## SCOPE OF DATA SCIENCE

Data Science is one of the fastest evolving fields & a Data Scientist’s job is one of the fastest growing and highest paid in tech.

As there is a cut-throat competition in the market, top organizations are turning their minds to data analytics to identify new market opportunities to design their services and products. Surveys show that 75% of top organizations consider data analytics an essential component of business performance. This is where data scientists come in. Data scientists know how to use their skills in math, statistics, programming, and other related subjects to organize large data sets. Then, they apply their knowledge to uncover solutions hidden in the data to take on business challenges and goals. They are thus able to contribute to their organization’s business goals. So, learning data science through effective training can give you a bright future.

“Data Scientist” has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! It is a rewarding career that allows you to solve some of the world’s most interesting problems!

As per Payscale.com, a Data Scientist (IT) with Big Data Analytics skills earns an average salary of Rs 706,750 per year in India.

## COURSE OBJECTIVE

Xilytica is delighted to offer classroom program for candidates to help them build a career in the blooming field of Data Science.The program focuses on building foundation skills in business analytics and data science with in depth training of statistical platform & tools.

We will develop candidates to think analytically and solve business problems using data.

Our innovative approach to analytics training by combining deep knowledge and collaborative learning environment will help candidates to develop real skills in analytics.

Candidates taking this course can expect to gain the knowledge of analytical techniques required by organizations to take strategic decisions, and by solving case studies they will know how analytical techniques can be used to get real insights from real data. Candidates will be able to forecast sales, Identify customer segments, identify drivers of sales/profit, perform regression, analyze customer comments and do much more after the course.

In-depth course coverage, hands-on experience of entire data analytics project cycle, and case studies on real world analytics problems, cutting across different domains are the high points that make Skill Venue’s Data Science in R certification course a leap towards a successful analytics career.

## WHO SHOULD TAKE THIS COURSE

- IT professionals looking for career or technology change to Data Science & Analytics.
- BPO industry or non technical professionals who are looking for career in Data Science & Analytics.
- Engineering graduates who want to build a career in Data Science & Analytics.
- Non- technical & MBA graduates who are interested in building a career in Data Science.
- Anyone who likes manipulating data & loves getting insights out of data.
- Prerequisite – There are not prerequisite for this course except hard work & dedication.

