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Data Science Masters Program Training Course

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Beginner & Advanced level Classes.
Hands-On Learning in Data Science Master Program.
Best Practice for interview Preparation Techniques.
Lifetime Access for Student’s Portal, Study Materials, Videos & Top MNC Interview Question.
Affordable Fees with Best Curriculum Designed by Industrial Expert.
Delivered by 10+ years of Certified Expert | 13502+ Students Trained & 350+ Recruiting Clients.
Next Data science Master Program Training Batch to Begin this week – Enroll Your Name Now!

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Course Syllabus Data Science With R Program
₹ 22000
Course SyllabusData Science with Python
₹ 22000
Course SyllabusMachine Learning
₹ 16000
Course SyllabusTableau Training
₹ 16000
Course SyllabusBig Data Hadoop and Spark Developer
₹ 18000
Course SyllabusData Science Capstone
₹ 18000
Total Amount
~~₹ 112000~~
Discount Offered
₹ 50400
Amount To Pay
₹ 61600

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Upcoming Batches

29-Apr-2024

Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

24-Apr-2024

Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

27-Apr-2024

Sat,Sun

Weekend Regular

(10:00 AM - 01:30 PM)

(Class 3hr - 3:30Hrs) / Per Session

27-Apr-2024

Sat,Sun

Weekend Fasttrack

(09:00 AM - 02:00 PM)

(Class 4:30Hr - 5:00Hrs) / Per Session

Outline of Data Science Masters Program Training

We train students for interviews and Offer Placements in corporate companies
Suitable for Graduates and Experienced Candidates from any Technical Background
Trainers have more than 10+ years of experience in the Data Science Master Programming domain
Outstanding Lab Facility and Infrastructure
We Exhibitions Students to Industry best practices with Aptitude & SoftSkills
Guidance for Resume & Interviews Preparation
We act as recruiting partners to provide Qualified candidates for top corporates in the Market
Learning Concepts: Data Science with R Programming, Data Science with Python, Machine Learning, Tableau Training, Big Data Hadoop and Spark Developer, Data Science Capstone.
BEGIN YOUR CAREER WITH DATA SCIENCE MASTER PROGRAM THAT GETS YOU A JOB OFFER UPTO 10 LACS IN JUST 60 DAYS!

Classroom Batch Training
One To One Training
Online Training
Customized Training
Enroll Now

This is How ACTE Students Prepare for Better Jobs

Course Objectives

Is Data Science Master Program a good career choice?

A Data Science Masters Program helps you grab the knowledge on predictive Predictive Analytics using Python, Machine Learning, Data Visualization, Big Data, Natural Language Processing technologies. After completing the training, you will begin your career as Data Analyst, Data Scientist, Data Engineer, Product Analyst, etc.

What is the scope of Data Science Master Program?

A Masters Program certified Data Engineers work with multiple technologies on handling the data. They can handle more aspects of a project than average analysts. They cut costs for companies because they can do the work of many specialists alone.

What background knowledge is necessary?

You should have some basic knowledge of Data analytics. This Specialization is for Data Engineer Professionals seeking to enter the industry to handle a variety of data and experienced technical professionals are also welcome.

Will ACTE Help Me With Placements After My Data Science Master Program Course Completion?

We are happy and proud to say that we have a strong relationship with over 900+ Small, Mid-Sized, and Top MNCs. Many of these companies have openings for Data Engineers. Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.

Is it difficult to become a Data Science Master Program?

Being a Decision analyst plays an efficient role to deals with a lot of varieties of data. You need to recognize, understand, implements the data analytics. It's something any motivated data engineer can do.

What are the prerequisites for learning Data Science Master Program?

Concepts of Data Science with R Programming.
Concepts of Data Science with Python.
Advanced concepts of Machine Learning
Guidance for Tableau Training.
Big Data Hadoop and Spark Developer.
Guidance for Data Science Capstone

Does Data Science Master Program require coding?

