Data Science Masters Program in Chennai

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Online master's in Data Science Masters Program in Chennai allows you to obtain expertise in Data Science. You will work on practical Data Science projects using R, Hadoop Development, Administration, Testing, and Analysis, Apache Spark, Scala, Deep Learning, Power BI, SQL, MongoDB, and other tools. As part of the online classroom training, you will receive five additional self-paced courses including Deep Learning with TensorFlow, Build Chatbots with Watson Assistant, R for Data Science, Spark MLlIb, and Python for Data Science. Enrol today to start your online MS in Data Science master program.

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Overview
Curriculam
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Certifications
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FAQ
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Tools and languages covered

Python
NumPy
Pandas
Statistics
Mathematics
Devops
Machine Learning
Deep Learning & NLP
Artificial intelligence

Overview of Data Science Master Course

The Master in Data Science programme will teach you all you need to know about Data Science, real-time analytics, statistical computing, SQL, parsing machine-generated data, and finally Deep Learning in Artificial Intelligence. Moreover, you will learn how to use Spark for Data Science and Big Data Analytics in this program.

Data science is a multidisciplinary area that everyone, from engineers to marketers, may participate in. Data science is the process of gathering and analysing massive amounts of data in order to get profound insights that may be utilized for corporate decisions, product development, consumer interaction, and so on.

Because it integrates tools, techniques, and technologies to derive meaning from data, data science is significant. Due to the growth of gadgets that can automatically gather and store data, modern companies are overrun with data. In the areas of e-commerce, healthcare, banking, and every other facet of human existence, online systems and payment portals collect more data. We have access to enormous amounts of text, audio, video, and image data.

The data science process is often started by a business challenge. With the help of business stakeholders, a data scientist will learn what the business requires. Once the issue has been identified, the data scientist can use the OSEMN data science approach to resolve it:
Obtain data
Data might be pre-existing, recently collected, or downloaded from an online data source. Data scientists can obtain information from internal or external databases, firm CRM software, web server logs, social media, or trustworthy third-party sources.
Scrub data
Data scrubbing, also known as data cleaning, is the act of standardising data in accordance with a specified format. It includes dealing with missing data, correcting data mistakes, and eliminating any outliers in the data.
• Converting all dates to a similar, uniform format.
• Removing extra spaces or spelling errors.
• Eliminating commas from huge numbers or correcting mathematical errors.
Explore data
Data exploration is the early investigation of data used to develop future data modelling plans. Using descriptive statistics and data visualisation tools, data scientists get a basic knowledge of the data. The data is then explored to uncover intriguing patterns that might be investigated or implemented.
Model data
Software and machine learning algorithms are utilised to get deeper insights, forecast outcomes, and prescribe the best course of action. The training data set is subjected to machine learning techniques including association, classification, and clustering. To evaluate the correctness of the results, the model may be evaluated against predetermined test data.

Data science professionals use sophisticated technology like:
Artificial intelligence: Machine learning models and accompanying software are used for predictive and prescriptive analyses.
Cloud computing: Cloud technologies now provide data scientists with the processing power and flexibility needed for advanced data analyses.
Internet of things (IoT) refers to many gadgets that may connect to the internet automatically. These gadgets gather information for projects using data science. They produce enormous amounts of data that can be mined and extracted.
Quantum computing: Quickly completing intricate computations is a capability of quantum computers. They are used by knowledgeable data scientists to create sophisticated quantitative algorithms.

As part of the data science process, a data scientist might make use of a variety of different methodologies, tools, and technologies. They choose the optimum combinations based on the issue at hand for quicker and more precise outcomes.
The responsibilities and daily tasks of a data scientist vary according to the needs and size of the firm. Although they frequently adhere to the data science approach, the specifics may change. To guarantee that the entire data science process is followed and that business objectives are met, a data scientist may collaborate with other analysts, engineers, machine learning specialists, and statisticians in bigger data science teams.

