ROC/AOC Curve

KS Test

Market Basket Analysis

Applications of Market Basket Analysis

What is association Rules

Overview of Apriori algorithm

Key terminologies in MBA

Support

Confidence

Lift

Model building for MBA

Transforming sales data to suit MBA

MBA Rule selection

Ensemble modelling applications using MBA

Time Series Analysis (Forecasting)

Model building using ARIMA, ARIMAX, SARIMAX

Data De-trending & data differencing

KPSS Test

Dickey Fuller Test

Concept of stationarity

Model building using exponential smoothing

Model building using simple moving average

Time series analysis techniques

Components of time series

Prerequisites for time series analysis

Concept of Time series data

Applications of Forecasting

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