Learn the fundamental data science concepts. Join today to become a competent data science expert.

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  • Are you wondering what is Data science and what can be the career option for data science experts If yes, you have landed the right place to get replies to all your queries. Data science is a lucrative career option that witnesses amazing growth and endless career opportunities.
  • And data science experts are like a torch that can safeguard companies from many unwanted activities. The major job role of a data science expert is to analyze data from various sources and convert the same into meaningful content or information.
  • The focus of our Data Science online training is to deliver the same skills so that you can work as a superstar at your workplace. You will learn how to work with different concepts of computer science, how to make data visuals, programming skills in data science using R and Python languages, and many other things at the end of the Data Science Placement Course.
  • As the leading Data Science online training in India, we help to acquire advanced data science skills too that involve AI, Machine Learning, algorithms Designing, Hadoop skills, Tableau concepts, and more. In the end, you will find yourself more competent and claim for BIG salary packages that can change your life.

Data Science Online Training


  • The objective of this Data Science Certification Training Online is to deliver all essential skills that can make you future-ready.
    • Also, prepare you for the related data science certification and clear the exam in the first attempt mostly.

      You would be able to analyze all types of data either it is structured, unstructured, or complex data types, you could always drive meaningful from it.

      Being the reputed Data science online training institute in India, we help you to gain all data science skills from an initial level to the complex stage where you will find yourself more competent and confident as a data science expert.

      Also, learn the right set of tools, techniques, visualization methods, programming languages, AI, ML, algorithm designing, and other similar skills that will make your resume outshine the crowd.

      In the end, Data science online training can help you to gain all soft skills, intellectual skills, and communication skills to apply for various jobs and get hired quickly.

  • As a data scientist, you can earn really HUGE if you are skilled and competent. You will be amazed to know that there is a deficiency of 2Lac+ Data science professionals across the world and there are only a few professionals available so far. It makes the field more promising and lucrative when compared to other similar technology.
  • If you are an experienced guy then you will see a certain hike in the coming years. If you are completely new to the domain, still you are open to working in different roles and profiles. Here is an estimate by different job portals of what you can earn on the completion of the Data Science online course.
    • The average salary of a Data science expert is $139K as per Indeed.

      The average salary of a Data science expert is $113K as per Glassdoor.

      The average salary of a Data science expert is $100K as per PayScale.

  • The benefits don’t end here but you will experience gradual growth in your salary as a trained and certified professional. Being the prominent Data Science online training in India, we will help you to prepare for the top industries and acquire attractive salary packages by clearing your certification exam in the first attempt.

  • So, you have decided to join the Data science online training in India course but not sure of career growth after it. Let us help you with your doubts. the carer growth after Data Science training is tremendous especially you are certified and confident enough to handle all types of job roles. The best part is that Data Science online training is completely oriented to the certification program where you don’t have to learn anything extra to attempt the exam.
  • Moreover, you will become eligible to work with various industries and multiple industry verticals too. Get hired at attractive salary packages and establish yourself as an in-demand IT resource worldwide.

  • Data science is not a new name but an optimum path to the future when everything is coming online these days. Today billions of data are generated by industries and Data Science helps to drive meaningful insights from this data. If data is not taken care of well then it may result in business failure too. This is the reason why companies are always in a dire need of professionals who can complete their work much proficiently.
  • For all industry verticals, either it is healthcare, medical sector, telecom field, eCommerce industry, insurance sector, data is of utmost importance and it has to be analyzed profoundly. So, this task cannot be done professionally, if you are not skilled to do so. Our Data Science online training program will make you an in-demand IT resource and help you to acquire job options much quickly on the course completion.
  • Moving ahead, let us see some interesting facts about why exactly is it the hottest career option these days and why should you consider Data Science Training Online.
    • It is the number one IT skills with amazing salary packages.

      There are more than 190K jobs are predicted by the next year.

      The growth in the Data Science field may exceed more than $16 billion by the end of 2025.

      Thousands of students are taking Data Science online training these days.

