Data Science Masters Program Training Course - ACTE Technologies update2024 | Updated 2025
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Data Science Masters Program Training Course

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Rated #1 Recognized as the No.1 Institute for Data Science Masters Program Training course

Expand your career opportunities with our Data Science Masters Program Training course. This comprehensive training, led by industry experts, equips you with cutting-edge skills in machine learning, deep learning, data analytics, and visualization, enabling you to build scalable and data-driven solutions for business success.

The Data Science Masters Program Course provides hands-on experience with essential tools like Python, R, SQL, TensorFlow, and Power BI, preparing you for real-world applications and projects. Receive personalized mentorship, earn your Data Science Masters Program certification, and take advantage of our dedicated job placement support to secure top roles in this rapidly expanding industry.

  • Enhance your expertise with our Data Science Master Program.
  • With over 350+ hiring partners and 15,000+ graduates successfully placed.
  • Get Data Science Master Program placement support and elevate your professional profile.
  • Learning through live projects makes our course the perfect platform to advance your career.
  • Our curriculum is designed to meet industry standards, ensuring your job-ready upon completion.
  • Join our Data Science Master Program Online Training today to unlock unparalleled career growth.
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  • Course 1

    Data Science with R

  • Course 2

    Data Science with Python

  • Course 3

    Machine Learning

  • Course 4

    Tableau Course

  • View 5 more courses

  • Course 5

    Big Data Hadoop with Spark Developer

  • Course 6

    Data Science Capstone

  • Course 7

    Advanced Deep Learning

  • Hands-on Project

    (Data Cleaning, Machine Learning, Data Visualization)

  • Add On

    (Recorded Video, Group Discussion, 1-on-1 Sessions)

  • Job Preparation

    (Aptitude, Resume Preparation, Mock Interview)

  • Master’s Certificate

    *Distinct certificates awarded for each course.

Upcoming Batches

28-Apr-2025
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

30-Apr-2025
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

03-May-2025
Sat,Sun

Weekend Regular

(10:00 AM - 01:30 PM)

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

04-May-2025
Sat,Sun

Weekend Fasttrack

(09:00 AM - 02:00 PM)

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

    Hear it from our Graduate

    Have Cracked Their Dream Job in Top MNC Companies

    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

    Course Objectives

    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.

    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.

    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.

    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.

    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.

    • 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

    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.

    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.

    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.

    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.
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    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.

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    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 .

    Data Science Master 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: Module 2: Correlation / Association Regression Categorical 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 Cours
    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
    • K – Nearest Neighbour
    • Naïve Bayes Classifier
    • Decision Tree – CART
    • Decision Tree – C50
    • Random Forest
    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 Rank
    • 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 Layout
    • 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 Course
    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
    • 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 5: 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 6: 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 7: 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 8: 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 9: 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 10: 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 11: 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 12: 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 13: 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 14: 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 15: 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 16: Deploying Spark in local mode
    • Spark environment setup
    • Installing and configuring prerequisites
    • Installation of Spark in local mode
    • Troubleshooting encountered problems
    Module 17: 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 18: 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
    • Disturbuted persistence
    • Spark streaming
    • Testing and troubleshooting
    Module 19: Learning RDDs in Spark
    • What is an RDD?
    • Creating RDDs
    • Transformations
    • Actions
    • RDD Persistence
    • Fault Tolerance
    • Example Workflow
    • Best Practices
    Module 20: 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 21: 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 22: 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 23: 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)
    Syllabus of Deep Learning Course
    Module 1: Introduction to Deep Learning
    • Overview of Deep Learning
    • Deep Learning vs. Traditional Machine Learning
    • Key Concepts: Neural Networks, Activation Functions, and Layers
    • Applications of Deep Learning in various industries
    Module 2: Artificial Neural Networks (ANN)
    • Introduction to ANN
    • Structure of a Neural Network
    • Forward Propagation and Backward Propagation
    • Loss Functions and Optimization Techniques
    • Training Neural Networks: Batch, Stochastic, and Mini-batch Gradient Descent
    Module 3: Convolutional Neural Networks (CNN)
    • Introduction to CNNs
    • Convolutional Layers, Pooling, and Fully Connected Layers
    • Activation Functions in CNNs
    • Applications of CNNs: Image Classification, Object Detection
    • Hands-on Implementation of CNNs using TensorFlow/Keras
    Module 4: Recurrent Neural Networks (RNN)
    • Introduction to RNNs
    • Understanding Sequence Data and Time Series Prediction
    • Long Short-Term Memory (LSTM) Networks
    • Gated Recurrent Unit (GRU) Networks
    • Applications of RNNs: Language Modeling, Text Generation
    • Implementing RNNs and LSTMs using TensorFlow/Keras
    Module 5: Generative Adversarial Networks (GANs)
    • Introduction to GANs
    • Architecture of GANs: Generator and Discriminator Networks
    • Training GANs: Challenges and Solutions
    • Applications of GANs: Image Generation, Style Transfer, Data Augmentation
    • Hands-on Implementation of GANs using TensorFlow/Keras
    Module 6: Deep Learning for Natural Language Processing (NLP)
    • Overview of NLP with Deep Learning
    • Word Embeddings: Word2Vec, GloVe
    • Sequence-to-Sequence Models
    • Attention Mechanisms and Transformers
    • Applications of Deep Learning in NLP: Sentiment Analysis, Machine Translation
    • Implementing NLP models using TensorFlow/Keras
    Module 7: Advanced Deep Learning Techniques
    • Transfer Learning and Pre-trained Models
    • Autoencoders and Dimensionality Reduction
    • Reinforcement Learning Basics
    • Deep Reinforcement Learning
    • Hyperparameter Tuning and Model Optimization
    • Deploying Deep Learning Models in Production
    Module 8: Deep Learning Capstone Project
    • Define a real-world problem
    • Develop a project plan
    • Implement a Deep Learning solution
    • Test and optimize the model
    • Present the project and its findings
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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

    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.

    Data Science Master Program Course FAQs

    Looking for better Discount Price?

    Call now: +91-7669 100 251 and know the exciting offers available for you!
    • 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
      ACTE Gives Certificate For Completing A Course
    • 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
    • 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
    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.
    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.
    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
    Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.
    You will receive ACTE globally recognized course completion certification Along with project experience, job support, and lifetime resources.
    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.
    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
    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.
    You can contact our support number at +91-7669 100 251 / Directly can do by ACTE.in's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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          Other Category Placements
          • Non-IT to IT (Career Transition) 2371+
          • Diploma Candidates3001+
          • Non-Engineering Students (Arts & Science)3419+
          • Engineering Students3571+
          • CTC Greater than 5 LPA4542+
          • Academic Percentage Less than 60%5583+
          • Career Break / Gap Students2588+
          Top Placement Record

        Job Opportunities in Data Science

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