### Course Curriculum

Introduction | |||

Introduction to the Course | 00:00:00 | ||

Overview Of Course Curriculum | 00:00:00 | ||

How To Ace Data Science- Pointers | 00:00:00 | ||

What Is Data Science | 00:00:00 | ||

Introduction to Programing Language | |||

What is Programming Language | 00:00:00 | ||

Ecosystem Of Any Programming Language | 00:00:00 | ||

Windows Installation Set-Up | |||

How to Install R & R Studio on Windows Operating System | 00:00:00 | ||

Linux Installation Set-Up | |||

How to Install R & R Studio on Linux Operating System | 00:00:00 | ||

Mac OS Installation Set-Up | |||

How to Install R & R Studio on Mac OS Operating System | 00:00:00 | ||

Introduction to Basic R | |||

Introduction to R Basics | 00:00:00 | ||

Arithmatic Operations in R | 00:00:00 | ||

Variables | 00:00:00 | ||

R Basic Data Types | 00:00:00 | ||

Vector Basics | 00:00:00 | ||

Vector Operations | 00:00:00 | ||

Vector Indexing & Selecting | 00:00:00 | ||

Getting Help in R & R Studio | 00:00:00 | ||

Comparison Operators | 00:00:00 | ||

Quiz 01 | 00:00:00 | ||

R Matrices | |||

Introduction to R matrices | 00:00:00 | ||

Creating a Matrix | 00:00:00 | ||

Arithmetic Operations In Matrix | 00:00:00 | ||

Matrix Operations | 00:00:00 | ||

Matrix Indexing & Selecting | 00:00:00 | ||

Factor & Categorical Matrices | 00:00:00 | ||

Quiz 02 | 00:00:00 | ||

R Data Frames | |||

Introduction to R Data Frames | 00:00:00 | ||

Data Frame Basics | 00:00:00 | ||

Data Frame Indexing & Selecting | 00:00:00 | ||

Data Frame Operations | 00:00:00 | ||

Quiz 03 | 00:00:00 | ||

List in R | |||

Introduction To Lists in R | 00:00:00 | ||

Creating Lists With Different Objects | 00:00:00 | ||

Selecting Elements of a List | 00:00:00 | ||

Quiz 03 | 00:00:00 | ||

Data Input & Output with R | |||

Introduction to Data Input & Output with R | 00:00:00 | ||

Reading Data from CSV Files | 00:00:00 | ||

Writing Data to CSV Files | 00:00:00 | ||

Reading Data from Excel Files | 00:00:00 | ||

Writing Data to Excel Files | 00:00:00 | ||

SQL with R | 00:00:00 | ||

Web Scraping with R | 00:00:00 | ||

Quiz 04 | 00:00:00 | ||

R Programming basics | |||

Introduction to Programming Basics | 00:00:00 | ||

Logical Operators | 00:00:00 | ||

If, Else, Else If Statements | 00:00:00 | ||

While Loop | 00:00:00 | ||

For Loop | 00:00:00 | ||

Functions | 00:00:00 | ||

Quiz 05 | 00:00:00 | ||

R programming Advance | |||

Introduction to Advance R Programming | 00:00:00 | ||

Built In R functions | 00:00:00 | ||

Apply Fucntion | 00:00:00 | ||

Math Functions with R | 00:00:00 | ||

Regular Expressions | 00:00:00 | ||

Dates & Times Stamps | 00:00:00 | ||

Exceptions And Debugging In R | 00:00:00 | ||

Quiz 06 | 00:00:00 | ||

Data Exploratory & Data Wrangling | |||

Data Manipulation with R Overview | 00:00:00 | ||

Guide to Using Dplyr | 00:00:00 | ||

Pipe Operator | 00:00:00 | ||

Guide to Using Tidyr Package | 00:00:00 | ||

Making Data Fit For Analysis Using Spread, Gather, Separate & Unite Functions | 00:00:00 | ||

