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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

  1. IT professionals looking for career or technology change to  Data Science & Analytics.
  2. BPO industry  or non technical  professionals who are looking for career in Data Science & Analytics.
  3. Engineering graduates who want to build a career in Data Science & Analytics.
  4. Non- technical & MBA graduates who are interested in building a career in Data Science.
  5. Anyone who likes manipulating data & loves getting insights out of data.
  6. 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

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