Data Science Training

(3) 3 Ratings

Course Schedule

30

JUL

Mon - Sat

07:30 PM - 10:00 PM ( IST )

06

AUG

Mon - Fri

11:00 AM - 02:00 PM ( IST )

11

AUG

Sat - Sun

05:30 AM - 06:30 AM ( IST )


Total Learners

794 Learners


LMS Access

365 days

Course duration

30 days


Support

24/7 support

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The Instructor for this course is from one of the Big5 Companies in the world.

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Modes of Training

Corporate Training

Live, Classroom Or Self Paced Training

Online Classroom

Attend our Instructor Led Online Virtual Classroom

Self Paced Training

Comprehensive Recorded Videos by Experts to learn at your own pace

Course Features

Live Instructor-led Classes

This isn't canned learning. Its dynamic, its interactive, its effective

Expert Educators

Only the best or they're out. We are constantly evaluating our trainers

24&7 Support

We never sleep. Need something answered at 3 am? No Problem

Flexible Schedule

You don't learn as per our calendar. We work according to yours

☰ Details

Course Curriculum

Introduction to Data Science

  • What is Data Science?
  • What does Data Science involve?
  • The era of Data Science
  • Business Intelligence vs Data Science
  • Life-cycle of Data Science
  • Tools of Data Science
  • Introduction to Big Data and Hadoop
  • Introduction to R
  • Introduction to Spark
  • Introduction to Machine Learning

Statistical Inference

  • What is Statistical Inference?
  • Terminologies of Statistics
  • Measures of Centers
  • Measures of Spread
  • Probability
  • Normal Distribution
  • Binary Distribution.

Data Extraction, Wrangling and Exploration

  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data.

Introduction to Machine Learning

  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Supervised Learning algorithm: Linear Regression and Logistic Regression.

Classification Techniques

  • What are the classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
  • What is Navies Bayes?
  • Support Vector Machine: Classification.

Hands-on with Python+Tensorflow

  • Determine the Probability Compare the Probability and Make Decision
  • One Sample T-Test Two Independent Samples Tests 
  • Paired T-test, Proportional Test 
  • Non-Parametric One-Sample Test 
  • Chi-Square, Test Z Test, F Test.

Unsupervised Learning

  • What is Clustering & its use cases
  • What is K-means Clustering?
  • What is the C-means Clustering?
  • What is the Canopy Clustering?
  • What is Hierarchical Clustering?

Recommender Engines

  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Types of Recommendations
  • User-Based Recommendation
  • Item-Based Recommendation
  • Difference: User-Based and Item-Based Recommendation
  • Recommendation use cases.

Text Mining

  • The concepts of text-mining
  • Use cases
  • Text Mining Algorithms
  • Quantifying text
  • TF-IDF
  • Beyond TF-IDF.

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 the ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement the respective ETS model for forecasting.

Course Description

What are the Course Objectives for this Training?

  • To learn the key features of Data Science.
  • ‘Understand the probability distributions in details.
  • Working with real-time problems.
  • To work on data handling concepts.
  • Working on integrating with other tools.

Who Should Attend this Training?

  • Software Developers
  • Statisticians
  • College / Fresher’s with statistics and math background
  • Statistics Professionals.

What are the Pre-requisites for this Training Course?

  • Experience in Statistics Machine Language will help becoming Data Scientist. Understanding business and domain concepts would be an added advantage. 
  • Basic R programming concepts will come in handy. Knowledge in BigData would also help.

FAQ

Do you have self paced training?

Yes, we offer self paced training

How do you provide training?

We offer three different modes of training. Instructor Led Live Training, Self Paced Training and Corporate Training

Do you offer any discounts?

Yes we offer discounts for group of 3 plus people.

Can I choose timings that suits my schedule?

Yes we are the only company where we work with students and offer flexible timings which fits your schedule

Who are the Instructors?

All our instructors are from MNC companies who have real time experience of more than 10 years.

Can I attend a demo session before joining?

Yes we offer a free demo session with the instructors. The trainer will answer all your queries and share the course agenda.

Course Reviews

Abhinav

Hi myself Abhinav, I really impressed with the experience in LTB. Our trainer is one of the best I have ever seen. He taught each and every concept in a detailed manner. Thank you so much.

Sandeep

Had a Great Experience in learning the Course with LTB. The trainer has a Practical Knowledge about the subject and taught us with real-time scenarios. Thank you LTB.

Keerthana

It was a great experience to undergo and get certified in the Data Science Course from LTB. As a working professional, it has not only given me an exposure to the domain but also helped me learn cross technologies and develop an inclination towards it. Thank you.