A Data Engineers implements the data with data handling tools like Hadoop and Cassandra, etc. Knowledge of various Programming technologies like R-Programming, Python, and Machine Learning.

Will I Be Given Sufficient Practical Training In Data Science Master Program?

Our courseware Program gives a hands-on approach to the students in Data Science Masters Program. This course deals with theory classes that teach the basics of modules followed by high intensity, Practical Sessions reflecting the current challenges that fulfill the Industrial Needs.

Is it worth learning Data Science Master Program?

A Data Science Master program certified Data Engineers do exist. However, you have to be careful when assessing the skills of a Freshers Masters's developer. Being an impactful Data Analyst is not about familiarity; it's about an intuitive and deep understanding of both data and its handling tools.

How long would it take to master in Data Science Master Program?

Three-Four months is long enough to learn a considerable amount of the Data Science Masters Program. If the concern already knowing the basics of data analytics, then two months would be a generous amount of time to learn enough Data Science Masters Program meaningfully contribute professionally.

Top reasons to consider a career in Data Science Master Program?

High Demand. The demand for Data Science Master Program is high.
Great Pay. The average salary of Big Data Analayst in India is around 6 LPA.
Creative Flexibility. You know about multiple aspects of data handling tools.
Better Productivity.

About Data Analyst Master Program Course

We try to cover all the topics related to Data Science Master Program technology. It would be your unique destination to be a Master in Data Science Technologies.

Learning Data Science Masters Program can help open up many opportunities for your career. It is a great skill-set to have as many roles in the job market requires proficiency in DataScience Masters. This Data Science Masters Program can help you grab opportunities in top MNC Companies like Paypal, Capgemini, Accenture, Mphasis, CTS, and MindLabs all are hiring Data Scientists.

This course will focus on the core concepts with many more advanced topics. This course is going to be one of the most comprehensive online programs on ACTE. R-Programming, Python, advanced topics Machine Learning Essentials; THERE IS NO PROBLEM. Every Topic will be covered.

Key Features

ACTE offers Data science Master Program Training in more than 27+ branches with expert trainers. Here are the key features,

40 Hours Course Duration
100% Job Oriented Training
Industry Expert Faculties
Free Demo Class Available
Completed 500+ Batches
Certification Guidance

Authorized Partners

ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Singapore.

Data Science Masters Program Course Content

Syllabus of Data Science with R

Module 1: Introduction to Data Science Methodologies

Data Types
Introduction to Data Science Tools
Statistics
Approach to Business Problems
Numerical Categorical
R, Python, WEKA, RapidMiner

Module 2: Correlation / AssociationRegressionCategorical variables

Introduction to Correlation Spearman Rank Correlation
OLS Regression – Simple and Multiple Dummy variables
Multiple regression
Assumptions violation – MLE estimates
Using UCI ML repository dataset or Built-in R dataset

Module 3: Data Preparation

Data preparation & Variable identification
Advanced regression
Parameter Estimation / Interpretation
Robust Regression
Accuracy in Parameter Estimation
Using UCI ML repository dataset or Built-in R dataset

Module 4: Logistic Regression

Introduction to Logistic Regression
Logit Function
Training-Validation approach
Lift charts
Decile Analysis
Using UCI ML repository dataset or Built-in R dataset

Module 5: Cluster AnalysisClassification Models

Introduction to Cluster Techniques
Distance Methodologies
Hierarchical and Non-Hierarchical Procedure
K-Means clustering
Introduction to decision trees/segmentation with Case Study
Using UCI ML repository dataset or Built-in R dataset

Module 6: Introduction and to Forecasting Techniques

Introduction to Time Series
Data and Analysis
Decomposition of Time Series
Trend and Seasonality detection and forecasting
Exponential Smoothing
Building R Dataset
Sales forecasting Case Study