Working With Data & Time | 00:00:00 | ||

Manipulating Strings | 00:00:00 | ||

Understanding Messy Data | 00:00:00 | ||

Tackling Missing Data | 00:00:00 | ||

Dealing With Outliers | 00:00:00 | ||

Case Study On EDA – Exploratory Data Analysis | 00:00:00 | ||

Quiz 07 | 00:00:00 | ||

Data Visualization with R | |||

Overview Of Ggplot2 – Grammar Of Graphics | 00:00:00 | ||

Different Layers Of Visualisation Ggplot2 | 00:00:00 | ||

7 Layers Of Ggplot2 – Data, Aesthetics, Geometries, Facets, Statistics, Coordinates, Themes | 00:00:00 | ||

Histograms | 00:00:00 | ||

Scatterplots | 00:00:00 | ||

Barplots – Simple, Stacked, Dodge | 00:00:00 | ||

Boxplots | 00:00:00 | ||

Line Charts | 00:00:00 | ||

Pie Charts, Coxcomb Plot | 00:00:00 | ||

2 Variable Plotting | 00:00:00 | ||

Visualisation For Exploratory Analysis | 00:00:00 | ||

Visualisation Best Practices | 00:00:00 | ||

Case Study On Visualisation | 00:00:00 | ||

Quiz 08 | 00:00:00 | ||

Statistics 1 | |||

Introduction To Statistics | 00:00:00 | ||

Understanding Types Of Data | 00:00:00 | ||

Measure Of Centers – Mean, Mode & Median | 00:00:00 | ||

Measure Of Spread | 00:00:00 | ||

Probability | 00:00:00 | ||

Continuous Probability Distribution | 00:00:00 | ||

Normal Distribution – Z Distribution | 00:00:00 | ||

F Distribution | 00:00:00 | ||

Student’s T Distribution | 00:00:00 | ||

Chi Square Distribution | 00:00:00 | ||

Discrete Probability Distribution | 00:00:00 | ||

Binary Distribution | 00:00:00 | ||

Poisson Distribution | 00:00:00 | ||

Statistics 2 | |||

Point Estimation | 00:00:00 | ||

Confidence & Significance Levels | 00:00:00 | ||

Hypothesis Testing | 00:00:00 | ||

Types Of Hypothesis | 00:00:00 | ||

Parametric Test | 00:00:00 | ||

One Sample, Two Sample T Test | 00:00:00 | ||

One Sample Z Test | 00:00:00 | ||

One Proportion, Two Proportion Test | 00:00:00 | ||

One Way Anova | 00:00:00 | ||

Chi-square Test | 00:00:00 | ||

Non – Parametric Test | 00:00:00 | ||

One Sample Sign Test | 00:00:00 | ||

Mann – Whitney Test | 00:00:00 | ||

Kruskal – Wallis Test | 00:00:00 | ||

Mood’s Median Test | 00:00:00 | ||

Wilcoxon Signed Rank Test | 00:00:00 | ||

Friedman’s Test | 00:00:00 | ||

Hypothesis Testing For Population Means | 00:00:00 | ||

Hypothesis Testing For Population Variance | 00:00:00 | ||

Hypothesis Testing For Population Proportions | 00:00:00 | ||

Quiz 09 | 00:00:00 | ||

Machine Learning Toolbox | |||

Introduction To Machine Learning With R | 00:00:00 | ||

Types Of Machine Learning | 00:00:00 | ||

Supervised Learning | 00:00:00 | ||

Unsupervised Learning | 00:00:00 | ||

Reinforcement Learning | 00:00:00 | ||

Regression Models | 00:00:00 | ||

Classification Models | 00:00:00 | ||

Quiz 10 | 00:00:00 | ||

Data Pre-processing | |||

Dealing With Missing Data | 00:00:00 | ||

Dealing With Categorical Data | 00:00:00 | ||

Feature Scaling Data | 00:00:00 | ||

Splitting Data Into Training & Validation & Test Sets | 00:00:00 | ||

K – Fold Cross Validation | 00:00:00 | ||

Quiz 11 | 00:00:00 | ||

Linear Regression | |||

Introduction To Regression Models | 00:00:00 | ||

Correlation Between Two Variables | 00:00:00 | ||

Visualising Correlation Using Corrgram And Corrplot | 00:00:00 | ||

Simple Linear Regression | 00:00:00 | ||

Multiple Linear Regression | 00:00:00 | ||

Non Linear Regression | 00:00:00 | ||

Training & Evaluating Regression Models Performance | 00:00:00 | ||

Linear Regression Assumptions – Homoscedasticity, Multicollinearity. Etc. | 00:00:00 | ||

Interpretation Of Regression Plots – Residual Vs Fitted Values, Normal Q-q Plot, Scale Location Plot, Residuals Vs Leverage Plot | 00:00:00 | ||

Case Study Using Linear Regression, Multiple Linear Regression, Non Linear Regression | 00:00:00 | ||

