DATA SCIENCE TRAINING IN CHENNAI

Btree System’s Data science training in Chennai will help you to be more familiar with statistics Machine Learning, Deep Learning, Tensorflow, Artificial intelligence, and machine learning algorithm Concepts. Learning data science from Btree Systems will get you more benefits from different streams that include statistics, computer science, & software engineering. We provide you with the complete knowledge which helps you manage & analyze data the complete data.
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About Data Science Course

What is Data Science?

In simple, the study of Data is called Data Science. It became the most effective and futuristic medium that will be effective to attain premium career goals.

Why should you learn Data Science?

Data Science encourages the company or individual to understand the requirements of the customer better. This knowledge helps them provide good service that tends to grow your firm.

Why Data Scientist?

 Data, a precious terminology of any budding or profitable business. To grab more accurate statistics on data, you need a scientist of its own. Being a one will shape your career at the best. 

Job Roles of Data Scientist  

In well-developed countries like USA, the data scientist has ranked first among very promising roles. A data scientist has to deal with stakeholders, business algorithm, revenue generation module etc. 

BTREE SYSTEM – Key Features

Training from
Industrial Experts
Hands on
Practicals/ Projects
100% Placement
Assistance
24 x 7
Expert Support
Certification
of Completion
Free
Live Demo

What you will get in the Course?

  • You could learn the complete algorithms of machine learnings
  • Detail discussion on linear regression & polynomial regression. 
  • Experts of Betree systems help you with Random Forest, Naïve Bayes, SVM, GBM, Xgboost
  • In-depth sessions will occur using K means for clustering algorithms. 

Highlights of Our Data Science Course in Chennai

  • Btreesystems is one of the best data science training institute that is providing extraordinary training trainees with any background. 
  • Syllabus of Btree systems is based on a thesis that helps you learn advanced programs in an effective manner. 
  •  Rich in terms of course content at affordable price segments. 
  • Since the classes will be conducted by the experts who are associated with topmost MNCs, you would get a clearcut picture of the syllabus. 
  • One to one classes will also be taken.
  • No issues with the timing. We are specialized in making tailor-made classes
  • Theories and trial model will never make you an expert but the real-time project does. Here you are allowed to deal with real-time projects.  
  • Certificates that we are offering will be proficient. 

CHAPTER 1: Introduction to Data Science

  • Market trend of Data Science
  • Opportunities for Data Science
  • What is the need for Data Scientists
  • What is Data science
  • Data Science Venn Diagram
  • Data Science Use cases
  • Knowing the roles of a Data Science practitioner
  • Data Science – Skills set
  • Understanding the concepts & definitions of:
  • Artificial Intelligence
  • Machine Learning- Deep Learning
  • NLP
  • Computer Vision

CHAPTER 2: Data and Tools

  • What is Business Intelligence?
  • What is ETL?
  • Layers of a Data Warehouse
  • OLAP VS OLTP
  • Facts and Dimensions
  • Big Data tools and it’s uses
  • Big Data stack
  • Understanding Structured text Data
  • Understanding Unstructured text Data

CHAPTER 3: Data Science- Deep dive

  • Understanding Descriptive vs Predictive vs Prescriptive Analytics
  • Difference between Analytics vs. Analysis
  • Data Science Project Lifecycle
  • Technology Stack Involved in the Lifecycle
  • Machine Learning tools
  • Development tools
  • Languages
  • Data Platforms
  • CRISP – Cross-industry standard process for Data Mining
  • 5WIH- The questions that kick start a ML project
  • 80-20 Rule of Data Analytics
  • Supervised Vs Unsupervised Learning
  • Data Science- Use case bubble
  • Data Mining techniques

CHAPTER 4: Data

  • Data Wrangling or Data Munging
  • Data Categorization basics
  • Different Types of Data
  • Types of Data Collection
  • Data Sources
  • Data Collection plan
  • Data Quality Issues
  • Types of Data Error
  • Ration Scale Vs Interval Scale
  • Predictors/Features vs Predictions/Labels
  • Understanding Imbalance in Data

