About Digital Lync

Digital Lync

Best Data Masters Program

  • Get Certified at the Best Data Masters Program Institute.
  • Get trained by industry experts.
  • Data Masters Classroom and Online training.
  • 20+ Real-time projects
  • Real time projects, Interviews and Job Support
25000+ Students Enrolled
4.7 Rating (500) Ratings
45 Days Duration

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Roles for Data Masters Program
  • Data Scientist
  • Data Engineer
  • Machine Learning Engineer
  • BigData Engineer
  • Data Architect

Why Data Masters Program?

  • Learn Think of a business that relies on quick, agile decisions to stay competitive, and most likely data masters analytics is involved in making that business tick. 53% Of Companies Are Adopting Data Masters
  • Acces The average base pay for at least six big data skills itself is well over $120,000 a year.
  • Keyfeature Data Masters has seen massive exponential growth leading to numerous career opportunities.

Why Data Masters Program at Digital Lync?

  • Learn Digital Lync offers one of the best Data Masters Program in Hyderabad with a comprehensive course curriculum.
  • Acces Elevate your practical knowledge with quizzes, assignments, Competitions and Hackathons to give a boost to your confidence with our hands-on Big Data Training.
  • Acces Data Masters Program in Hyderabad at Digital lync makes you industry ready with coaching sessions, interview prep advice, and resume with 1-1 Mentoring.

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Course Curriculum

It stretches your mind, think better and create even better.

4V ( Volume, Velocity, Variety and Veracity) characteristics

Structured and Unstructured Data

Application and use cases of Big Data

Limitations of traditional large Scale systems

How a distributed way of computing is superior (cost and scale)

Opportunities and challenges with Big Data

Introduction to Linux and Big Data Virtual Machine (VM)

Introduction to Linux - Why Linux? -

Windows and the Linux equivalents

Different flavors of Linux

Unity Shell (Ubuntu UI)

Basic Linux

Commands (enough to get started with Hadoop)

HDFS Overview and Architecture

Deployment Architecture

Name Node, Data Node and Checkpoint Node ( aka Secondary Name Node)

Safe mode

Configuration files

HDFS Data Flows ( Read v/s Write)

Load Balancer

Dist Cp

HDFS Federation

HDFS High Availability

Hadoop Archives

CRC Checksum

Data replication

Rack awareness and Block placement policy

Small files problem

Command Line Interface

File System

Web Interface

Legacy MR v/s Next Generation MapReduce ( aka YARN/ MRv2)

Slots v/s Containers


Shuffling, Sorting

Hadoop Data Types

Input and Output Formats

Input Splits - Partitioning ( Hash Partitioner v/s Customer Partitioner)

Speculative execution


JVM Reuse


Word Count

Term Frequency

Inverse Document Frequency

Log Data Analysis

Different ways of joining data

Purchases Data Analysis

Max Temperature


Inverted Index

Introduction and Architecture

Different Modes of executing Pig constructs

Data Types

Dynamic invokers Pig streaming Macros

Pig Latin language Constructs (LOAD, STORE, DUMP, SPLIT, etc)

User Defined Functions

Use Cases

Introduction and Architecture

Different Modes of executing Hive queries

Metastore Implementations

HiveQL (DDL & DML Operations)

External v/s Managed Tables Views

Partitions & Buckets

Joins, Group by, Order by

User Defined Functions

Review of RDBMS

Need for NoSQL

Brewers CAP Theorem


Schema on Read vs. Schema on Write

Different levels of consistency

Key Value




HBase Architecture

Master and Region Server

Catalog Tables (Root and Meta)

HBase Data Modeling

Loading data in HBase

Apache Sqoop

Data movement from Relational databases to Hadoop

Sqoop Commands

Sqoop Advanced features

Apache Flume

Components of Flume

Log Data ingestion to Hadoop

Introduction to RDD

Installation and Configuration of Spark

Spark Architecture

Different interfaces to Spark

Data frames and Datasets

Querying massive data using SparkSql

Sample Python programs in Spark

Data Visualization using Apache Zeppelin

Cloudera Hadoop cluster on the Amazon Using EMR (Elastic Map Reduce)

Using EC2 (Elastic Compute Cloud)