      There is a quick requirement of data science professionals in almost every industry vertical.

  • As the superior Data Science Training institute online, our trainers are completely dedicated to delivering you all essential skills, so that you can start your career like a superstar and grow immensely.

  • As we discussed earlier too, you will become eligible to apply for various roles on the successful completion of the Data Science placement course in India. Each of the roles has different job duties. Let us have a quick look at common job roles and responsibilities in Data Science.
    • You must know how to design algorithms or patterns that can help you to understand or analyze data in a quick manner.

      Also, you must have a sound idea of programming languages too that include R and Python.

      You should know about various data handling techniques so that data can be converted into meaningful content.

      You must know about various data analytics tools and big data management techniques like Hadoop, Hive, etc.

      Also, you should have stronger technical, analytical, and logical skills, to establish yourself as a leader in the Data Science industry.

  • With this discussion, you must have a sound idea of job roles and responsibilities in Data Science. As the eminent Data Science Training Institute Online, we prepare to handle all these duties more effectively and provide 100 percent placement assistance too.

  • The top hiring industries in Data Science include IBM, Accenture, Amazon, Flipkart, Snapdeal, Microsoft, Google, Infosys, NETFLIX, FEDEX, American Express, and more.
  • And the Industry-Domain you can work in, as a Data Scientist on the successful completion of Data Science course Online includes, IT Sector, Healthcare and Medical Sector, Banking & Finance, Transportation, Travel Industry, eCommerce, Media & Entertainment, Non-Profit Industries, Insurance Sector, etc.
  • As the best Data Science training institute Online in India, we prepare you for all these industries and make you eligible for the various industry domains too. We assure you that you will feel more confident and able to crack interviews in the first attempt mostly.
  • Also, we provide 100 percent placement assistance, resume building guide, and important steps to prepare for an interview that includes a set of Data Science interview questions answers too.

  • On the completion of Data Science Online Course, you will get a completion certificate that is valid proof of your skills. If you attempt the global certification exam then it will make you more eligible among the crowd. Instead of your current position in the industry, it is always great moving ahead with the right career plan. So, take your first step towards the career and get accomplished as an expert data science professional in 2020 and beyond.

Why Should You Learn Data Science?

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TrackWeek DaysWeekendsFast Track
Course Duration 40-45 Days 7 Weekends 8 Days
Hours 1 Hrs. Per Day 2 Hrs. Per Day 6+ Hrs. Per Day
Training ModeClassroom/OnlineClassroom/OnlineClassroom/Online
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Data Science Certification Training

  • Data Science is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in data science, you may convert voluminous data into meaningful contents.
  • In this program you will learn:
    • Python Statistics for Data Science

      Databases – MySQL and SQL Queries

      Data Science Master’s Program

      Machine Learning

      Deep Learning

      Power BI

      Hadoop- Apache Spark and Scala Certification

      Tableau - For Data Visualization

      Data Science Masters - Live Projects

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  • Introduction To Python
    • Installation and Working with Python

      Understanding Python variables

      Python basic Operators

      Understanding the Python blocks.

  • Introduction To Variables
    • Variables, expression condition and function

      Global and Local Variables in Python

      Packing and Unpacking Arguments

      Type Casting in Python

      Byte objects vs. string in Python

      Variable Scope

  • Python Data Type
    • Declaring and usingNumeric data types

      Using stringdata type and string operations

      Understanding Non-numeric data types

      Understanding the concept of Casting and Boolean.






  • Introduction Keywords and Identifiers and Operators
    • Python Keyword and Identifiers

      Python Comments, Multiline Comments.