Module 7: Advanced Time Series Modeling

Box – Jenkins Methodology
Introduction to Auto Regression and Moving Averages, ACF, PACF
Detecting order of ARIMA processes
Seasonal ARIMA Models (P,D,Q)(p,d,q)
Introduction to Multivariate Time-series Analysis
Using built-in R datasets

Module 8: File Input/Output

Live example/ live project
Using client given stock prices / taking stock price data

Module 9: Pharmaceuticals

Box – Jenkins Methodology
Case Study with the Data
Based on open set data

Module 10: Market Research

Case Study with the Data
Based on open set data

Module 11: Machine Learning

Supervised Learning Techniques
Conceptual Overview
Unsupervised Learning Techniques
Association Rule Mining Segmentation

Module 12: Fraud Analytics

Fraud Identification Process in Parts procuring
Sample data from online
Text Analytics

Module 13: Text Analytics

Sample text from online

Module 14: Social Media Analytics

Social Media Analytics
Sample text from online

Syllabus of Data Science with Python Course

Module 1: Introduction to Data Science

What is Data Science?
What is Machine Learning?
What is Deep Learning?
What is AI?
Data Analytics & it’s types

Module 2: Introduction to Python

What is Python?
Why Python?
Installing Python
Python IDEs
Jupyter Notebook Overview

Module 3: Python Basics

Python Basic Data types
Lists
Slicing
IF statements
Loops
Dictionaries
Tuples
Functions
Array
Selection by position & Labels

Module 4: Python Packages

Pandas
Numpy
Sci-kit Learn
Mat-plot library

Module 5: Importing data

Reading CSV files
Saving in Python data
Loading Python data objects
Writing data to csv file

Module 6: Manipulating Data

Selecting rows/observations
Rounding Number
Selecting columns/fields
Merging data
Data aggregation
Data munging techniques

Module 7: Statistics Basics

Central Tendency
Mean
Median
Mode
Skewness
Normal Distribution
Probability Basics
What does mean by probability?
Types of Probability
ODDS Ratio?
Standard Deviation
Data deviation & distribution
Variance
Bias variance Trade off
Underfitting
Overfitting
Distance metrics
Euclidean Distance
Manhattan Distance
Outlier analysis
What is an Outlier?
Inter Quartile Range
Box & whisker plot
Upper Whisker
Lower Whisker
catter plot
Cook’s Distance
Missing Value treatments
What is a NA?
Central Imputation
KNN imputation
Dummification
Correlation
Pearson correlation
Positive & Negative correlation
Error Metrics
Classification
Confusion Matrix
Precision
Recall
Specificity
F1 Score
Regression
MSE
RMSE
MAPE

Module 8: Machine Learning

Module 9: Supervised Learning

Linear Regression
Linear Equation
Slope<
Intercept
R square value
Logistic regression
ODDS ratio
Probability of success
Probability of failure
ROC curve
Bias Variance Tradeoff

Module 10: Unsupervised Learning

K-Means
K-Means ++
Hierarchical Clustering

Module 11: Other Machine Learning algorithms

Title
Base
Link
Style s
Script

Syllabus of Machine Learning Course

Module 1: Introduction to Data Analytics

Business Analytics, Data, Information
Understanding Business Analytics and R
Compare R with other software in analytics
Install R
Perform basic operations in R using command line
Learn the use of IDE R Studio
Use the ‘R help’ feature in R

Module 2: Introduction to R programming

Variables in R
Scalars
Vectors
Matrices
List
Data frames
Using c, Cbind, Rbind, attach and detach functions in R
Factors

Module 3: Data Manipulation in R

Data sorting
Find and remove duplicates record
Cleaning data
Recoding data
Merging data
Slicing of Data
Merging Data
Apply functions

Module 4: Data Import techniques in R

Reading Data
Writing Data
Basic SQL queries in R
Web Scraping

Module 5: Exploratory data Analysis

Box plot
Histogram
Pareto charts
Pie graph
Line chart
Scatterplot
Developing Graphs