Importing/ exporting data across RDBMS and HDFS using Sqoop

Getting real- time events into HDFS using Flume

Creating workflows in Oozie

Introduction to Graph processing

Graph processing with Neo4J

Processing data in real time using Storm

Interactive Adhoc querying with Impala

Introduction to Excel

Customizing tabs, options in excel

Name managers

Data validation: Options in data validation for list, whole numbers, dates

Using name manager for data validation


Custom sorting


Sorting left to right

Advanced sorting with multiple options

Advanced filter options

Sorting and filtering with color options

Pivot tables

Short cuts to create pivot tables

Changing row and column labels

Custom format tables and default tables

Changing number formats

Value field settings and summarizing values by 11 different options

Value field settings and showing values as different options

Grouping of continuous fields

Pivot charts, compared regular charts

Slicers, Slicer settings, advanced slicer

Calculated fields in pivot tables

Vlookup and Hlookup

Syntax for Vlookup

What-if there are errors

Vlookup with data validation

Approximation for Vlookup

Using column function for dynamic column numbers

Using Choose function to select a table for Vlookup

Using Match function to identify column number

Locking cells for absolute and relative cells


Auto sum functions

Logical functions

Text functions

Date and Time functions

Lookup and reference functions

Information functions


Column charts

Line charts

Pie charts, Pie in pie and Bar of pie, Donut

Stacker bar and clustered bar charts


Scatter charts

Radar charts

Tree maps





Conditional formatting

Duplicate values

Alternate rows

Multiple criteria

Negative numbers

Gantt charts and Formula

Advanced topics

What-if analysis

Text to columns

Flash fill

Remove duplicates, Consolidate

Grouping, ungrouping and sub-totals

Freezing and unfreezing panes


Understanding the start pane

Connecting to data source

Data sources that can be connected

Various file formats



Connecting to excel, Joins, Splitting data

Live and extract

Dimensions and measures

Clearing sorts and filters

Views: Standard fit width and height

Drilling down

Expanding the marks in pane

Swapping axis

Renaming sheets

Editing color pane

Adding highlighters

Understanding show me

Sorting and hierarchy

Data pane and analytics pane

Different view options at bottom of sheet

Managing data and extracts

Hiding and unhiding fields

Creating folders to move dimensions and measures

Adding default colors and properties

Adding multiple data sources

Extracting workbook

Replacing data sources

Data cleansing

Database joins


Sorting and Filtering

Default charts

Highlighter for color and shape

Sorting from axis, color, category, manually and clearing sort

Creating groups from pane, manually, visually, parameters, and bins

Adding filter, show filter, wildcards, Top N parameters

Discrete and continuous dates

Types of filters: Applying to specific sheets, Editing page shelf

Hiding cards

Sets and parameters



Tool tips

Cluster analysis


Dashboards and storyboards

Building dashboards

Hiding and unhiding sheets

Interface between sheets, dashboard and storyboard

Elements in dashboard


Actions in dashboard

Device designer

Story points


Word cloud

Bump charts

Box and whisker


Step and Line






Heat map


Show me charts


Basic syntax

Regular calc and table calc

Adding totals

Date calc

Logic calc

String calc

Number calc





Converting geo to non-geo

Chart default

Options for maps

Unrecognized locations




Importing data

Scripting basics

Configuring sheets

Creating and configuring sheets

List boxes

Input boxes

Pivots and Sets

Pivot tables

Set analysis

Lookup and apply map functions


List boxes



Resident load

Text objects

Statistics box

Creating PDFs

Scripting basics

Cyclic dimensions

Multiple topics

Table box

Multi box

Button objects

Combo charts


Bar charts

Line charts

Pie charts

Scatter charts

Load inline


Straight table


Block charts

Trellis bar

Grid charts




Interchanging bar and Line



Magic quadrant

Heat map

References in chart

Custom labels


Before and after functions



Container objects

Search objects

Creating blank database

Creating table at backend table design view

Adding data

Creating select query


Query criteria

Action queries

Create queries

Delete query

Table query

Parameter queries


Defining relationships between tables





Calculated expressions



Summarizing data







Query wizard


Multiple forms

Split forms

Modify forms


Navigation forms

Combo box

SQL view


Controls and properties

Reports basics

Formatting reports







And Or Not

Order By

Insert into

Null values




Min and Max

Count, Avg, sum













Group By



Create table

Drop table

Alter table

Not null


Primary key





Connecting to database, basic transformation

Managing queries, splitting columns

Modifying data types


Re-ordering columns, conditional columns

Connecting to folders

Merge queries

Query dependency

Transform data

Enter data

Query parameter

Data modeling

Data modeling

Data relationships

Calculated columns

Quick measures

Optimize models

Calculated measures


Calculated tables

Time intelligence

Insert new data tables


Grouping and Binning


Create and format visualizations

Hierarchical axis and concatenating


Combo charts


Clustering data


Data behind visualization

Date slicers

Maps, ESRI maps

Tables and matrixes

Table style

Scatter charts

Waterfall and funnel charts

Gauge, cards and KPIs

Coloring charts

Shapes, grid lines, page settings

Duplicates, categories with no data

Aligning, hierarchies


Combo charts

Custom visual store