Data scientists require a unique combination of technical and soft abilities. The following are some essential technical abilities for prospective data scientists, followed by soft skills.
Technical skills
• Statistics
• Coding and programming
• Modelling for prediction
• Deep learning and machine learning
• Data manipulation, cleaning, and preparation
Soft skills
• Outstanding business acumen
• Problem-solving
• Thinking critically
• Communication
• Collaboration

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Curriculum for Data Science Masters Program

Introduction to Data Science
High level view of Data Science, Artificial Intelligence & Machine Learning
Subtle differences between Data Science, Machine Learning & Artificial Intelligence
Approaches to Machine Learning
Terms & Terminologies of Data Science
Understanding an end to end Data Science Pipeline, Implementation cycle Math for Data Science, Machine Learning and Artificial Intelligence Linear Algebra
Matrices, Matrix Operations Eigen Values, Eigen Vectors Scalar, Vector, and Tensors Prior and Posterior Probability Conditional Probability Calculus
Differentiation, Gradient, and Cost FunctionsGraph Theory

Descriptive Statistics

Types of Data (Discrete vs Continuous)
Types of Data (Nominal, Ordinal)
Measures of Central Tendency(Mean, Median, Mode)
Measures of Dispersion (Variance, Standard Deviation)
Range, Quartiles, Inter Quartile Ranges
Measures of Shape (Skewnessand Kurtosis)
Tests for Association (Correlation and Regression)
Random Variables
Probability Distributions
Standard Normal Distribution
Probability Distribution Function
Probability Mass Function
Cumulative Distribution Function

Inferential Statistics

Statistical sampling& Inference
Hypothesis Testing
Null and Alternate Hypothesis
Margin of Error
Type I and Type II errors
One-Sided Hypothesis Test, Two-Sided Hypothesis Test
Tests of Inference: Chi-Square, T-test, Analysis of Variance
t-value and p-value
Confidence Intervals

Loading data with Numpy
Comparing Numpy with Traditional Lists
Numpy Data Types
Indexing and Slicing
Copies and Views
Numerical Operations with Numpy
Matrix Operations on Numpy Arrays
Aggregations functions
Shape Manipulations
Broadcasting
Statistical operations using Numpy
Resize, Reshape, Ravel
Image Processing with Numpy

Basics of Pandas
Loading data with Pandas
Series
Operations on Series
Data Framesand Operations of Data Frames
Selection and Slicing of Data Frames
Descriptive statistics with Pandas
Map, Apply, Iterations on Pandas Data Frame
Working with text data
Multi-Index in Pandas
GroupBy Functions
Merging, Joining and Concatenating Data Frames
Visualization using Pandas

Anatomy of Matplotlib figure
Plotting Line plots with labels and colors
Adding markers to line plots
Histogram plots
Scatter plots
Size, Color, and Shape selection in Scatter plots.
Applying Legend to Scatter plots
Displaying multiple plots using subplots
Boxplots, scatter_matrix and Pair plots
Basic Plotting using Seaborn
Violin Plots
Box Plots
Cat Plots
Facet Grid
Swarm Plot
Pair Plot
Bar Plot
LM Plot
Variations in LM plot using hue, markers, row, and columns

Exploratory Data Analysis
Pipeline ideas
Exploratory Data Analysis
Feature Creation
Evaluation Measures Data Analytics Cycle ideas
Data Acquisition
Data Preparation
Data cleaning
Data Visualization
Plotting
Model Planning & Model Building data

Reading and writing data to text files
Reading data from a CSV
Reading data from JSON Data preparation
Selection and Removal of Columns
Transform
Rescale
Standardize
Normalize
Binarize
One hot Encoding
Imputing
Train, Test Splitting

Introduction to Machine Learning
Applications of Machine Learning
Supervised Machine Learning
Classification
Regression
Unsupervised Machine Learning
Reinforcement Learning
Latest advances in Machine Learning
Model Representation
Model Evaluation
Hyper Parameter tuning of Machine Learning Models. Evaluation of ML Models.
Estimating and Prediction of Machine Learning Models Deployment strategy of ML Models.