Module 6: Basics of Statistics & Linear & Logistic Regression

Basics of Statistics
Inferencial statistics
Probability
Hypothesis
Standard deviation
Outliers
Correlation
Linear & Logistic Regression

Module 7: Data Mining: Clustering techniques, Regression & Classification

Introduction to Data Mining
Understanding Machine Learning
Supervised and Unsupervised Machine Learning Algorithms
K- means clustering

Module 8: Anova & Sentiment Analysis

Anova
Sentiment Analysis

Module 9: Data Mining: Decision Trees and Random Forest

Decision Tree
Concepts of Random Forest
Working of Random Forest
Features of Random Forest

Module 10: Project work

Syllabus of Tableau Course

Module 1: Tableau Course Material

Start Page
Show Me
Connecting to Excel Files
Connecting to Text Files
Connect to Microsoft SQL Server
Connecting to Microsoft Analysis Services
Creating and Removing Hierarchies
Bins
Joining Tables
Data Blending

Module 2: Learn Tableau Basic Reports

Parameters
Grouping Example 1
Grouping Example 2
Edit Groups
Set
Combined Sets
Creating a First Report
Data Labels
Create Folders
Sorting Data
Add Totals, Sub Totals and Grand Totals to Report

Module 3: Learn Tableau Charts

Area Chart
Bar Chart
Box Plot
Bubble Chart
Bump Chart
Bullet Graph
Circle Views
Dual Combination Chart
Dual Lines Chart
Funnel Chart
Traditional Funnel Charts
Gantt Chart
Grouped Bar or Side by Side Bars Chart
Heatmap
Highlight Table
Histogram
Cumulative Histogram
Line Chart
Lollipop Chart
Pareto Chart
Pie Chart
Scatter Plot
Stacked Bar Chart
Text Label
Tree Map
Word Cloud
Waterfall Chart

Module 4: Learn Tableau Advanced Reports

Dual Axis Reports
Blended Axis
Individual Axis
Add Reference Lines
Reference Bands
Reference Distributions
Basic Maps
Symbol Map
Use Google Maps
Mapbox Maps as a Background Map
WMS Server Map as a Background Map

Module 5: Learn Tableau Calculations & Filters

Calculated Fields
Basic Approach to Calculate Rank
Advanced Approach to Calculate Ra
Calculating Running Total
Filters Introduction
Quick Filters
Filters on Dimensions
Conditional Filters
Top and Bottom Filters
Filters on Measures
Context Filters
Slicing Fliters
Data Source Filters
Extract Filters

Module 6: Learn Tableau Dashboards

Create a Dashboard
Format Dashboard Layou
Create a Device Preview of a Dashboard
Create Filters on Dashboard
Dashboard Objects
Create a Story

Module 7: Server

Tableau online.
Overview of Tableau Server.
Publishing Tableau objects and scheduling/subscription.

Syllabus of Big Data Hadoop with Spark Developer

Module 1: The big picture of Big Data

Necessity of Big Data and Hadoop in the industry
Paradigm shift - why the industry is shifting to Big Data tools
Different dimensions of Big Data
Data explosion in the Big Data industry
Various implementations of Big Data
Different technologies to handle Big Data
Traditional systems and associated problems
Future of Big Data in the IT industry

Module 2: Demystifying Hadoop

Why Hadoop is at the heart of every Big Data solution
Introduction to the Big Data Hadoop framework
Hadoop architecture and design principles
Ingredients of Hadoop
Hadoop characteristics and data-flow
Components of the Hadoop ecosystem
Hadoop Flavors – Apache, Cloudera, Hortonworks, and more

Module 3: Setup and Installation of Hadoop

Hadoop environment setup and pre-requisites
Hadoop Installation and configuration
Working with Hadoop in pseudo-distributed mode
Troubleshooting encountered problems
Hadoop environment setup on the cloud (Amazon cloud)
Installation of Hadoop pre-requisites on all nodes
Configuration of masters and slaves on the cluster
Playing with Hadoop in distributed mode