CHAPTER 5: Statistics & Probability

  • What is Statistics
  • Sample Vs Population
  • Measure of Central vs Dispersion
  • Frequency Distribution
  • Cumulative Frequency Distribution
  • Mean, Median, Mode
  • Quartiles/Percentile
  • Range, Variance, Standard Deviation, Co-efficient of Variation
  • 68-95-99 Rule of SD
  • Z Score (Standard Score)
  • P-Value
  • Maximum Likelihood Estimation
  • Probability vs Likelihood
  • PDF vs PMF
  • Normal Distribution of Data
  • Skewness & it’s types
  • Kurtosis & it’s types
  • Kth Central Moments
  • Co-Variance/Joint Probability Distribution
  • Correlation
  • Entropy
  • ANOVA
  • Chi-Square
  • F tests
  • Types of Data Distribution
  • Real-time Practicals
  • Hands on- Lab using pen and paper Only

CHAPTER 6: Setup

  • Anaconda & Python
  • Understanding Jupyter Notebooks
  • Python Package Installation
  • Tableau Installation
  • Oracle Database & Server

CHAPTER 7: Data Sourcing, Exploratory Data Analysis & Readiness

  • Concept of List, Data frame, Dictionary
  • Connecting to Databases using Python
  • Importing data from csv, text, Excel
  • Converting JSON, XML, to Data frame
  • Understanding EDA
  • Frequency Distribution
  • Analyzing NA, blanks
  • Using SQL concepts inside Python
  • Real-time Practicals
  • Hands on- Lab using Python

CHAPTER 8: Data Transformation/Wrangling

  • Handling missing Values
  • Handling Outliers
  • Normalization techniques
  • Standardization techniques
  • Regularization techniques
  • Feature Extraction
  • Train Test data selection
  • Real-time Practicals
  • Hands on- Lab using Python

CHAPTER 9: Data Science Concepts

  • No Free Lunch
  • Hypothesis vs Null Hypothesis
  • BIAS VS Variance tradeoff
  • Local Vs Global Minima/Maxima
  • Bias – Loss/ Loss-Cost Function

CHAPTER 10: Linear Regression

  • Understanding Regression math
  • Linear Algebra concepts
  • Least Mean Square
  • Analyzing Co-relation
  • Heat Maps, Pair Plots, Distribution Graphs
  • Simple Vs Multiple Linear regression
  • Train Test data selection
  • Real-time Practicals
  • Hands on- Lab & Model Implementation using Python

CHAPTER 11: Polynomial Regression

  • Understanding the math
  • Polynomial Algebra concepts
  • Degree of Polynomial
  • Real-time Practicals
  • Hands on- Lab & Model Implementation using Python

CHAPTER 12: Classification

  • Overfitting/ Under fitting/ Optimal Fits
  • Handling Categorical Data inside
  • Confusion Matrix
  • Type I & Type II errors
  • Precision Vs Accuracy
  • AUC/ROC curve

CHAPTER 13: Logistic Regression

  • Understanding the statistics behind Logistic Sigmoid
  • Logistic regression math
  • Real-time Practicals
  • Hands on- Lab & Model Implementation using Python

CHAPTER 14: Random Forest

  • Understanding the Decision Tree & Bagging
  • Math behind Classification and Regression in tree
  • Decision Tree concepts
  • Using Random Forest for Regression
  • K fold Cross Validation
  • Model Optimizers
  • Hyper parameter Tuning
  • Real-time Practicals
  • Hands on- Lab & Model Implementation using Python

CHAPTER 15: Naïve Bayes Theorem

  • Understanding the Naïve Bayes theorem
  • Bayesian Vs Gaussian theorems
  • Using naïve Bayes for Regression
  • Model Optimizers
  • Hyper parameter Tuning
  • Real-time Practicals
  • Hands on- Lab & Model Implementation using Python

CHAPTER 16: NLP for Machine Learning Featuring

  • Label Encoding
  • One hot encoding
  • Synonym treatment
  • Stemming
  • Lemmatization
  • Stop words
  • Parts Of Speech Tagging
  • TF-IDF and its math Behind
  • Real-time Practicals
  • Hands on- Lab using Python

CHAPTER 17: Support Vector Machine

  • Understanding the SVM Concept
  • Hyper plane and Kernel
  • Using SVM for Regression
  • Grid Search
  • Model Optimizers
  • Hyper parameter Tuning
  • Real-time Practicals
  • Hands on- Lab & Model Implementation using Python

CHAPTER 18: Gradient Boosting Machine & Xgboost

  • Understanding the Boosting Concept
  • Hyper plane and Kernel
  • Learning Rate
  • Model Optimizers
  • Hyper parameter Tuning
  • Real-time Practicals
  • Hands on- Lab & Model Implementation using Python