Supervised Machine Learning - Classification

Classification methods & respective evaluation
K Nearest Neighbors
Decision Trees
Naive Bayes
Stochastic Gradient Descent
SVM
Linear
Non linear
Radial Basis Function
Random Forest
Gradient Boosting Machines
XGboost
Logistic regression Ensemble methods
Combining models
Bagging
Boosting
Voting
Choosing best classification method Model Tuning
Train Test Splitting
K-fold cross-validation
Variance bias tradeoff
L1 and L2 norm
Overfit, underfit along with learning curves variance bias sensibility using graphs
Hyper Parameter Tuning using Grid Search CV Respective performance measures
Different Errors (MAE, MSE, RMSE)Accuracy, Confusion Matrix, Precision, Recall

Supervised Machine Learning - Regression

Regression
Linear Regression
Variants of Regression
Lasso
Ridge
Multi Linear Regression
Logistic Regression (effectively, classification only)
Regression Model Improvement
Polynomial Regression
Random Forest Regression
Support Vector Regression Respective performance measures

Different Errors (MAE, MSE, RMSE)

Mean Absolute Error
Mean Square Error
Root Mean Square Error Unsupervised Machine Learning Clustering
K means
Hierarchical Clustering
DBSCAN
Association Rule Mining
Association Rule Mining.
Market Basket Analysis using Apriori Algorithm
Dimensionality reduction using Principal Component Analysis (PCA)
Natural Language Processing
Text Analytics
Stemming, Lemmatization, and Stop word removal. POS tagging and Named Entity Recognition
Bigrams, Ngrams, and colocationsTerm Document Matrix
Count Vectorizer

Term Frequency and Advanced Analytics

Time series
Time-series Analysis.
ARIMA example Recommender Systems
Content Based Recommendation
Collaborative Filtering
Reinforcement Learning
Basic concepts of Reinforcement Learning Action
Reward
Penalty Mechanism Feedback loop Deep Q Learning
Artificial Intelligence

Neural Networks& terminologies
Nonlinearity problem,illustration
Perceptron learning
Feed ForwardNetwork and Back propagation
Gradient Descent
Mathematics of Artificial neural networks
Gradients
Partial derivatives
Linear algebra o Li
LD
Eigen vectors
Projections
Vector quantization
Overview of tools used in Neural Networks
Tensor Flow
Keras Deep Learning Deep Learning
Tensorflow & Kerasinstallation
More elaboratediscussion on cost function
Measuring accuracyof hypothesis function
Role of gradient functionin minimizing cost function
Explicit discussion of Bayes models
Hidden MarkovModels (HMM)
Optimization basics
Sales Prediction of a Gaming company using Neural Networks
Build an Image similarity engine.

Architecture of CNN
Types of layers in CNN
Different Filtersand Kernals
Building an Image classifier with and without CNN Recurrent neural nets
Fundamental notions & ideas o Recurrent neurons
Handling variablelength sequences
Training a sequence classifier
Training to predict Time series

Introduction to CloudComputing
Amazon Web Services Preliminaries - S3, EC2, RDS
Big data processing on AWS using Elastic Map Reduce (EMR) Machine Learning using Amazon Sage Maker
Deep Learning on AWS Cloud
Natural Language processing using AWS Lex
Analytics services on AWS Cloud
Data Warehousing on AWS Cloud Creating Data Pipelines on AWS Cloud

Introduction to DevOps for Data Science Tasks in Data Science Development Deploying Models in Production
Deploying Machine Learning Models as Services Running Machine Learning Services in Containers Scaling ML Services with Kubernetes

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Data Science Masters Program Trainers Profile

Our trainers are 8+ years of expertise in Data Science field, they have vast experience working on projects based on real-world scenarios.
Our certified professionals with massive practical and theoretical knowledge of data science techniques.
As part of their training, trainers guide students with interview techniques and assistance in developing a great resume.
Trainers are also expertised in corporate training for top organisations and helped data science teams, agile coaches and product owners. Our trainers will train you in real time projects with hands-on assistance. Experts have previous experience in implementing data science projects with technologies like Keras, Tensorflow, Theano, Py-Torch

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Key Features for Data Science Master Program Training

Real-Time Experts

At BTree, you will receive training from real-world professionals who are passionate about sharing their knowledge with students. Call us and Book your Seat Now!