Module 4: HDFS – The Storage Layer

Module 5: HDFS – The Storage Layer

The need for a distributed processing framework
Issues before MapReduce and its evolution
List processing concepts
Components of MapReduce – Mapper and Reducer
MapReduce terminologies- keys, values, lists, and more
Hadoop MapReduce execution flow
Mapping and reducing data based on keys
MapReduce word-count example to understand the flow
Execution of Map and Reduce together
Controlling the flow of mappers and reducers
Optimization of MapReduce Jobs
Fault-tolerance and data locality
Working with map-only jobs
Introduction to Combiners in MapReduce
How MR jobs can be optimized using combiners

Module 6: MapReduce - Advanced Concepts

Anatomy of MapReduce
Hadoop MapReduce data types
Developing custom data types using Writable & WritableComparable
InputFormats in MapReduce
InputSplit as a unit of work
How Partitioners partition data
Customization of RecordReader
Moving data from mapper to reducer – shuffling & sorting
Distributed cache and job chaining
Different Hadoop case-studies to customize each component
Job scheduling in MapReduce

Module 7: Hive – Data Analysis Tool

The need for an adhoc SQL based solution – Apache Hive
Introduction to and architecture of Hadoop Hive
Playing with the Hive shell and running HQL queries
Hive DDL and DML operations
Hive execution flow
Schema design and other Hive operations
Schema-on-Read vs Schema-on-Write in Hive
Meta-store management and the need for RDBMS
Limitations of the default meta-store
Using SerDe to handle different types of data
Optimization of performance using partitioning
Different Hive applications and use cases

Module 8: Pig - Data Analysis Tool

The need for a high level query language - Apache Pig
How Pig complements Hadoop with a scripting language
What is Pig
Pig execution flow
Different Pig operations like filter and join
Compilation of Pig code into MapReduce
Comparison - Pig vs MapReduce

Module 9: NoSQL Database - HBase

NoSQL databases and their need in the industry
Introduction to Apache HBase
Internals of the HBase architecture
The HBase Master and Slave Model
Column-oriented, 3-dimensional, schema-less datastores
Data modeling in Hadoop HBase
Storing multiple versions of data
Data high-availability and reliability
Comparison - HBase vs HDFS
Comparison - HBase vs RDBMS
Data access mechanisms
Work with HBase using the shell

Module 10: Data Collection using Sqoop

The need for Apache Sqoop
Introduction and working of Sqoop
Importing data from RDBMS to HDFS
Exporting data to RDBMS from HDFS
Conversion of data import/export queries into MapReduce jobs

Module 11: Data Collection using Flume

What is Apache Flume
Flume architecture and aggregation flow
Understanding Flume components like data Sources and Sinks
Flume channels to buffer events
Reliable & scalable data collection tools
Aggregating streams using Fan-in
Separating streams using Fan-out
Internals of the agent architecture
Production architecture of Flume
Collecting data from different sources to Hadoop HDFS
Multi-tier Flume flow for collection of volumes of data using AVRO

Module 12: Apache YARN & advanced concepts in the latest version

The need for and the evolution of YARN
YARN and its eco-system
YARN daemon architecture
Master of YARN – Resource Manager
Slave of YARN – Node Manager
Requesting resources from the application master
Dynamic slots (containers)
Application execution flow
MapReduce version 2 application over Yarn
Hadoop Federation and Namenode HA

Module 13: Exploring Scala

Introducing Scala
Installation and configuration of Scala
Developing, debugging, and running basic Scala programs
Various Scala operations
Functions and procedures in Scala
Scala APIs for common operations
Loops and collections- Array, Map, List, Tuple
Pattern-matching and Regex
Eclipse with Scala plugin

Module 14: Object-Oriented and Functional Programming

Introduction to OOP - object oriented programming
Different oops concepts
Constructors, getters, setters, singletons; overloading and overriding
Nested Classes and visibility Rules
Functional Structures
Functional programming constructs
Call by Name, Call by Value