Project – Building Prediction Model | 00:00:00 | ||

Quiz 12 | 00:00:00 | ||

Classification Models | |||

Introduction To Classification Model | 00:00:00 | ||

Training And Evaluating Classification Models | 00:00:00 | ||

Confusion Matrix – Accuracy, Precision, Etc. | 00:00:00 | ||

Interpreting Roc Curve | 00:00:00 | ||

Fine Tuning Models Using Hyper Parameters | 00:00:00 | ||

Quiz 13 | 00:00:00 | ||

Logistic Regression | |||

Introduction To Logistic Regression | 00:00:00 | ||

Building Logistic Regression Model | 00:00:00 | ||

Visualising Logistic Regression | 00:00:00 | ||

Interpreting Logistic Regression | 00:00:00 | ||

Making Problistic Predictions | 00:00:00 | ||

Logistic Regression Case Study | 00:00:00 | ||

Project On Logistic Regression | 00:00:00 | ||

Quiz 14 | 00:00:00 | ||

K Nearest Neighbour | |||

Introduction To K Nearest Neighbour | 00:00:00 | ||

Building K Nearest Neighbour Model | 00:00:00 | ||

Visualising K Nearest Neighbour | 00:00:00 | ||

Interpreting K Nearest Neighbour | 00:00:00 | ||

Making Predictions | 00:00:00 | ||

K Nearest Neighbour Case Study | 00:00:00 | ||

Project On K Nearest Neighbour | 00:00:00 | ||

Quiz 15 | 00:00:00 | ||

Decision Tree | |||

Types Of Tree Based Models | 00:00:00 | ||

Introduction To Decision Tree | 00:00:00 | ||

Information Gain, Entropy Gain, Gini Index | 00:00:00 | ||

Building Decision Tree Model | 00:00:00 | ||

Visualising Decision Tree | 00:00:00 | ||

Interpreting Decision Tree | 00:00:00 | ||

Making Predictions | 00:00:00 | ||

Decision Tree Case Study | 00:00:00 | ||

Project On Decision Tree | 00:00:00 | ||

Quiz 16 | 00:00:00 | ||

Bagged Trees | |||

Introduction To Bagged Trees | 00:00:00 | ||

Bootstrap Sampling | 00:00:00 | ||

Building Bagged Tree Model | 00:00:00 | ||

Visualising Bagged Tree | 00:00:00 | ||

Interpreting Bagged Tree | 00:00:00 | ||

Making Predictions | 00:00:00 | ||

Fine Tuning Bagged Tree Using Hyper Parameters | 00:00:00 | ||

Bagged Tree Case Study | 00:00:00 | ||

Project On Bagged Tree | 00:00:00 | ||

Quiz 17 | 00:00:00 | ||

Random Forests | |||

Introduction To Random Forest | 00:00:00 | ||

Building Random Forest Model | 00:00:00 | ||

Visualising Random Forest | 00:00:00 | ||

Interpreting Random Forest | 00:00:00 | ||

Making Predictions | 00:00:00 | ||

Fine Tuning Random Forest Using Hyper Parameters | 00:00:00 | ||

Variable Selection And Variable Importance Plot | 00:00:00 | ||

Random Forest Case Study | 00:00:00 | ||

Project On Random Forest | 00:00:00 | ||

Quiz 18 | 00:00:00 | ||

Boost Trees | |||

Introduction To Boost Trees | 00:00:00 | ||

Building Gradient Boost Models | 00:00:00 | ||

Visualising Gradient Boost | 00:00:00 | ||

Interpreting Gradient Boost | 00:00:00 | ||

Making Predictions | 00:00:00 | ||

Fine Tuning Gradient Boost Using Hyper Parameters | 00:00:00 | ||

Variable Selection And Variable Importance Plot | 00:00:00 | ||

Xgboost, Lightgbm – Popular On Kaggle | 00:00:00 | ||

Gradient Boost Case Study | 00:00:00 | ||

Project On Gradient Boost | 00:00:00 | ||

Quiz 19 | 00:00:00 | ||

Support Vector Machines | |||

Introduction To Support Vector Machines | 00:00:00 | ||

Building Support Vector Model | 00:00:00 | ||

Understanding Kernel And Gamma In Svm | 00:00:00 | ||

Visualising Support Vector Machine | 00:00:00 | ||

Interpreting Support Vector Machine | 00:00:00 | ||

