#### Data Science Training

**what is data science?**

Data science is a multidisciplinary blend of data inference, algorithm and technology in order to solve analytically complex problems. At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses.development(Data Science training in gachibowli Hyderabad)

**why you want to learn Data science course?**

The number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings by 2020 - Forbes Businesses analysing data will see $430 billion in productivity benefits over their rivals not analysing data by 2020.(Data science training in kondapur )

**About Data Science Training Course**

This is a complete Data Science Bootcamp specialization training course from qshore technologies that provides you detailed learning in data science, data analytics, project life cycle, data acquisition, analysis, statistical methods and machine learning.(Data science training in Kondapur)You will gain expertise to deploy Recommenders using R programming, data analysis, data transformation, experimentation and evaluation.(Data science training in gachibowli Hyderabad)

**What you will learn in this Data Science Course?**

*Deploying recommender

*Big Data fundamentals and Hadoop integration with R

*Predictive analytics, segmentation using clustering

*Different algorithms used in Machine Learning

*Experimentation, evaluation and project deployment tools

*Data acquisition and Data Science lifecycle

*Data Science introduction systems on importance real-world data sets

*Work on data mining, data structures, data manipulation.

*Data Scientist roles and responsibilities.

**Who should take this Data Science Course? **

Those looking to take up the roles of Data Scientist and Machine Learning Expertinterest in stats and topics related to it ...statisticians, developers looking to master machine learning and predictive analytics, Data Science training in gachibowli, Everyone can learn data science, its that you need to have Big data, business intelligence and business analyst professionals, information, architects, (Data science training in kondapur),(Data science training in Madhapur Hyderabad)

**Where do I learn Data Science or R Programming or Data Analytics in Hyderabad? **

** ****… At Qshore Technologies **The trendy and hot software technology, Data Science with R Programming and Data Analytics is taught in Qshore Technologies, which is a corporate training company in Gachibowli|Kondapur. Data Science Course at Qshore Technologies is considered to be the best course in Business Analytics and BigData Analytics. Visit Qshore Technologies for the Best Data Science and R Programming Training in Hyderabad.(Data science training in gachibowli Hyderabad).

**What is the Data Science course market trend in Hyderabad?**

There is a huge market for professionals who have qualifications in various Data Science aspects like statistical computing, Data Pre-processing, Business statistics and more.

.The market for Data Science in Hyderabad is booming as getting the right insights at the right time is no longer optional but critical to business success. Machine learning, advanced analytics, Data Pre-processing, Business statistics and more.(Data science training in kondapur Hyderabad).

**what do we provide in our training?**

Keynotes During Training

Real Time Project Studies

Soft copy materials

Flexible Timings

Affordable Cost

Trainer Support

Technical Support

Certificate of Completion

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Rating:4.9

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Review:

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#### Curriculum

**Here you can download the course and schedule for Data Science Training Download PDF**

• What is Data Science? – Introduction.

• What background is required?

• Why Data Science?

• Importance of Data Science.

• Demand for Data Science Professional.

• Brief Introduction to Big data and Data Analytics.

• Lifecycle of data science.

• Tools and Technologies used in data Science.

• What is Machine Learning?

• Different types of Data Science Tasks.

• Descriptive statistics and Inferential Statistics

• Sample and Population

• Variables and Data types

• Percentiles

• Measures of Central Tendency

• Measures of Spread

• Skeweness, Kurtosis

• Degrees of freedom

• Variance, Covariance, Correlation

• Standardization/Scaling

• Probability

• Expected of ‘x’

• Sampling Distribution

• Standard Probability Distribution Functions

• Bernoulli, Binomial, Normal distributions

• Standard Normal Deviate

• Decision Making Rules

• Test of Hypothesis

• One sample t-Test, Chi-square

• Two sample t-Test Analysis of Variance (ANOVA)

• Summary Statistics

• Data Transformations

• Outlier Detection and Management

• Charts and Graphs

• One Dimensional Chart

• Box plots

• Bar graph

• Histogram

• Scatter plots

• Multi-Dimensional Charts

• Fancy Charts - Bubble charts

• Summary Statistics

• Data Transformations

• Outlier Detection and Management

• Charts and Graphs

• One Dimensional Chart

• Box plots

• Bar graph

• Histogram

• Scatter Plots

• Multi-Dimensional Charts

• Fancy Charts - Bubble charts

? Simple Linear Regression

? Multiple Linear Regression

? Estimation of Model Parameters

? Hypothesis Testing in Multiple Linear Regression

? Extra sum of squares

? R – Square, R- Square Adjusted

? Variable Selection

a. All Possible Regressions

b. Sequential Selection (Forward, Backward, Stepwise)

? Multicollinearity – VIF

? Residual Analysis/Regression Diagnostics.

? Polynomial Regression

? Transformations

a. Bulging Rules

b. Box Tidwell

c. Box cox

d. Weighted Least Square

? Dummy variables

a. General Concepts of Indicator variables.