CHAPTER 19: K MEANS CLUSTERING ALGORITHM

  • Understanding Nearest Neighbors concept
  • Statistics behind K Means Clustering Algorithm
  • Real-time Practicals
  • Hands on- Lab & Model Implementation using Python

CHAPTER 20: Keras Tensor flow – MLP Deep Learning (Neural Networks)

  • Understanding Deep learning
  • MLP Vs other Deep Learning
  • How Neural Network works & Architecture
  • Activation functions.
  • Model Optimizers
  • Hyper parameter Tuning
  • Best Practice and when to use DL
  • Real-time Practicals
  • Hands on- Lab & Model Implementation using Python

CHAPTER 21: H20.ai

  • Introduction to H20.ai
  • Pros and Cons
  • Available models in H20.ai
  • Real-time Practicals
  • Hands on- Lab & Model Implementation using Python

CHAPTER 22: Sampling & Dimension Reduction (DR)

  • Introduction to Sampling
  • Over sampling and Under sampling
  • SMOTE/SMOTENC & Near Miss
  • Pros and Cons of sampling
  • Introduction to DR
  • PCA & it’s code

CHAPTER 23: Deployment of Model to Production

  • Introduction to Pyinstaller
  • Pickle and Joblib
  • Real-time Practicals
  • Hands on- Lab & Model deployment using Python

CHAPTER 24: Tableau Basics

  • Introduction to Tableau
  • Data sources
  • Exploratory Data Analysis
  • Clustering Analysis and Inferences using Tableau
  • Creating visualizations

 

You will be going through detailed 2 to 3 months of Data Science Hands-on training

  • Professional data scientist helps you learn, apply, and become a data scientist. 
  • Help you develop a professional portfolio by yourself. 
  • A complete material fr data science will be handover to you which include segments like how to crack an interview and project resources.  

Have Queries? Ask our Experts

Have Queries? Ask our Experts

Learning Outcomes of our Best Data Science Course in Chennai

  • Experts help you develop skills that would get updated regularly. Eg- Loop functions and debugging tools. 
  • Develop the understanding and the ability to solve real-world issues with the guidance of data mining software.   
  • We help you find and test the intensified level principles of data science. 
  • Tools that have high complexity and algorithms of data science will no longer tough. 
  • The reliability of Python and BigData technologies will be taught by experts of its own. 
Top Factors which makes us the Best Data Science Training Institute in Chennai
  • No worries about the schedule planning, you could attend it on both weekdays and weekends. If requested, experts could also make it online. 
  • Get an opportunity to learn Data Science from the experts in its own industry. 
  • Here you could find the best data science learning experience that could be traced by professionals. 
  • BtreeSystem helps you find real-time data to learn it with real-world experience. 
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Frequently Asked Data Science Questions

Why Learn Data Science?
Data leaning will be the most paid jobs in future. There are high chances fo you o be hired by the topmost MNCs
Who can learn Data science?
It is one of the most endurable profiles that is mandatory for any industry. Data Science suits best for those who have high mathematical skills, problem-solving skills, creating visual data skills, interest in future technology and creating a business strategy on marketing status.
What are the Pre requiste to learn Data science?
  1. We will help you became a great problem solver. 
  2. Huge structured and unstructured data could be handled by you with zero pressure.

You could be great in the algorithm that helps to generate the right data visualisation

Nearby Access Areas
Our Velachery and OMR branches are very nearby access to the below locations.
Medavakkam, Adyar, Tambaram, Adambakkam, OMR, Anna Salai, Velachery, Ambattur, Ekkattuthangal, Ashok Nagar, Poonamallee, Aminjikarai, Perambur, Anna Nagar, Kodambakkam, Besant Nagar, Purasaiwakkam, Chromepet, Teynampet, Choolaimedu, Madipakkam, Guindy, Navalur, Egmore, Triplicane, K.K. Nagar, Nandanam, Koyambedu, Valasaravakkam, Kilpauk, T.Nagar, Meenambakkam, Thiruvanmiyur, Nungambakkam, Thoraipakkam, Nanganallur, St.Thomas Mount, Mylapore, Pallikaranai, Pallavaram, Porur, Saidapet, Virugambakkam, Siruseri, Perungudi, Vadapalani, Villivakkam, West Mambalam, Sholinganallur.
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