Live Project

We make sure that every student receives hands-on experience by working on live projects and real-world case studies, which increases their chances of getting hired.

Placement Support

We provide mock interview questions PDFs and guaranteed placement assistance. We have worked with small and medium-sized organizations to help you start your career.

Certifications

Get a global certificate that will help you become ready for a job in a high-demand industry and will boost the value of your resume.

Affordable Fees

We offer courses at a very reasonable fee structure. The course fee at BTree is not only reasonable, but you can also pay in installments.

Flexibility

We provide flexible scheduling to ensure that our students are comfortable while taking the course. You can choose any time that works best for you.

Data Science Master Certification

Data science master course certification is a professional credential that demonstrates the candidate’s ability to gain entire topic knowledge in addition to understanding all of the fundamental tools and algorithms used by Data Science specialists.
The Data science master training certificate will increase the value of your resume. As a consequence, you will have access to a diverse range of job opportunities. With the aid of this Certificate, you will obtain new skills in the creation of advanced concepts in Data Science.
The course syllabus includes all areas of Machine Learning, Statistics, Python programming, Big Data Analytics, R, Cloud Computing, etc.

Data Science Masters Program Training Certificate

Your Career, Your Way Job Opportunities in Data Science Masters Program

You can Work as a

Data Architect
Data Scientist
Machine Learning Engineer

Salary after Data Science Masters Program Certification

Devops Engineer

Avg Salary- 23,00,000

Data Science Masters Program Engineer

Avg Salary- 10,10,000

Senior Devops Engineer

Avg Salary- 7,40,000

Data Science Masters Program Training Options

Our ultimate aim is to bring the best in establishing the career growth of the students in each batch individually. To enhance and achieve this, we have highly experienced and certified trainers to extract the best knowledge on Data Science Masters Program Certification. Eventually, we offer three modes of training options for the students to impart their best innovations using the Data Science Masters Program & course skills. For more references and to choose a training mode, Contact our admission call at 044 - 4560 5237

Live Classroom Training

40 Hours of Offline Training
Real-Time Trainer Assistance
Ample Tech Equipped Classrooms
Non-Crowded Training Batches
Work on Live projects offline
Flexible Timings for sessions

Live Interactive Online Training

40 Hours of Real-Time Training
Dedicated Learning Platform
Chat and Discussion Panel for Assistance
Create and Complete Assignments
Work on Live Projects Online
Flexible Timings for sessions

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Knowledge Hub with Additional Information of Data Science Masters Program

Benefits of Data Science: Data is being created at an alarming rate in today’s environment. Every second, a large amount of data is created, whether it is from Facebook users or other social networking sites, phone calls, or data generated by other businesses. Because of this massive volume of data, the area of Data Science provides a variety of advantages. Some of the benefits are as follows:
Various Job Opportunities: Because it is in high demand, it has created a plethora of job possibilities in its different domains. Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, and more titles are among them.
Advantages for businesses include: Data Science assists firms in determining how and when their products sell best, ensuring that things are always supplied to the appropriate location and right time. The organisation makes faster and better judgements in order to enhance efficiency and earn bigger revenues.
High-paying occupations and career prospects include: As Data Scientist continues to be the sexiest career, the pay for this position is also excellent. A Data Scientist earns an annual average pay of $106,000 per year, according to a Dice Salary Survey.
Advantages of Hiring: It has made it simpler to filter data and find the top people for a business. Big Data and data mining have aided recruiting teams in the processing and selection of CVs, aptitude tests, and games.