Module 15: Big Data and the need for Spark

Problems with older Big Data solutions
Batch vs Real-time vs in-Memory processing
Limitations of MapReduce
Apache Storm introduction and its limitations
Need for Apache Spark

Module 16: A deep dive into Apache Spark

Introduction to Apache Spark
Architecture and design principles of Apache Spark
Spark features and characteristics
Apache Spark Ecosystem components and their insights

Module 17: Deploying Spark in local mode

Spark environment setup
Installing and configuring prerequisites
Installation of Spark in local mode
Troubleshooting encountered problems

Module 18: Apache Spark deployment in different modes

Spark installation and configuration in standalone mode
Installation and configuration of Spark in YARN mode
Installation and configuration of Spark on a real cluster
Best practices for Spark deployment

Module 19: Demystifying Apache Spark

Working on the Spark shell
Executing Scala and Java statements in the shell
Understanding SparkContext and the driver
Reading data from local file-system and HDFS
Caching data in memory for further use
Distributed persistence
Spark streaming
Testing and troubleshooting

Module 20: Learning RDDs in Spark

Introduction to Spark RDDs
How RDDs make Spark a feature rich framework
Transformations in Spark RDDs
Spark RDDs action and persistence
Lazy operations and fault tolerance in Spark
Loading data and how to create RDD in Spark
Persisting RDD in memory or disk
Pairing operations and key-value in Spark
Hadoop integration with Spark
Apache Spark practicals and workshops

Module 21: Spark Streaming

The need for stream analytics
Comparison with Storm and S4
Real-time data processing using streaming
Fault tolerance and checkpointing in Spark
Stateful Stream Processing
DStream and window operations in Spark
Spark Stream execution flow
Connection to various source systems
Performance optimizations in Spark

Module 22: Spark MLlib and Spark GraphX

Introducing Scala
Installation and configuration of Scala
Developing, debugging, and running basic Scala programs
Various Scala operations
Functions and procedures in Scala
Scala APIs for common operations
Loops and collections- Array, Map, List, Tuple
Pattern-matching and Regex
Eclipse with Scala plugin

Module 23: Spark SQL

Introduction to Spark SQL
Apache Spark SQL Features and Data flow
Architecture and components of Spark SQL
Hive and Spark together
Data frames and loading data
Hive Queries through Spark
Various Spark DDL and DML operations
Performance tuning in Spark

Module 24: Real Life Hadoop & Spark Project

Live Apache Spark & Hadoop project using Spark & Hadoop components to solve real-world Big Data problems in Hadoop & Spark.

Syllabus of Data Science Capstone Course

Module 1

Ignite Talk
Statement of work

Module 2

Milestone #1 Presentation
Summary Report + technical report
Self-/peer- evaluation
Review another group's reports
Code (runs as advertised)

Module 3

Milestone #2 Presentation ("Midterm")
Summary Report + technical report
Self-/peer- evaluation
Review another group's reports
Code (runs as advertised)

Module 4

Milestone #3 Presentation
Summary Report + technical report
Self-/peer- evaluation
Review another group's reports
Code (runs as advertised)

Module 5

Final Presentation to class
Final write-up via blog
Poster and video recording
Self-/peer- evaluation
Code (runs, is organized and readable)

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Need customized curriculum?

Hands-on Real Time Projects in Data Science

Project 1

Customer Segmentation project

Sentiment Analysis is to gain deeper insights into the customers’ opinions and emotions about different products and services.

Project 2

Trip History dataset

Trip History dataset is to classify whether the user is a permanent or a random member it, uses decision tree concepts.

Project 3

Health status prediction

This project is designed to predict the health status based on massive datasets.

Project 4

Time Series Analysis Dataset

Identifying the character of the development delineate by the sequence of observations, and prognostication.

Our Top Hiring Partner for Placements

ACTE offers placement opportunities as add-on to every student / professional who completed our classroom or online training. Some of our students are working in these companies listed below.