? Predicted Error sum of squares (PRESS)

? Assessing Performance

a. Variance Biased Trade-off

b. Resampling Methods

c. Cross Validation

d. Leave one out Cross validation

e. k-Fold Cross Validation

f. Bootstrap

? Logistic Regression

A Case Study will be presented on Logistic Regression

Introduction to Supervised and unsupervised Learning

? Neural Networks

a. Network Topology

b. Single Layer Perceptron

c. Multi-Layer perceptron

d. Feed forward and Back propagation Models

? Introduction to Deep Learning

? Association Rules

a. Market Basket Analysis

b. APRIORI

c. Support, Lift, Confidence

? Nearest-Neighbour Methods (KNN – Classifier)

a. Euclidian Distance

b. Hamming Distance

? Decision Tree

a. Finding Root Node, Intermediate Nodes, Terminal Nodes

b. Construction of Rules

c. Miss classification

d. Gini Index

e. Overfitting and Prunning

f. Regression Trees

? Boosting, Bagging and Random Forest

a. Resampling Methods

b. Resampling methods with Replacement

c. Resampling methods without Replacement

d. Random Forest

? Dimensional Reduction Techniques

1. Principle Component Analysis

a. Eigen values and Eigen Vectors

2. Cluster Analysis

a. Hierarchal Clustering

b. Linkage Methods

c. Non- Hierarchal Clustering

d. K-Means Clustering

? Text Mining / Natural Language processing

a. Unstructured Data

b. Text Analytics

c. Cleaning Text data

d. Tokenization

e. Pre-processing

f. Word counts and word clouds

g. Sentiment Analysis

h. Text classification

i. Distance measures

? Introduction to probabilistic methods Introduction

a. Naive Bayes

b. Joint and Condition probabilities

c. Classification using Naive Bayes Approach

? Support Vector Machines

a. Maximum Margin Classifier

b. Support vector Classifier

c. Support vector machines

d. Kernels – Linear and Non-Linear

? PYTHON - PROGRAMMING

• How to install python (Anaconda)

• How to install sciKit Learn (Anaconda)

• How to work with Jupyter Notebook

• How to work with Spyder IDE

• Strings

• Lists

• Tuples

• Sets

• Dictionaries

• Control Flows

• Functions

• Formal/Positional/Keyword arguments

• Predefined functions (range, len, enumerates etc…)

• Data Frames

• Packages required for data Science in Python

• Lab/Coding

• One-dimensional Array

• Two-dimensional Array

• Pr-defined functions (arrange, reshape, zeros, ones, empty)

• Basic Matrix operations

• Scalar addition, subtraction, multiplication, division

• Matrix addition, subtraction, multiplication, division and transpose

• Slicing

• Indexing

• Looping

• Shape Manipulation

• Stacking

• Series

• DataFrame

• df.GroupBy

• df.crosstab

• df.apply

• df.map

What is Spark

Introduction to Spark RDD

Introduction to Spark SQL and Data frames

Using R-Spark for machine learning

Hands-on:

installation and configuration of Spark

Hands-on Spark RDD programming

Hands on of Spark SQL

Dataframe programming

Using R-Spark for machine learning programming

1. Getting R

1.1 Downloading R

1.2 R Version

1.3 32-bit versus 64-bit

1.4 Installing

2. The R Environment

2.1 Command Line Interface

2.2 RStudio

3. R Packages

3.1 Installing Packages

3.2 Loading Packages

4. Reading Data into R

4.1 Reading CSVs

4.2 Excel Data

4.3 Clipboard

5. Advanced Data Structures

5.1 Data.frames

5.2 Lists

5.3 Matrices

5.4 Arrays

5.5. Factors

6. Basics of R

6.1 Basic Math

6.2 Variables

6.3 Data Types

6.4 Vectors

6.5 Calling Functions

6.6 Function Documentation

6.7 Missing Data

7. Control Statements

7.1 if and else

7.2 switch

7.3 if else

8. Loops

8.1 for Loops

8.2 while Loops

8.3 Controlling Loops

9. Group Manipulation

9.1 Apply Family

9.2 aggregate

10. Data Reshaping

10.1 cbind and rbind

10.2 Joins

10.3 Reshape2

11. String Theory

11.1 paste

11.2 sprintf

11.3 Extracting Text/ Regular Expressions

12. Graphs with R and GGPlot2

12.1 Basic and Interactive Plots

12.2 Dendrograms

12.3 Pie Chart and Its Alternatives

12.4 Adding the Third Dimension

12.5 Visualizing Continuous Data

13. Basic Statistics

13.1 Summary Statistics

13.2 Correlation and Covariance

13.3 T-Tests

13.4 ANOVA

14. Probability Distributions

14.1 Normal Distribution

14.2 Binomial Distribution

#### About Instructors

**Mr.Advaith**

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