Now, in alphabetical sequence, let’s look at those tools and their benefits:

Algorithms.io.
This tool is a machine-learning (ML) resource that transforms raw data into real-time insights and actionable events, specifically in the context of machine-learning.
Advantages:
Because it is hosted on a cloud platform, it offers all of the SaaS benefits of scalability, security, and infrastructure.
Machine learning is made simple and accessible to developers and businesses.
Apache Hadoop
This free and open-source platform generates simple programming models and distributes large data sets across thousands of computer clusters. Hadoop is equally useful for research and production. Hadoop is ideal for complex computations.
Advantages:
• Open-source
• Extremely scalable
• There are several modules available.
• Failures are dealt with at the application layer.
Apache Spark
The most popular data science tool, this all-powerful analytics engine is also known as “Spark.” It is well-known for providing lightning-fast cluster computing. Spark has access to a variety of data sources, including Cassandra, HDFS, HBase, and S3. It can also easily handle big datasets.
Advantages:
• Over 80 high-level operators streamline the parallel app development process.
• Can be used interactively from the Scale, Python, and R shells.
• The advanced DAG execution engine provides in-memory processing and acyclic data flow.
BigML
This application is another top-rated data science resource that gives users with a fully interactive, cloud-based GUI environment perfect for processing ML algorithms. Depending on your needs, you may register a free or premium account, and the online interface is simple to use.
Advantages:
• A low-cost resource for developing complicated machine learning solutions.
• Predictive data patterns are transformed into clever, practical solutions that anybody may use.
• It may be deployed in the cloud or on-premises.
D3.js
D3.js is a JavaScript toolkit that allows you to create interactive visualisations in your web browser. It emphasises web standards in order to fully utilise all of the advantages of contemporary browsers without being hampered by a proprietary framework.
Advantages:
• D3.js is based on the widely used JavaScript and is ideal for client-side
• Internet of Things (IoT) interactions.
• Useful for producing interactive visuals.
Data Robot
This technology is characterised as an advanced platform for automated machine learning. It is used by data scientists, executives, IT professionals, and software developers to help them construct better quality predictive models faster.
Advantages:
• You can train, test, and compare several different models with a single click or line of code.
• It includes Python SDK and APIs.
• It comes with a straightforward model deployment procedure.
Excel
Yes, even this ubiquitous old database worker receives some attention here! Originally designed by Microsoft for spreadsheet computations, it has now acquired broad usage as a tool for data processing, visualisation, and advanced calculations.
Advantages:
• With a single click, you may sort and filter your data.
• The Advanced Filtering option allows you to filter data depending on your own criteria.
• Well-known and ubiquitous
ForecastThis
This is the tool for you if you’re a data scientist looking for automated predictive model selection. Forecast This enables investment managers, data scientists, and quantitative analysts to leverage in-house data to maximise complicated future objectives and generate reliable projections.
Advantages:
• Simple to scale to any size task
• Algorithms for robust optimization are included.
• Simple API and spreadsheet plugins
BigQuery by Google
This is a highly scalable, server less data warehousing solution designed for efficient data analysis. It does super-fast SQL queries on append-only tables using Google’s infrastructure-based computing power.
Advantages:
• Extremely quick
• Reduces expenses because customers just pay for storage and computer use.
• Simple to scale
Java
Java is a well-known object-oriented programming language that has been around for many years. It is straightforward, architecture-independent, secure, platform-independent, and object-oriented.
Advantages:
• If combined with Java 8 and Lambdas, it is suitable for huge science projects.
• Java includes a plethora of tools and packages that are ideal for machine learning and data research.
• Simple to grasp
MATLAB
MATLAB is a high-level language that works in conjunction with an interactive environment to do numerical calculation, programming, and visualisation. MATLAB is a strong tool and programming language that is great for graphics, math, and programming.
Advantages:
• Intuitive application
• It analyses data, builds models, and makes algorithms.
• It scales analysis to run on clouds, clusters, and GPUs with a few easy code modifications.
MySQL
MySQL, another well-known and widely used programme, is one of the most popular open-source databases accessible today. It’s great for retrieving information from databases.
Advantages:
• Users may simply save and access structured data.
• Java and other programming languages are supported.
• It is a relational database management system that is open source.
NLTK
Natural Language Toolkit (NLTK) is an open-source tool that deals with human language data and is a popular Python application builder. NLTK is perfect for beginning data scientists and students.
Advantages:
• It includes a set of text processing libraries.
• Over 50 simple interfaces are available.
• It includes an active discussion forum where you may learn a lot of new things.
Quick Miner
This data science solution is a single platform that combines data preparation, machine learning, and model deployment to simplify and accelerate data science activities. It is widely used in the industrial, telecommunications, utilities, and financial sectors.
Advantages:
• All of the materials are housed on a single platform.
• The GUI method is built on a block diagram, which simplifies these blocks into a plug-and-play environment.
• Models machine learning algorithms using a visual workflow designer.
SAS
This data science tool is specifically built for statistical procedures. It is a closed-source proprietary software application for huge enterprises that specialises in processing and analysing vast volumes of data. It is well-supported by its corporation and quite dependable. Still, you get what you pay for with SAS, which is pricey and best suited for large corporations and organisations.
Advantages:
• There are several analytics functions that span anything from social media to automated forecasting to geographical data.
• It has interactive dashboards and reports that allow the user to go directly from reporting to analysis.
• Advanced data visualisation techniques such as auto charting are used to provide attractive findings and data.