      Python Indentation

      Understating the concepts of Operators

  • Data Structure
    • List

      • What is List.
      • List Creation
      • List Length
      • List Append
      • List Insert
      • List Remove
      • List Append & Extend using “+” and Keyword
      • List Delete
      • List related Keyword in Python
      • List Revers
      • List Sorting
      • List having Multiple Reference
      • String Split to create a List
      • List Indexing
      • List Slicing
      • List count and Looping
      • List Comprehension and Nested Comprehension


      • Dict Creation
      • Dict Access (Accessing Dict Values)
      • Dict Get Method
      • Dict Add or Modify Elements
      • Dict Copy
      • Dict From Keys.
      • Dict Items
      • Dict Keys (Updating, Removing and Iterating)
      • Dict Values
      • Dict Comprehension
      • Default Dictionaries
      • Ordered Dictionaries
      • Looping Dictionaries
      • Dict useful methods (Pop, Pop Item, Str , Update etc.)
  • Sets, Tuples and Looping Programming
    • Sets

      • What is Set
      • Set Creation
      • Add element to a Set
      • Remove elements from a Set
      • PythonSet Operations
      • Frozen Sets


      • What is Tuple
      • Tuple Creation
      • Accessing Elements in Tuple
      • Changinga Tuple
      • TupleDeletion
      • Tuple Count
      • Tuple Index
      • TupleMembership
      • TupleBuilt in Function (Length, Sort)

      Control Flow

      • Loops
      • Loops and Control Statements (Continue, Break and Pass).
      • Looping techniques in Python
      • How to use Range function in Loop
      • Programs for printing Patterns in Python
      • How to use if and else with Loop
      • Use of Switch Function in Loop
      • Elegant way of Python Iteration
      • Generator in Python
      • How to use nested IF and Else in Python
      • How to use nested Loop in Python
      • Use If and Else in for and While Loop
      • Examples of Looping with Break and Continue Statements
      • How to use IN or NOTkeywordin Python Loop.
  • Exception and File Handling, Module, Function and Packages
    • Python Exception Handling

      • Python Errors and Built-in-Exceptions
      • Exception handing Try, Except and Finally
      • Catching Exceptions in Python
      • Catching Specific Exception in Python
      • Raising Exception
      • Try and Finally

      Python File Handling

      • Opening a File
      • Python File Modes
      • Closing File
      • Writing to a File
      • Reading from a File
      • Renaming and Deleting Files in Python
      • Python Directory and File Management
      • List Directories and Files
      • Making New Directory
      • Changing Directory

      Python Function, Modules and Packages

      • Python Syntax
      • Function Call
      • Return Statement
      • Write an Empty Function in Python –pass statement.
      • Lamda/ Anonymous Function
      • *argsand **kwargs
      • Help function in Python
      • Scope and Life Time of Variable in Python Function
      • Nested Loop in Python Function
      • Recursive Function and Its Advantage and Disadvantage
      • Organizing python codes using functions
      • Organizing python projects into modules
      • Importing own module as well as external modules
      • Understanding Packages
      • Programming using functions, modules & external packages
      • Map, Filter and Reduce function with Lambda Function
      • More example of Python Function
  • Data Automation (Excel, SQL, PDF etc)
    • Python Object Oriented Programming—Oops

      • Concept of Class, Object and Instances
      • Constructor, Class attributes and Destructors
      • Real time use of class in live projects
      • Inheritance, Overlapping and Overloading operators
      • Adding and retrieving dynamic attributes of classes
      • Programming using Oops support

      Python Database Interaction

      • SQL Database connection using
      • Creating and searching tables
      • Reading and Storing configinformation on database
      • Programming using database connections

      Reading an excel

      • Reading an excel file usingPython
      • Writing toan excel sheet using Python
      • Python| Reading an excel file
      • Python | Writing an excel file
      • Adjusting Rows and Column using Python
      • ArithmeticOperation in Excel file.
      • Plotting Pie Charts
      • Plotting Area Charts
      • Plotting Bar or Column Charts using Python.
      • Plotting Doughnut Chartslusing Python.
      • Consolidationof Excel File using Python
      • Split of Excel File Using Python.
      • Play with Workbook, Sheets and Cells in Excel using Python
      • Creating and Removing Sheets
      • Formatting the Excel File Data
      • More example of Python Function

