Data Science Training

 >>  Data Science Training

Data Science Training


 (4.9) | 800 Ratings


Introduction


Data Science Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 35 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course Curriculum

Tpoic1: Introduction to Data Science and Statistical Analytics

Introduction to Data Science, Use cases, Need of Business Analytics, Data Science Life Cycle, Different tools available for Data Science

Topic2: Introduction to R

Installing R and R-Studio, R packages, R Operators, if statements and loops (for, while, repeat, break, next), switch case

Topic3: Data Exploration, Data Wrangling and R Data Structure

Importing and Exporting data from external source, Data exploratory analysis, R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List), Functions, Apply Functions

Topic4: Data Visualization

Bar Graph (Simple, Grouped, Stacked), Histogram, Pi Chart, Line Chart, Box (Whisker) Plot, Scatter Plot, Correlogram

Tpoic5: Introduction to Statistics

Terminologies of Statistics ,Measures of Centers, Measures of Spread, Probability, Normal Distribution, Binary Distribution, Hypothesis Testing, Chi Square Test, ANOVA

Tpoic6: Predictive Modeling – 1 (Linear Regression)

Supervised Learning – Linear Regression, Bivariate Regression, Multiple Regression Analysis, Correlation( Positive, negative and neutral), Industrial Case Study, Machine Learning Use-Cases, Machine Learning Process Flow, Machine Learning Categories

Tpoic7: Predictive Modeling – 2 (Logistic Regression)

Logistic Regression

Tpoic8: Decision Trees

What is Classification and its use cases?, What is Decision Tree?, Algorithm for Decision Tree Induction, Creating a Perfect Decision Tree, Confusion Matrix

Tpoic9: Random Forest

Random Forest, What is Naive Bayes?

Tpoic10: Unsupervised learning

What is Clustering & it’s Use Cases? What is K-means Clustering?, What is Canopy Clustering?, What is Hierarchical Clustering?

Tpoic11: Association Analysis and Recommendation engine

Market Basket Analysis (MBA), Association Rules, Apriori Algorithm for MBA, Introduction of Recommendation Engine, Types of Recommendation – User-Based and Item-Based, Recommendation Use-case

Tpoic12: Sentiment Analysis

Introduction to Text Mining, Introduction to Sentiment, Setting up API bridge, between R and Tweeter Account, Extracting Tweet from Tweeter Acc, Scoring the tweet

Tpoic13: Time Series

What is Time Series data?, Time Series variables, Different components of Time Series data, Visualize the data to identify Time Series Components, Implement ARIMA model for forecasting, Exponential smoothing models, Identifying different time series scenario based on which different Exponential Smoothing model can be applied, Implement respective ETS model for forecasting

Data Science Project

Project 1 – Understanding Cold Start Problem in Data Science

Topics: This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the following:

  • Algorithms for Recommender
  • Ways of Recommendation
  • Types of Recommendation -Collaborative Filtering Based Recommendation, Content-Based Recommendation
  • Complete mastery in working with the Cold Start Problem.

Project 2 – Recommendation for Movie, Summary

Topics: This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:

  • Recommendation for movie
  • Two Types of Predictions – Rating Prediction, Item Prediction
  • Important Approaches: Memory Based and Model-Based
  • Knowing User Based Methods in K-Nearest Neighbor
  • Understanding Item Based Method
  • Matrix Factorization
  • Decomposition of Singular Value
  • Data Science Project discussion
  • Collaboration Filtering
  • Business Variables Overview

Case Study

The Market Basket Analysis (MBA) case study

This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.

Our online training provider or project support oriented training offering an entire spectrum of IT Professional courses. we are World’s the best online training providers with innovative, creative and flexible training solutions by experienced working professionals as our core team of instructors. We facilitate unique training programmers by world class training methodologies in various software IT courses. We also deliver certification based course training’s. The major objectives of our training programs are to get understand the concepts of IT environments. We are in the process of building the careers of professionals as guide to explore their career with right training and support.