Date slicer

Waterfall and funnel charts

Grouping and binning

Map visualizations

Report measures

Tables and matrices

Turn off visual headers

Persistent filters

Sync slicers


Custom date tables

Default summarization

Measure filtering

Excel data and collaboration

Upload an excel table

Excel data with BI content

Pinning excel tables

Analyze in excel


Power BI service

Overview of dashboards

Publish from Power BI

Get insights

Pinning on dashboards, dashboard tiles, display options

Adding widgets

Navigating content

Filter the list in navigating pane

Dashboard settings

Sharing, print and export dashboards

Focus mode

Export to csv or excel or ppt

Notifications, alerts



Description: Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. In this first module we will introduce to the field of Data Science and how it relates to other fields of data like Artificial Intelligence, Machine Learning and Deep Learning.

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

Description: Mathematics is very important in the field of data science as concepts within mathematics aid in identifying patterns and assist in creating algorithms. The understanding of various notions of Statistics and Probability Theory are key for the implementation of such algorithms in data science.

Linear Algebra

Matrices, Matrix Operations

Eigen Values, Eigen Vectors

Scalar, Vector and Tensors

Prior and Posterior Probability

Conditional Probability


Differentiation, Gradient and Cost Functions

Graph Theory

Description: This module focuses on understanding statistical concepts required for Data Science, Machine Learning and Deep Learning. In this module, you will be introduced to the estimation of various statistical measures of a data set, simulating random distributions, performing hypothesis testing, and building statistical models.

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 (Skewness and 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

Python for Data Science



Matplotlib & Seaborn

Jupyter Notebook


NumPy is a Python library that works with arrays when performing scientific computing with Python. Explore how to initialize and load data into arrays and learn about basic array manipulation operations using NumPy.

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


Statistical operations using Numpy

Resize, Reshape, Ravel

Image Processing with Numpy


Pandas is a Python library that provides utilities to deal with structured data stored in the form of rows and columns. Discover how to work with series and tabular data, including initialization, population, and manipulation of Pandas Series and DataFrames.

Basics of Pandas

Loading data with Pandas


Operations on Series

DataFrames and Operations of DataFrames

Selection and Slicing of DataFrames

Descriptive statistics with Pandas

Map, Apply, Iterations on Pandas DataFrame

Working with text data

Multi Index in Pandas

GroupBy Functions

Merging, Joining and Concatenating DataFrames

Visualization using Pandas

Data Visualization using Matplotlib

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+

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

Data Visualization using Seaborn

Seaborn is a data visualization library that provides a high-level interface for drawing graphs. These graphs are able to convey a lot of information, while also being visually appealing.

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 col

Exploratory Data Analysis helps in identifying the patterns in the data by using basic statistical methods as well as using visualization tools to displays graphs and charts. With EDA we can assess the distribution of the data and conclude various models to be used.

Pipeline ideas

Exploratory Data Analysis

Feature Creation

Evaluation Measures

Data Analytics Cycle ideas

Data Acquisition

Data Preparation

Data cleaning

Data Visualization


Model Planning & Model Building

Data Inputting

Reading and writing data to text files

Reading data from a csv

Reading data from JSON

Data preparation

Selection and Removal of Columns






One hot Encoding


Train, Test Splitting

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. This module on Machine Learning is a deep dive to Supervised, Unsupervised learning and Gaussian / Naive-Bayes methods. Also you will be exposed to different classification, clustering and regression methods.

Introduction to Machine Learning

Applications of Machine Learning

Supervised Machine Learning



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 learning is one of the most popular techniques in machine learning. In this module, you will learn about more complicated supervised learning models and how to use them to solve problems.

Classification methods & respective evaluation

K Nearest Neighbors

Decision Trees

Naive Bayes

Stochastic Gradient Descent



Non linear

Radial Basis Function

Random Forest

Gradient Boosting Machines


Logistic regression

Ensemble methods

Combining models




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

Regression is a type of predictive modelling technique which is heavily used to derive the relationship between variables (the dependent and independent variables). This technique finds its usage mostly in forecasting, time series modelling and finding the causal effect relationship between the variables. The module discusses in detail about regression and types of regression and its usage & applicability


Linear Regression

Variants of Regression



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 learning can provide powerful insights on data without the need to annotate examples. In this module, you will learn several different techniques in unsupervised machine learning.