We are associated with top organizations like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc. It make us capable to place our students in top MNCs across the globe
We have separate student’s portals for placement, here you will get all the interview schedules and we notify you through Emails.
After completion of 70% Data science Master Program training course content, we will arrange the interview calls to students & prepare them to F2F interaction
Data science Master Program Trainers assist students in developing their resume matching the current industry needs
We have a dedicated Placement support team wing that assist students in securing placement according to their requirements
We will schedule Mock Exams and Mock Interviews to find out the GAP in Candidate Knowledge

Recently Placed Students in TOP MNC

IT WAS GREAT LEARNING EXPERIENCE

Sandeep Kumar D ( Software Engineer, Cisco Systems )

It was great learning experience. Trainer had provided relevant examples & truely enjoyed the sessions, I wholeheartedly thank Training & Placement Cell for the constant support

APPRECIATE THE PLACEMENT CELL AT ACTE FOR THE EFFORTS

Vinoth P ( UI Developer, TCS )

I would like to appreciate the placement cell at ACTE for the efforts they made for providing a fair and ample amount of chances to me. boot-camps and extra classes for us to enhance our technical and interpersonal skills.

I FEEL GREAT STUDYING AT ACTE AND GETTING GREAT OPPORTUNITY

Vijeyalakshme kannan ( QA Engineer, Amazon Development Center )

I feel great studying at ACTE and getting great opportunity as well as support from faculties & placement officers. It is dream come true, being an IT engineer

FIRST EVER OPPORTUNITY THAT CAME INTO MY LIFE

Pranav Srinivas ( Software Testing, Capgemini )

My sincere appreciation & gratitude to the Training & Placement Department and all staffs of ACTE for their efforts in imparting quality technical and aptitude training. I am very grateful to them for effectively and sincerely helping me to grab first ever opportunity that came into my life.

GOOD CORPORATE EXPOSURE IS GIVEN TO STUDENTS

Prasanna ( SAP Admin, HCL )

I am very Thankful to the faculties of ACTE as they are very much supportive, helpful and the Placement Cell which is also doing great job by arranging excellent placements of MNCs & good corporate exposure is given to students.

Last 3 Month's Placed Students

Be a Certified Expert in Data Science Masters Program

Acte Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher's as well as corporate trainees.

Our certification at Acte is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC's of the world. The certification is only provided after successful completion of our training and practical based projects.

Complete Your Course

a downloadable Certificate in PDF format, immediately available to you when you complete your Course

Get Certified

a physical version of your officially branded and security-marked Certificate.

Get Certified

About Experienced Tranier in DataScience Masters Programming

Our Data science Master Program Training Course : Trainers are certified professionals with 7+ years of experience in their respective domain as well as they are currently working with Top MNCs.
As all Trainers are Data science Master Program domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
All our Trainers are working with companies such as Cognizant, Dell, Infosys, IBM, L&T InfoTech, TCS, HCL Technologies, etc.
Trainers are also help candidates to get placed in their respective company by Employee Referral / Internal Hiring process.
Our trainers are industry-experts and subject specialists who have mastered on running applications providing Best Data science Master Program training to the students.
We have received various prestigious awards for Data science Master Program Training recognized IT organizations.

Java Course Reviews

Our ACTE Reviews are listed here. Reviews of our students who completed their training with us and left their reviews in public portals and our primary website of ACTE & Video Reviews.

NANCY RAMKUMAR

Studying

I had completed my Data Science Masters Program with the advance by ACTE. The best place to excel in the next phase of your career. The trainers and faculty here were very friendly and comfortable. My trainer had explained each and every topic clearly and he won't move to another topic until I was clear in it. Thanks to my trainer and ACTE for this wonderful opportunity!!