Because data science encompasses a wide range of subjects, machine learning falls under the purview of data science. Machine learning employs a variety of approaches, including regression and supervised clustering. In contrast, the data’ in data science may or may not be generated by a machine or a mechanical process. The key distinction between the two is that data science, as a larger phrase, encompasses not just algorithms and statistics, but also the complete data processing approach.
Data science is the synthesis of several parent disciplines, including data analytics, software engineering, data engineering, machine learning, predictive analytics, data analytics, and others. It encompasses the retrieval, collecting, ingestion, and processing of massive volumes of data, often known as big data. Data science is responsible for adding structure to huge data, searching for compelling patterns, and advising decision-makers on how to successfully implement changes to meet business goals. Data analytics and machine learning are two of the several tools and methods employed by data science.

• Data Science and Data Analytics both deal with Big Data in their own ways. Data Science is a broad term that includes Data Analytics. Mathematics, Statistics, Computer Science, Information Science, Machine Learning, and Artificial Intelligence are all part of Data Science.
• Data mining, data inference, predictive modelling, and ML algorithm development are all used to discover patterns from complicated information and turn them into effective business strategies. Data analytics, on the other hand, is primarily concerned with Statistics, Mathematics, and Statistical Analysis.
• While Data Science seeks significant connections across big datasets, Data Analytics seeks to elucidate the specifics of derived insights. In other words, Data Analytics is a subset of Data Science that focuses on providing more detailed solutions to the problems raised by Data Science.
• Data Science tries to answer new and unique questions that might drive corporate innovation. Data Analysis, on the other hand, seeks answers to these questions and determines how they might be implemented inside a company to stimulate data-driven innovation.

• Data mining is the process of collecting useful data from valuable data sources.
• Selecting features, creating and optimising classifiers with machine learning tools.
• Performing structured and unstructured data preparation Improving data gathering techniques to capture all important information for constructing analytic systems.
• Data processing, cleaning, and validation to ensure data integrity for analysis.
• Analysing massive volumes of data to discover patterns and solutions.
• Making predictions and machine learning algorithms.
• Clear presentation of results.
• Provide answers and methods for dealing with corporate difficulties.
• Work with the business and IT teams to achieve your goals.

The average beginning salary for a Data Scientist is roughly 3.8 Lakhs per year (31.7k per month). A Data Scientist must have at least one year of experience.
An Entry Level Data Scientist with less than three years of experience receives an annual salary of 9.6 Lakhs. A mid-career Data Scientist with 4-9 years of experience makes an average pay of 14.8 Lakhs per year, while an experienced Data Scientist with 10-20 years of experience earns an average salary of 20.5 Lakhs per year.

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Success Stories Meet Our Placed Candidates

TCS

Btree for your career switch to data science. My trainer gives day wise assignment and algorithm of machine learning also provide us our backup classes. And they provide good placement support with guidance. Now I placed in reputed company. Thanks to btree!