K means

Hierarchical Clustering


Association Rule Mining

Association Rule Mining.

Market Basket Analysis using Apriori Algorithm

Dimensionality reduction using Principal Component analysis (PCA)

Natural language is essential to human communication, which makes the ability to process it an important one for computers. In this module, you will be introduced to natural language processing and some of the basic tasks.

Text Analytics

Stemming, Lemmatization and Stop word removal.

POS tagging and Named Entity Recognition

Bigrams, Ngrams and colocations

Term Document Matrix

Count Vectorizer

Term Frequency and TF-IDF

Advanced Analytics covers various areas like Time series Analysis, ARIMA models, Recommender systems etc.

Time series

Time series Analysis.

ARIMA example

Recommender Systems

Content Based Recommendation

Collaborative Filtering

Reinforcement learning is an area of Machine Learning which takes suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.

Basic concepts of Reinforcement Learning



Penalty Mechanism

Feedback loop

Deep Q Learning

Artificial intelligence (AI) is the ability of a computer program or a machine to think and learn. It is also a field of study which tries to make computers "smart"

Artificial Neural Networks

Neural Networks & terminologies

Non linearity problem, illustration

Perceptron learning

Feed Forward Network and Back propagation

Gradient Descent

Mathematics of Artificial Neural Networks


Partial derivatives

Linear algebra



Eigen vectors


Vector quantization

Overview of tools used in Neural Networks

Tensor Flow


Deep learning is part of a broader family of machine learning methods based on the layers used in artificial neural networks. In this module, you’ll deep dive in the concepts of Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Auto Encoders and many more.

Deep Learning

Tensorflow & keras installation

More elaborate discussion on cost function

Measuring accuracy of hypothesis function

Role of gradient function in minimizing cost function

Explicit discussion of Bayes models

Hidden Markov Models (HMM)

Optimization basics

Sales Prediction of a Gaming company using Neural Networks

Build an Image similarity engine.

Deep Learning with Convolutional Neural Nets

Architecture of CNN

Types of layers in CNN

Different Filters and Kernels

Building an Image classifier with and without CNN

Recurrent neural nets

Fundamental notions & ideas

Recurrent neurons

Handling variable length sequences

Training a sequence classifier

Training to predict Time series

Cloud computing is massively growing in importance in the IT sector as more and more companies are eschewing traditional IT and moving applications and business processes to the cloud. This section covers detailed information about how to deploy Data Science models on Cloud environments.


Introduction to Cloud Computing

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

DevOps play a pivotal role in bridging the gap between Development and Operational teams. This section covers key DevOps tools which a Data Scientist need to be aware of for doing their day to day data science work.


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

Live Projects

Big Data Live Projects

HR Analytics for Attrition Prediction using Logistic Regression

Description: Employee Attrition is an important subject to gauge the satisfaction of the employee in a..

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Big Data Live Projects

Predicting Housing prices using Regression:

Predict the sales price for each house based on input features provided for the...

Read More
Big Data Live Projects

Retail Customer segmentation based on spending patterns

Customer analysis plays a crucial role in determining the profitability of Retail companies. Segmentation of the...

Read More
Big Data Live Projects

Stock market price prediction

This project deals with the predictions of stock market prices using history of Data. It also considers the physical factors...

Read More
Big Data Live Projects

Exploratory Data analysis of Crime records in Boston

This project analyses data using quantitative prediction of crimes in Boston and drawing visualizations of Trends in the...

Read More
Big Data Live Projects

Market Basket Analysis using Apriori Algorithm

Market Basket Analysis is a technique which identifies the strength of association between pairs of products purchased...

Read More
Data Analysis Projects

The History of the Superbowl

Super Bowl LIII was the lowest-scoring Super Bowl ever! Explore the results of past Super Bowl games in this visualization by Jeff Plattner.

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Data Analysis Projects

Which country is the happiest?

Did you know Norway ranks #1 in the world for happiness and freedom? In this visualization, Sara Hamdoun takes a look at...

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Data Analysis Projects

Gaming revenue by platform

Mobile games are currently dominating the gaming market...but will PCs ever make a comeback? In this #MakeoverMonday visualization...

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Data Analysis Projects

Is your country ready for autonomous vehicles?

The Autonomous Vehicles Readiness Index (AVRI) ranks countries on their preparedness to adopt self-driving...

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Data Analysis Projects

House Prices in England and Wales

How has the housing market changed since 1997? Explore this visualization by Rob Radburn to see the price of homes in England and Wales...

