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Advance Big Data Science - Python Big Data Course

Learn all that is necessary to be a Data Scientist - Python, Hadoop, Spark with Machine Learning

Do you know that Apache Spark has risen to become the most active open source project in big data? No wonder McKinsey Global Institute estimates shortage of 1.7 million Data Science and Big Data professionals over next 3 years.


Considering this increasing gap in the demand and supply with the help of this Advance Data Science training, IT/ ITES professionals can bag lucrative opportunities and boost their career by gaining sought after Big Data Analytics skills.


This advanced Big Data training, where attendees will gain practical skill set not only on Hadoop in detail, but also learn advanced analytics concepts through Python, Hadoop and Spark. For extensive hands-on practice, candidates will get access to the virtual lab and several assignments and projects. At end of the program candidates are awarded Advance Data Science Certification on successful completion of projects that are provided as part of the training.


A completely industry relevant Big Data Analytics training and a great blend of analytics and technology, making it quite apt for aspirants who want to develop Big Data skills and head-start in Big Data Analytics!


Course duration: 380 hours (At least 138 hours live training + Practice and Self-study, with 8-10 hrs of weekly self-study)

Who Should do this course?

Students coming from IT, Software, Datawarehouse background and wanting to get into the Big Data Analytics domain

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Course Duration 380 hours
Classes 45
Tools Python, Spark,Hadoop, Cloud Computing
Learning Mode Live/Video Based

What will you get



Access to 105 hours instructor led live classes of 35x3 hours each, spread over 18 weekends



Video recordings of the class sessions for self study purpose



Weekly assignment, reference codes and study material in PDF format



Module wise case studies/ projects



Career guidance and career support post the completion of some selected assignments and case studies

Course Outline

  • What is analytics?
  • Analytics vs. Data warehousing, OLAP, MIS Reporting
  • 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
  • Future of analytics and critical requirement
  • Different phases of a typical Analytics projects

  • Overview of Python- Starting with Python
  • Why Python for data science?
  • Anaconda vs. python
  • Introduction to installation of Python
  • Introduction to Python Editors & IDE's(Jupyter,/Ipython)
  • Understand Jupyter notebook & Customize Settings
  • Concept of Packages - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc.)
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels – Date & Time Values
  • Basic Operations - Mathematical - string – date
  • Control flow & conditional statements
  • Debugging & Code profiling
  • Python Built-in Functions (Text, numeric, date, utility functions)
  • User defined functions – Lambda functions
  • Concept of apply functions
  • Python – Objects – OOPs concepts
  • How to create class and modules?
  • How to call classes and modules?
  • Concept of pipelines in Python

  • What is NumPy?
  • Overview of functions & methods in NumPy
  • Data structures in NumPy
  • Creating arrays and initializing
  • Reading arrays from files
  • Special initializing functions
  • Slicing and indexing
  • Reshaping arrays
  • NumPy Maths
  • Combining arrays
  • Basic algebraic operations using NumPy arrays
  • Solving linear equations
  • Matix inversions
  • Calculating Eigen vectors

  • What is pandas, its functions & methods
  • Pandas Data Structures (Series & Data Frames)
  • Creating Data Structures (Data import – reading into pandas)

  • Importing Data from various sources (Csv, txt, excel,etc.)
  • Database Input (Connecting to database)
  • Viewing Data objects - sub setting, methods
  • Exporting Data to various formats
  • Understanding of data
  • Important python modules: Pandas, NumPy

  • Understand the data
  • Sub Setting Data or Filtering Data or Slicing Data
  • Using [] brackets
  • Using indexing or referring with column names/rows
  • Using functions
  • Dropping rows & columns
  • Mutation of table (Adding/deleting columns)
  • Binning data (Binning numerical variables in to categorical variables using cut() and qcut() functions)
  • Renaming columns or rows
  • Sorting (by data/values, index; By one column or multiple columns; Ascending or Descending)
  • Type conversions
  • Setting index
  • Handling duplicates
  • Handling missing values – detect, filter, replace
  • Handling outliers
  • Creating dummies from categorical data (using get_dummies())
  • Applying functions to all the variables in a data frame (broadcasting)
  • Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc.)
  • Important Python modules for data manipulation (Pandas, NumPy, re, math, string, datetime etc.)

  • Exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Uni-variate Analysis (Distribution of data & Graphical Analysis)
  • Bi - Variate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating different Graphs using multiple python packages- Bar/pie/line chart/histogram/stack chart/boxplot/ scatter/ density etc)
  • Important Packages for Visualization (graphical analysis) – Pandas, Matplotlib, Seaborn, Bokeh etc.)