      Working with PDF and MS Word using Python

      • Extracting Text from PDFs
      • Creating PDFs
      • Copy Pages
      • Split PDF
      • Combining pages from many PDFs
      • Rotating PDF’s Pages

      Complete Understanding of OS Module of Python

      • Check Dirs. (exist or not)
      • How to split path and extension
      • How to get user profile detail
      • Get the path of Desktop, Documents, Downloads etc.
      • Handle the File System Organization using OS
      • How to get any files and folder’s details using OS
  • Data Analysis & Visualization
    • Pandas

      • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
      • How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
      • Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
      • Exploring the Data Plotting, Correlations, and Histograms
      • Getting statistical information about the data Analysis Concepts, Handle the None Values
      • Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
      • Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
      • Applying formulas on the columns Basic Grouping, Concepts of Aggregate Function
      • Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (Setting Indices)
      • Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
      • Exporting the results to Excel Joins:
      • Python | Pandas Data Frame Inner Join
      • Under sting the properties of Data Frame Left Join (Left Outer Join)
      • Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
      • Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
      • Pandas | Find Missing Data and Fill and Drop NA Appending DataFrameand Data
      • Pandas | How to Group Data How to apply Lambda / Function on Data Frame
      • Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)


      • Introduction to NumPy: Numerical Python
      • Importing NumPy and Its Properties
      • NumPy Arrays
      • Creating an Array from a CSV
      • Operations an Array from aCSV
      • Operations with NumPy Arrays
      • Two-Dimensional Array
      • Selecting Elements from 1-D Array
      • Selecting Elements from 2-D Array
      • Logical Operation with Arrays
      • Indexing NumPy elements using conditionals
      • NumPy’sMean and Axis
      • NumPy’sMode, Median and Sum Function
      • NumPy’sSort Function and More


      • Bar Chart using Python MatPlotLib
      • Column Chart using Python MatPlotLib
      • Pie Chart using Python MatPlotLib
      • Area Chart using Python MatPlotLib
      • Scatter Plot Chart using Python MatPlotLib
      • Play with Charts Properties Using MatPlotLib
      • Export the Chart as Image
      • Understanding plt. subplots () notation
      • Legend Alignment of Chart using MatPlotLib
      • Create Charts as Image
      • Other Useful Properties of Charts.
      • Complete Understanding of Histograms
      • Plotting Different Charts, Labels, and Labels Alignment etc.

      Introduction to Seaborn

      • Introduction to Seaborn
      • Making a scatter plot with lists
      • Making a count plot with a list
      • Using Pandas with seaborn
      • Tidy vs Untidy data
      • Making a count plot with a Dataframe
      • Adding a third variable with hue
      • Hue and scattera plots
      • Hue and count plots

      Visualizing Two Quantitative Variables

      • Introduction to relational plots and subplots
      • Creating subplots with col and row
      • Customizing scatters plots
      • Changing the size of scatter plot points
      • Changing the style of scatter plot points
      • Introduction to line plots
      • Interpreting line plots
      • Visualizing standard deviation with line plots
      • Plotting subgroups in line plots

      Visualizing a Categorical and a Quantitative Variable

      • Current plots and bar plots
      • Count plots
      • Bar plot with percentages
      • Customizing bar plots
      • Box plots
      • Create and interpret a box plot
      • Omitting outliers
      • Adjusting the whiskers
      • Point plots
      • Customizing points plots
      • Point plot with subgroups

      Customizing Seaborn Plots

      • Changing plot style and colour
      • Changing style and palette
      • Changing the scale
      • Using a custom palette
      • Adding titles and labels: Part 1
      • Face Grids vs. Axes Subplots
      • Adding a title to a face Grid object
      • Adding title and labels: Part 2
      • Adding a title and axis labels
      • Rotating x-tics labels
      • Putting it all together
      • Box plot with subgroups
      • Bar plot with subgroups and subplots
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  • Python - MySQL
    • Introduction to MySQL

      What is the MySQLdb

      How do I Install MySQLdb

      Connecting to the MYSQL

      Selecting a database

      Adding data to a table

      Executing multiple queries

      Exporting and Importing data tables.