Making Predictions | 00:00:00 | ||

Fine Tuning Support Vector Machine | 00:00:00 | ||

Support Vector Machine Case Study | 00:00:00 | ||

Project On Support Vector Machine | 00:00:00 | ||

Quiz 20 | 00:00:00 | ||

K - Means Clustering | |||

Understanding K – Means Clustering | 00:00:00 | ||

Selecting Right Number Of Clusters | 00:00:00 | ||

Elbow Plot | 00:00:00 | ||

K Means Clustering Case Study | 00:00:00 | ||

Project On K Means Clustering | 00:00:00 | ||

Quiz 21 | 00:00:00 | ||

Hierarchical Clustering | |||

Understanding Hierarchical Clustering | 00:00:00 | ||

Selecting Right Number Of Clusters | 00:00:00 | ||

Interpreting Dendrogram | 00:00:00 | ||

Hierarchical Clustering Case Study | 00:00:00 | ||

Project On Hierarchical Clustering | 00:00:00 | ||

Quiz 22 | 00:00:00 | ||

Dimensionality Reduction - PCA | |||

Understanding Dimensional Reduction | 00:00:00 | ||

Applications Of Dimensional Reduction | 00:00:00 | ||

PCA – Principal Component Analysis Intuition | 00:00:00 | ||

PCA Calculations In R | 00:00:00 | ||

PCA – Benefits | 00:00:00 | ||

PCA Case Study | 00:00:00 | ||

Project On PCA | 00:00:00 | ||

Quiz 23 | 00:00:00 | ||

NLP - Text Mining - Bag Of Words | |||

Understanding Text Mining | 00:00:00 | ||

Cleaning And Preprocessing Text Data | 00:00:00 | ||

Understanding Terminology | 00:00:00 | ||

Tdm And Dtm Formats | 00:00:00 | ||

Plotting Better | 00:00:00 | ||

Extracting Amazon Reviews Data | 00:00:00 | ||

NLP – Case Study | 00:00:00 | ||

NLP – Project | 00:00:00 | ||

Quiz 24 | 00:00:00 | ||

NLP - Text Mining - Sentiment Analysis | |||

Understanding Sentiment Analysis | 00:00:00 | ||

Cleaning And Preprocessing Text Data | 00:00:00 | ||

Understanding Terminology | 00:00:00 | ||

Visualising Sentiments | 00:00:00 | ||

Connecting With Twitter | 00:00:00 | ||

Extracting Twitter Data | 00:00:00 | ||

Sentiment Analysis – Case Study | 00:00:00 | ||

Sentiment Analysis – Project | 00:00:00 | ||

Quiz 25 | 00:00:00 | ||

Association Rule | |||

Understanding Association Rule | 00:00:00 | ||

Understanding Apriori Intuition | 00:00:00 | ||

Case Study Using Apriori | 00:00:00 | ||

Understanding Eclat | 00:00:00 | ||

Case Study Using Eclat | 00:00:00 | ||

Market Basket Analysis | 00:00:00 | ||

Quiz 26 | 00:00:00 | ||

Recommendation Engine | |||

Introduction To Recommendation Systems Engine | 00:00:00 | ||

How Recommendation Engine Works | 00:00:00 | ||

Types Of Recommendation Systems | 00:00:00 | ||

Building A Recommendation System Engine | 00:00:00 | ||

Project On Recommendation System | 00:00:00 | ||

Quiz 27 | 00:00:00 | ||

Time Series Analysis | |||

Introduction To Time Series Analysis | 00:00:00 | ||

Manipulating Time Series Data | 00:00:00 | ||

Forecasting Using Arima | 00:00:00 | ||

Exponential Smoothing Method | 00:00:00 | ||

Visualising Time Series Data | 00:00:00 | ||

Time Series – Case Study | 00:00:00 | ||

Time Series – Project | 00:00:00 | ||

Quiz 28 | 00:00:00 | ||

Data Wrangling With Sql | |||

Introduction To Sql Queries | 00:00:00 | ||

Connecting R With Sql Server | 00:00:00 | ||

Fetching & Querying Data From Sql Server Using R | 00:00:00 | ||

Quiz 29 | 00:00:00 | ||

Deep Learning & Neural Nets | |||

Introduction To Neural Nets | 00:00:00 | ||

Neural Nets With R | 00:00:00 | ||

Project On Neural Nets | 00:00:00 | ||

Quiz 30 | 00:00:00 |

**2 STUDENTS ENROLLED**