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Data Analysis Projects

How do we spend our time?

The average American lives for 79 years or 28,835 days. How many of these days are spent at work or in traffic? In this visualization,...

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Datascience Training

Build an XML to CSV converter using Python

This python project which will help students to brush up their basic python skills to build a real-world XML to CSV...

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Datascience Training

Building a Photo Editor from scratch using Flask and Numpy

Numpy is a versatile package to do data operations...

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Datascience Training

Exploratory Data Analysis on Retail Shop Sales data.

Performing exploratory data analysis to find patterns in data which will determine the approach to take in Machine...

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Datascience Training

Fruit type prediction using K Nearest Neighbors algorithm.

K Nearest Neighbor algorithm is used to predict the type of fruit given its mass, height, width and color...

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Datascience Training

Predict Malignancy in Mammographic Masses using Decision Tree Classifiers

Mammographic masses is a public dataset from UCI machine...

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Datascience Training

Predict whether a candidate will be shortlisted in H1B Visa process using Random Forest Algorithm.

Every year there are close...

Read More
Datascience Training

Predict Breast Cancer using Support Vector Machine algorithm

Wisconsin Breast Cancer dataset has 569 sample of Breast cancer observations determining Malignancy...

Read More
Datascience Training

HR Analytics for Attrition Prediction using Logistic Regression

Employee Attrition is an important subject to gauge the satisfaction of the employee in...

Read More
Datascience Training

Predicting Housing prices using Regression:

Predict the sales price for each house based on input features provided for the ...

Read More
Datascience Training

Social Network Ads based Prediction

Predicting if a user buys a specific product or not based on the ad populated on the Social...

Read More
Datascience Training

Retail Customer segmentation based on spending patterns

Customer analysis plays a crucial role in determining the profitability of Retail...

Read More
Datascience Training

Market Basket Analysis using Apriori Algorithm

Market Basket Analysis is a technique which identifies the strength of association between...

Read More
Datascience Training

SMS Spam Detection using Natural Language Processing

In our day-to-day lives, we receive a large number of spam/junk messages either in ...

Read More
Datascience Training

Sentiment analysis on Restaurant Reviews using Natural Language processing and Supervised Learning

It involves in identifying and...

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Datascience Training

Image Classification using Deep Learning

Classifying Images based on the features is a tough problem. With Deep Learning algorithms like CNN...

Read More
Datascience Training

Content Based Recommender Engine using Deep Learning

Content based recommender systems use the content in the data to segment items and...

Read More
Datascience Training

Chatbots using Recurrent Neural Networks and Deep Learning

A chatbot is an artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in...

Read More
Datascience Training

Stock market price prediction

This project deals with the predictions of stock market prices using history of Data. It also considers the physical factors...

Read More
Datascience Training

Exploratory Data analysis of Crime records in Boston

This project analyses data using quantitative prediction of crimes in Boston and drawing visualizations of Trends in the...

Read More

Case Studies

Data Science at Netflix
Data Science at Netflix

See how netflix uses data science to improve its recommendation system. how they leverage data science to provide personalized recommendations to its users.

Data Science in Education
Data Science in Education

Check out the modern way of learning of how data science is transforming the education system. Get an in-depth understanding of the diverse role of data science.


Our Data Masters Program has precisely been developed to reach out to the demand of the learners by keeping in mind the industry standards.
This Data Masters Program will particularly be helpful for the career advancement of the following audience -

Graduates from the College.

Currently working employees looking to upskill themselves.

Candidates looking for a change in the IT Field.

As such, there are no specific prerequisites for Data Masters program in Hyderabad. If you are familiar with programming and foundation skills with a sense of curiosity and willingness to learn you are all set for the Big Data training.

Data Masters Program Classes are conducted over the Weekdays and Weekends through classroom and online sessions. Please get in touch with the Digital Lync team to get the exact schedule and timings.

Our Data Masters Program faculty has over 12 years of experience.

Data Masters Course duration is 50 hours.

Weekday Data Masters Program classes will be one hour long and Weekend classes will be three hours long.

Please find the detailed Data Masters course curriculum in the Digital Lync Big Data training curriculum section.

Yes, we will assist our students with all the interview preparation techniques.

Life at Digital Lync

Life at Digital Lync

The environment at Digital Lync is colorful and creative. It is where ideas are incubated and generated. An apt place to explore yourself.

Happy Partners

You'll be in good company

Inspiring Student Stories.

Here are stories of real knowldege, real people,
under real innovation.

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