  • SQL Functions
    • Single Row Functions

      Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date

  • General Functions
    • Count, Average, Sum, Now etc.

  • Joining Tables
    • Obtaining data from Multiple Tables

      Types of Joins (Inner Join, Left Join, Right Join & Full Join)

      Sub-Queries Vs. Joins

  • Operators (Data using Group Function)
    • Distinct, Order by, Group by, Equal to etc.

  • Database Objects (Constraints & Views)
    • Not Null


      Primary Key

      Foreign Key

  • Structural & Functional Database Testing using TOAD Tool
    • SQL Basic

      • SQL Introduction
      • SQL Syntax
      • SQL Select
      • SQL Distinct
      • SQL Where
      • SQL And & Or
      • SQL Order By
      • SQL Insert
      • SQL Update
      • SQL Delete

      SQL Advance

      • SQL Like
      • SQL Wildcards
      • SQL In
      • SQL Between
      • SQL Alias
      • SQL Joins
      • SQL Inner Join
      • SQL Left Join
      • SQL Right Join
      • SQL Full Join
      • SQL Union

      SQL Functions

      • SQL Avg()
      • SQL Count()
      • SQL First()
      • SQL Last()
      • SQL Max()
      • SQL Min()
      • SQL Sum()
      • SQL Group By
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  • Introduction to Data Science
    • What is Analytics & Data Science

      Common Terms in Analytics

      What is data

      Classification of data

      Relevance in industry and need of the hour

      Types of problems and business objectives in various industries

      How leading companies are harnessing the power of analytics

      Critical success drivers

      Overview of analytics tools & their popularity

      Analytics Methodology & problem-solving framework

      List of steps in Analytics projects

      Identify the most appropriate solution design for the given problem statement

      Project plan for Analytics project & key milestones based on effort estimates

      Build Resource plan for analytics project

      Why Python for data science

  • Accessing/Importing and Exporting Data
    • Importing Data from various sources (Csv, txt, excel, access etc)

      Database Input (Connecting to database)

      Viewing Data objects - sub setting, methods

      Exporting Data to various formats

      Important python modules: Pandas

  • Data Manipulation: Cleansing - Munging Using Python Modules
    • Cleansing Data with Python

      Filling missing values using lambda function and concept of Skewness.

      Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.

      Normalizing data

      Feature Engineering

      Feature Selection

      Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.

      Label encoding/one hot encoding

  • Data Analysis: Visualization Using Python
    • Introduction exploratory data analysis

      Descriptive statistics, Frequency Tables and summarization

      Univariate Analysis (Distribution of data & Graphical Analysis)

      Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)

      Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)

      Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)

  • Introduction to Statistics
    • Descriptive Statistics

      Sample vs Population Statistics

      Random variables

      Probability distribution functions

      Expected value

      Normal distribution

      Gaussian distribution


      Central limit theorem

      Spread and Dispersion

      Inferential Statistics-Sampling

      Hypothesis testing

      Z-stats vs T-stats

      Type 1 & Type 2 error

      Confidence Interval

      ANOVA Test

      Chi Square Test

      T-test 1-Tail 2-Tail Test

      Correlation and Co-variance

  • Introduction to Predictive Modelling
    • Concept of model in analytics and how it is used

      Common terminology used in Analytics & Modelling process

      Popular Modelling algorithms

      Types of Business problems - Mapping of Techniques

      Different Phases of Predictive Modelling

  • Data Exploration for Modelling
    • Need for structured exploratory data

      EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

      Identify missing data

      Identify outliers’ data

      Imbalanced Data Techniques

  • Data Pre-Processing & Data Mining
    • Data Preparation

      Feature Engineering

      Feature Scaling


      Dimensionality Reduction

      Anomaly Detection

      Parameter Estimation

      Data and Knowledge

      Selected Applications in Data Mining

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  • Introduction to Machine Learning
    • Artificial Intelligence