Exam & Certification

0

Course Review

(4.9)
5 stars
4 stars
3 stars
2 stars
1 stars

Course Curriculum

Tpoic1: Introduction to Data Science and Statistical Analytics

Introduction to Data Science, Use cases, Need of Business Analytics, Data Science Life Cycle, Different tools available for Data Science

Topic2: Introduction to R

Installing R and R-Studio, R packages, R Operators, if statements and loops (for, while, repeat, break, next), switch case

Topic3: Data Exploration, Data Wrangling and R Data Structure

Importing and Exporting data from external source, Data exploratory analysis, R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List), Functions, Apply Functions

Topic4: Data Visualization

Bar Graph (Simple, Grouped, Stacked), Histogram, Pi Chart, Line Chart, Box (Whisker) Plot, Scatter Plot, Correlogram

Tpoic5: Introduction to Statistics

Terminologies of Statistics ,Measures of Centers, Measures of Spread, Probability, Normal Distribution, Binary Distribution, Hypothesis Testing, Chi Square Test, ANOVA

Tpoic6: Predictive Modeling – 1 (Linear Regression)

Supervised Learning – Linear Regression, Bivariate Regression, Multiple Regression Analysis, Correlation( Positive, negative and neutral), Industrial Case Study, Machine Learning Use-Cases, Machine Learning Process Flow, Machine Learning Categories

Tpoic7: Predictive Modeling – 2 (Logistic Regression)

Logistic Regression

Tpoic8: Decision Trees

What is Classification and its use cases?, What is Decision Tree?, Algorithm for Decision Tree Induction, Creating a Perfect Decision Tree, Confusion Matrix

Tpoic9: Random Forest

Random Forest, What is Naive Bayes?

Tpoic10: Unsupervised learning

What is Clustering & it’s Use Cases? What is K-means Clustering?, What is Canopy Clustering?, What is Hierarchical Clustering?

Tpoic11: Association Analysis and Recommendation engine

Market Basket Analysis (MBA), Association Rules, Apriori Algorithm for MBA, Introduction of Recommendation Engine, Types of Recommendation – User-Based and Item-Based, Recommendation Use-case

Tpoic12: Sentiment Analysis

Introduction to Text Mining, Introduction to Sentiment, Setting up API bridge, between R and Tweeter Account, Extracting Tweet from Tweeter Acc, Scoring the tweet

Tpoic13: Time Series

What is Time Series data?, Time Series variables, Different components of Time Series data, Visualize the data to identify Time Series Components, Implement ARIMA model for forecasting, Exponential smoothing models, Identifying different time series scenario based on which different Exponential Smoothing model can be applied, Implement respective ETS model for forecasting

Data Science Project

Project 1 – Understanding Cold Start Problem in Data Science

Topics: This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the following:

  • Algorithms for Recommender
  • Ways of Recommendation
  • Types of Recommendation -Collaborative Filtering Based Recommendation, Content-Based Recommendation
  • Complete mastery in working with the Cold Start Problem.

Project 2 – Recommendation for Movie, Summary

Topics: This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:

  • Recommendation for movie
  • Two Types of Predictions – Rating Prediction, Item Prediction
  • Important Approaches: Memory Based and Model-Based
  • Knowing User Based Methods in K-Nearest Neighbor
  • Understanding Item Based Method
  • Matrix Factorization
  • Decomposition of Singular Value
  • Data Science Project discussion
  • Collaboration Filtering
  • Business Variables Overview

Case Study

The Market Basket Analysis (MBA) case study

This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.

Our online training provider or project support oriented training offering an entire spectrum of IT Professional courses. we are World’s the best online training providers with innovative, creative and flexible training solutions by experienced working professionals as our core team of instructors. We facilitate unique training programmers by world class training methodologies in various software IT courses. We also deliver certification based course training’s. The major objectives of our training programs are to get understand the concepts of IT environments. We are in the process of building the careers of professionals as guide to explore their career with right training and support.

    Click here for Help and Support: info@sacrostectservices.com     For Inquiry Call Us:   +91 996-629-7972(IND)

  +91 996-629-7972(IND)
X

Quick Enquiry

X

Business Enquiry