Data Scientist

Saravanan Kumar

HCL

I had worked in business analysis and project management for 11-12 years before to taking the course, then I switched to data science. I made a terrific transfer with this and discovered my area of expertise. The best online training for Data science with hands-on projects. Thank you btree!

Data Architect

Srinivas

Accenture

I enrol 3-month data science course at btree systems great experience to learn. The flexible timing is very convenient to me to attend the classes without any distractions. Now I successfully completed my data science certification. Now I placed as a Machine learning engineer at Amazon. Thanks to btree providing a good training.

Machine Learning Engineer

Divya

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FAQ of Data Science Master Course

With the increase in technology, various tech-based food savers are experiencing an increase in demand. Due to the high-paying career opportunities in the industry, students are especially drawn to course data science. Every day, a massive amount of data is generated, transferred, and sourced, and it must be handled. Therefore, companies require qualified persons to their organization.

Data science is a difficult field. There are several reasons for this, the most notable of which is that it requires a diverse range of skills and expertise. Algorithms and software engineering are examples of computer science. The second half of the equation is domain knowledge, which implies understanding something about the field in which you engage.

• This programme was divided into three months:
• The first month is dedicated to data analysis.
• The second month is focused on machine learning.
• In month three, we’ll discover production-level technologies that data scientists use in the real world.
A three-month curriculum that will take you from complete novice to expert in the field of data science. You don’t even need to know how to code. We created it for anybody looking to develop their abilities and obtain paid job as soon as possible, whether through a full-time career or contract work. You’ll be studying a variety of technologies, like sequel Python Hadoop and even data storytelling, which all contribute to the overall data science pipeline.

BTree Systems are one of the leading IT training institutes in Chennai. We have trained students from various fields on technology. The below unique benefits.
• Fully equipped classes
• Hands-on training
• Experts devised course curriculum
• Adept Trainers Flexible batch timings
• Industry Exposure
• Reasonable Fee and Placement Assistance

Analytical minds who like coding and dealing with data are excellent candidates for data science training. People that are interested in machine learning, deep learning, and AI can also become data scientists.

After graduating, you can study about AI, deep learning, machine learning, coding languages, and other topics.

Yes, we providing internship for students after the course completion certificate, it will be assigned a live project that he must complete in a month.

Yes, you will need to have some experience of coding, OS - LINUX, and automation as data science incorporates software and IT sectors. It would be ideal to have knowledge of Python, PERL, and Java. Additionally, crucial is networking expertise.

BTree Systems provides active placement help to all students who successfully complete the programme. We have strong associations with over 50 leading MNCs globally. This will allow you to work for a variety of fantastic companies, like Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco. We can assist you with resume writing and interview preparation.

BTree accepts all major credit and debit cards. It also provides all available EMI options for you to choose from according on your preferences. You can speak with one of our advisers to learn more about the various EMI possibilities.

We accept all of the major types of payment choices, an email receipt will be delivered immediately for all of our courses.
MasterCard.
Online banking, Google Pay, PhonePe, PayPal, and Paytm.

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Real People, Real Feedback Hear from Our Students

Nusrath Fathima

One of the best data science training courses in Chennai is offered by BTree Systems. I signed up for their training with placement support service, which was excellent when the course was through. It was an effective learning opportunity. Friendly instructor who also clarified questions with examples from real-world situations. Thank you for offering such great training!

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Muhammed Safvan

Good coaching centre for Data Science the course curriculum was up to date with the latest trends and tools. The training was provided with good support from the institute side and they were very helpful in placement assistance.

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Vaidehi vaidehi

Hadoop courses and training are not difficult to learn. If you find a Best Hadoop Training Institute in Chennai like BTree Systems in Chennai.

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Pavan Kumar

I recently took an excellent Azure administration course. I want to thank my trainer for helping with CV preparation and providing interview tips. Everything he taught was practical. We appreciate Btree systems providing a skilled professional trainer. I am really pleased with the top quality of your training services.

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