      Machine Learning

      Machine Learning Algorithms

      Algorithmic models of Learning

      Applications of Machine Learning

      Large Scale Machine Learning

      Computational Learning theory

      Reinforcement Learning

  • Techniques of Machine Learning
    • Supervised Learning

      Unsupervised Learning

      Semi-supervised and Reinforcement Learning

      Bias and variance Trade-off

      Representation Learning

  • Regression
    • Regression and its Types

      Logistic Regression

      Linear Regression

      Polynomial Regression

  • Classification
    • Meaning and Types of Classification

      Nearest Neighbor Classifiers

      K-nearest Neighbors

      Probability and Bayes Theorem

      Support Vector Machines

      Naive Bayes

      Decision Tree Classifier

      Random Forest Classifier

  • Unsupervised Learning: Clustering
    • About Clustering

      Clustering Algorithms

      K-means Clustering

      Hierarchical Clustering

      Distribution Clustering

  • Model optimization and Boosting
    • Ensemble approach

      K-fold cross validation

      Grid search cross validation

      Ada boost and XG Boost

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  • Introduction to Deep Learning
    • What are the Limitations of Machine Learning

      What is Deep Learning

      Advantage of Deep Learning over Machine learning

      Reasons to go for Deep Learning

      Real-Life use cases of Deep Learning

  • Deep Learning Networks
    • What is Deep Learning Networks

      Why Deep Learning Networks

      How Deep Learning Works

      Feature Extraction

      Working of Deep Network

      Training using Backpropagation

      Variants of Gradient Descent

      Types of Deep Networks

      Feed forward neural networks (FNN)

      Convolutional neural networks (CNN)

      Recurrent Neural networks (RNN)

      Generative Adversal Neural Networks (GAN)

      Restrict Boltzman Machine (RBM)

  • Deep Learning with Keras
    • Define Keras

      How to compose Models in Keras

      Sequential Composition

      Functional Composition

      Predefined Neural Network Layers

      What is Batch Normalization

      Saving and Loading a model with Keras

      Customizing the Training Process

      Intuitively building networks with Keras

  • Convolutional Neural Networks (CNN)
    • Introduction to Convolutional Neural Networks

      CNN Applications

      Architecture of a Convolutional Neural Network

      Convolution and Pooling layers in a CNN

      Understanding and Visualizing CNN

      Transfer Learning and Fine-tuning Convolutional Neural Networks

  • Recurrent Neural Network (RNN)
    • Intro to RNN Model

      Application use cases of RNN

      Modelling sequences

      Training RNNs with Backpropagation

      Long Short-Term Memory (LSTM)

      Recursive Neural Tensor Network Theory

      Recurrent Neural Network Model

      Time Series Forecasting

  • Natural Language Processing
    • NLP with python

      Bags of words





      Sentiment Analysis

  • Overview of Tensor Flow
    • What is Tensor Flow

      Tensor Flow code-basics

      Graph Visualization

      Constants, Placeholders, Variables

      Tensor flow Basic Operations

      Linear Regression with Tensor Flow

      Logistic Regression with Tensor Flow

      K Nearest Neighbor algorithm with Tensor Flow

      K-Means classifier with Tensor Flow

      Random Forest classifier with Tensor Flow

  • Neural Networks Using Tensor Flow
    • Quick recap of Neural Networks

      Activation Functions, hidden layers, hidden units

      Illustrate & Training a Perceptron

      Important Parameters of Perceptron

      Understand limitations of A Single Layer Perceptron

      Illustrate Multi-Layer Perceptron

      Back-propagation – Learning Algorithm

      Understand Back-propagation – Using Neural Network Example


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  • Introduction to Power BI
    • Overview of BI concepts