CHANDRAN KANNAN

Software Engineer

It is a great curse for beginners and for filling in some gaps for intermediate programmers. I wouldn't recommend it though for advanced programmers as the knowledge is rather basic and even at x2.0 speed it will become boring. Again, great course overall of beginners who want to get started with the Data Science Masters Program. Thanks!

AMAN ADITHYA

Software Engineer

This course is really great, it's very awesome that the course constantly get updated and you get answers in Q&A very fast, also instructor explanation is very good and clear, this is the only course you need to Data Science Masters Program and Mr.Venket does a really good job on that, the only con for me was that it didn't have much theory covered it went right to code without showing slides about how it works, but thankfully they are adding them now, so this is really a 5-star course, thank you ACTE for these amazing courses.

KEERTHANA V

Studying

One of the best Programming courses that I have taken in a long time, ACTE has put a lot of effort into making the course easy to learn, even for a Data Science Masters Program Novice like me, which makes it truly a masterclass course to attend, and the responses from Goran and others have been very helpful.

RAMYA PANDIAN

Software Testing

This Course is really a masterclass, covers from basic to advanced level. Would definitely recommend it to anyone interested in learning Data science Master Program. All concepts explained in a well-structured course. Tim teaches the concepts really well with coding examples. Follow along with him on code, you will learn a lot on writing a professional code too!.. Thanks, ACTE & Team

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Data Science Master Program Course FAQs

Looking for better Discount Price?

Call now: +9193833 99991 and know the exciting offers available for you!

Does ACTE provide placement?

ACTE is the Legend in offering placement to the students. Please visit our Placed Students List on our website
We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.
More than 3500+ students placed in last year in India & Globally
ACTE conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
85% percent placement record
Our Placement Cell support you till you get placed in better MNC
Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions

Is ACTE certification good?

ACTE

Certificate

Certification is Accredited by all major Global Companies
ACTE is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore

Work On Live Projects?

The entire Data Science Master Program training has been built around Real Time Implementation
You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio
GitHub repository and Showcase to Recruiters in Interviews & Get Placed

Who are the Trainers?

All the instructors at ACTE are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by ACTE for providing an awesome learning experience.

What if I miss one (or) more class?

No worries. ACTE assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.

What are the modes of training offered for this Data Science Master Program Course?

We offer this course in “Class Room, One to One Training, Fast Track, Customized Training & Online Training” mode. Through this way you won’t mess anything in your real-life schedule.

Why Should I Learn Data Science Master Program Course At ACTE?

Data Science Master Program Course in ACTE is designed & conducted by Data Science Master Program experts with 10+ years of experience in the Data Science Master Program domain
Only institution in India with the right blend of theory & practical sessions
In-depth Course coverage for 60+ Hours
More than 50,000+ students trust ACTE
Affordable fees keeping students and IT working professionals in mind
Course timings designed to suit working professionals and students
Interview tips and training
Resume building support
Real-time projects and case studies

Can I Access the Course Material in Online?

Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.

What certification will I receive after course completion?

You will receive ACTE globally recognized course completion certification Along with National Institute of Education (NIE), Singapore.

How Old Is ACTE?

We have been in the training field for close to a decade now. We set up our operations in the year 2009 by a group of IT veterans to offer world class IT training & we have trained over 50,000+ aspirants to well-employed IT professionals in various IT companies.

What Will Be The Size Of A Data Science Master Program Batch At ACTE?

We at ACTE believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each Data Science Master Program batch to 5 or 6 members

Will I Be Given Sufficient Practical Training In Data Science Master Program?

Our courseware is designed to give a hands-on approach to the students in Data Science Master Program. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.

How Do I Enroll For The Data Science Master Program Course At ACTE?

You can contact our support number at 93833 99991 / Directly can do by ACTE.in's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India

Corporate Training

If you want to give the Trending technology experience to your esteemed employees, we are here to help you!

Group Discount

If you have Three or more people in your training we will be delighted to offer you a group discount.

3 to 4 Peoples

10%

5 to 9 Peoples

15%

10+ Peoples

20%