      Why we need BI

      Introduction to SSBI

      SSBI Tools

      Why Power BI

      What is Power BI

      Building Blocks of Power BI

      Getting started with Power BI Desktop

      Get Power BI Tools

      Introduction to Tools and Terminology

      Dashboard in Minutes

      Refreshing Power BI Service Data

      Interacting with your Dashboards

      Sharing Dashboards and Reports

  • Power BI Desktop
    • Power BI Desktop

      Power BI Dashboards

      Power BI Q & A

      Extracting data from various sources

      Workspaces in Power BI

      Data Transformation

      Measures and Calculated Columns

      Query Editor

  • Modelling with Power BI
    • Introduction to Modelling

      Modelling Data

      Manage Data Relationship

      Optimize Data Models

      Cardinality and Cross Filtering

      Default Summarization & Sort by

      Creating Calculated Columns

      Creating Measures & Quick Measures

  • Data Analysis Expressions (DAX)
    • What is DAX

      Data Types in DAX

      Calculation Types

      Syntax, Functions, Context Options

      DAX Functions

      Date and Time

      Time Intelligence





      Text and Aggregate

      Measures in DAX

  • Publishing and Sharing
    • Introduction and Sharing Options Overview

      Publish from Power BI Desktop and Publish to Web

      Share Dashboard with Power BI Service

      Workspaces and Apps (Power BI Pro) and Content Packs (Power BI Pro)

      Print or Save as PDF and Row Level Security (Power BI Pro)

      Export Data from a Visualization and Publishing for Mobile Apps

      Export to PowerPoint and Sharing Options Summary

  • Refreshing Datasets
    • Understanding Data Refresh

      Personal Gateway (Power BI Pro and 64-bit Windows)

      Replacing a Dataset and Troubleshooting Refreshing

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  • Introduction to Big Data Hadoop and Spark
    • What is Big Data

      Big Data Customer Scenarios

      Understanding BIG Data: Summary

      Few Examples of BIG Data

      Why BIG data is a BUZZ

      How Hadoop Solves the Big Data Problem

      What is Hadoop

      Hadoop’s Key Characteristics

      Hadoop Cluster and its Architecture

      Hadoop: Different Cluster Modes

      Why Spark is needed

      What is Spark

      How Spark differs from other frameworks

      Spark at Yahoo!

  • BIG Data Analytics and why it’s a Need Now
    • What is BIG data Analytics

      Why BIG Data Analytics is a ‘need’ now

      BIG Data: The Solution

      Implementing BIG Data Analytics – Different Approaches

  • Traditional Analytics vs. BIG Data Analytics
    • The Traditional Approach: Business Requirement Drives Solution Design

      The BIG Data Approach: Information Sources drive Creative Discovery

      Traditional and BIG Data Approaches

      BIG Data Complements Traditional Enterprise Data Warehouse

      Traditional Analytics Platform v/s BIG Data Analytics Platform

  • Big Data Technologies Scala
    • What is Scala

      Scala in other Frameworks

      Introduction to Scala REPL

      Basic Scala Operations

      Variable Types in Scala

      Control Structures in Scala

      Understanding the constructor overloading,

      Various abstract classes

      The hierarchy types in Scala,

      For-each loop, Functions and Procedures

      Collections in Scala- Array

  • Spark
    • Overview to Spark

      Spark installation, Spark configuration,

      Spark Components & its Architecture

      Spark Deployment Modes

      Limitations of Map Reduce in Hadoop

      Working with RDDs in Spark

      Introduction to Spark Shell

      Deploying Spark without Hadoop

      Parallel Processing

      Spark MLLib - Modelling Big Data with Spark

  • Apache Kafka and Flume
    • What is Kafka Why Kafka

      Configuring Kafka Cluster

      Kafka architecture

      Producing and consuming messages

      Operations, Kafka monitoring tool

      Need of Apache Flume

      What is Apache Flume

      Understanding the architecture of Flume

      Basic Flume Architecture

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  • Introduction to Tableau
    • What is Tableau

      Features of Tableau

      Architecture of Tableau

      Installation of Tableau Desktop

      The interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane etc.)

      How to Start with Tableau

      Top Chart Types in Tableau

      Introduction to the various File Type

      Quick Introduction to the User Interface in Tableau

      How to Create Data Visualization Using Tableau feature “Show Me”

      Reorder & Remove Visualization Fields

      How to Sort & Filter Data

      How to Create a Calculated Field

      How to Perform Operations using Cross-Tab

      Working with Workbook Data & Worksheets

      How to Create a Packaged Workbook

  • Data Preparation
    • Connecting to Different Data Sources

      Live vs. Extract Connection

      Data Source Editor

      Managing Metadata and Extracts

      Pivoting & Splitting

      Data Interpreter: Clean dirty data


      Complex Joins


      Data Blending & when it is required

  • Data Visualization
    • Data Visualization Principles

      • What is Data Visualization
      • Data Interpretation
      • Pivot Tables
      • Split Tables
      • Responsive Tool Tips
      • Radial & Lasso Selection
      • Right Click Filtering
      • Creating Calculated Fields
      • Manipulating Text-Left and Right Functions

      Basic Data Visualization

      • Heat Map
      • Highlight Table
      • Bar Charts
      • Line Charts
      • Pie Chart
      • Scatter Plot
      • Word Cloud
      • Tree Map
      • Blended Axis
      • Dual Axis
  • Managing Your Data
    • Filters

      Top & Bottom N Filtering

      Filtering order of operations


      Calculations - String, Basic, Date & Logic


      Working with Dates

      Table Calculation

      Discrete vs Continuous Measures

  • Grouping and Formatting Data
    • Grouping Data

      • Groups, Sets, Hierarchies, Bins, Combined Fields


      • Size, Updating Axis, Colors, Borders, Transparency, Chart Lines, Trend Line, Forecasting, Reference Line, Mark Labels, Annotations
  • Dashboard Design
    • Canvas Selection & Adjusting Sizes

      Tiled Objects

      Floating Objects

      Pixel Perfect Alignment

      Summary Box

      Chart Titles & Captions

      Adding Images & Text

      Adding Background Color

      Adding Shading

      Adding Separator Lines

      Dynamic Chart Titles

      Information Icons

      Creating a Story

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  • Data Science Masters - Live Projects
    • Managing credit card Risks

      Bank Loan default classification

      YouTube Viewers prediction

      Super store Analytics (E-commerce)

      Buying and selling cars prediction (like OLX process)

      Advanced House price prediction

      Analytics on HR decisions

      Survival of the fittest

      Twitter Analysis

      Flight price prediction

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Mock Interviews

Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures. Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
How Croma Campus Mock Interview Works?


Validate your skills and knowledge by working on industry-based projects that includes significant real-time use cases.Gain hands-on expertize in Top IT skills and become industry-ready after completing our project works and assessments.Our projects are perfectly aligned with the modules given in the curriculum and they are picked up based on latest industry standards. Add some meaningful project works in your resume, get noticed by top industries and start earning huge salary lumps right away.
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self assessment

Learn, Grow & Test your skill with Online Assessment Exam to achieve your Certification Goals



The main reason to choose the Data Science Online Training in India from the Croma Campus is so that you can grow your skills under the guidance of the corporate trainers that help you too gain the essential skills and knowledge to meet the demands of the organization with perfect solutions.

The Data Science Online Training in India from Croma Campus will help you to learn from the practical and theoretical formats and will also help you to learn from the real time-based projects that will upgrade your profile needed by the fortune organizations.

The Data Science Online Certification in India can be done with Croma campus offering various ways to learn. You can choose any service from:

  • Instructor Training
  • Online Training
  • Corporate Training
  • Self-paced Training
  • 1 on 1 Training

It takes around 5 to 6 months to learn the course from the Data Science Online Training in India. Also, it depends upon the learner. On an average this time is perfect to learn the course.

To start learning Data Science Online Certification Course in India you can contact to: Email: Info@cromacampus.com Contact no.: +91-9711526942 / +91-120-4155255

Career Assistancecareer assistance
  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired
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Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.



Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.

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Each certification associated with the program is affiliated with the top universities providing edge to gain epitome in the course.

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