  • Basic Statistics - Measures of Central Tendencies and Variance
  • What is probability distribution?
  • Important distributions (discrete & continuous distributions)
  • Deep dive of normal distributions and properties
  • Concept of sampling & types of sampling
  • Concept of standard error and central limit theorem
  • Inferential Statistics - Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square
  • Important modules for statistical methods: NumPy, SciPy, Pandas

  • What is Big Data & Data engineering?
  • Importance of Data engineering in the Big Data world
  • Role of RDBMS (SQL Server), Hadoop, Spark, NOSQL and Cloud computing in Data engineering
  • What is Big Data Analytics
  • Key terminologies (Data Mart, Data ware house, Data Lake, Data Ocean, ETL, Data Model, Schema, Data pipeline etc)

  • What are Databases & RDBMS
  • Create data model (Schema –Meta Data –ER Diagram) & database
  • Data Integrity Constraints & types of Relationships
  • Working with Tables
  • Introduction to SQL Server & SQL
  • SQL Management Studio & Utilizing the Object Explorer
  • Basic concepts – Queries, Data types & NULL Values, Operators, Comments in SQL, Joins, Indexes, Functions, Views, Sorting, filtering, sub querying, summarising, merging, appending, new variable creation, case when statement usage etc.
  • Data manipulation – Reading & Manipulating a Single and multiple tables
  • Data based objects creation(DDL Commands) (Tables, Indexes, views etc)
  • Optimizing your work
  • End to End to data manipulation exercise

  • Motivation for Hadoop
  • Limitations and Solutions of existing Data Analytics Architecture
  • Comparison of traditional data management systems with Big Data Evaluate key framework requirements for Big Data analytics
  • Hadoop Ecosystem & core components
  • The Hadoop Distributed File System - Concept of data storage
  • Explain different types of cluster setups(Fully distributed/Pseudo etc.)
  • Hadoop Cluster Overview & Architecture
  • A Typical enterprise cluster – Hadoop Cluster Modes
  • HDFS Overview & Data storage in HDFS
  • Get the data into Hadoop from local machine(Data Loading ) - vice versa
  • Practice complete data loading and managing them using command line(Hadoop commands) & HUE
  • Map Reduce Overview (Traditional way Vs. MapReduce way)

  • Integrating Hadoop into an Existing Enterprise
  • Loading Data from an RDBMS into HDFS, Hive, Hbase Using Sqoop
  • Exporting Data to RDBMS from HDFS, Hive, Hbase Using Sqoop

  • Apache Hive - Hive Vs. PIG - Hive Use Cases
  • Discuss the Hive data storage principle
  • Explain the File formats and Records formats supported by the Hive environment
  • Perform operations with data in Hive
  • Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts
  • Hive Script, Hive UDF
  • Join datasets using a variety of techniques, including Map-side joins and Sort-Merge-Bucket joins
  • Use advanced Hive features like windowing, views and ORC files
  • Hive Persistence formats
  • Loading data in Hive - Methods
  • Serialization & Deserialization
  • Integrating external BI tools with Hadoop Hive
  • Use the Hive analytics functions (rank, dense_rank, cume_dist, row_number)
  • Use Hive to compute ngrams on Avro-formatted files

  • Impala & Architecture
  • How Impala executes Queries and its importance

  • Introduction to Data Analysis Tools
  • Apache PIG - MapReduce Vs Pig, Pig Use Cases
  • PIG’s Data Model
  • PIG Streaming
  • Pig Latin Program & Execution
  • Pig Latin : Relational Operators, File Loaders, Group Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF
  • PIG Macros
  • Parameterization in Pig (Parameter Substitution)
  • Use Pig to automate the design and implementation of MapReduce applications
  • Use Pig to apply structure to unstructured Big Data

  • Introduction to Apache Spark
  • Streaming Data Vs. In Memory Data
  • Map Reduce Vs. Spark
  • Modes of Spark
  • Spark Installation Demo
  • Overview of Spark on a cluster
  • Spark Standalone Cluster

  • Invoking Spark Shell
  • Creating the Spark Context
  • Loading a File in Shell
  • Performing Some Basic Operations on Files in Spark Shell
  • Caching Overview
  • Distributed Persistence
  • Spark Streaming Overview

  • Basics of Scala that are required for programming Spark applications
  • Basic constructs of Scala such as variable types, control structures, collections, and more

  • Understanding & Loading data into RDD
  • Hadoop RDD, Filtered RDD, Joined RDD
  • Transformations, Actions and Shared Variables
  • Spark Operations on YARN
  • Sequence File Processing

  • Spark Structured Query Language
  • Linking with Spark SQL
  • Initializing Spark SQL and execute Basic Queries
  • Analyze Hive and Spark SQL Architecture

  • Spark Streaming, its Architecture and abstraction
  • Different Transformations in Spark Streaming such as Stateless and Stateful, Input Sources
  • 24/7 Operations and Streaming UI

  • Introduction to MLib
  • Data Types and working with vectors
  • Examples for usage of Spark MLLib

  • Limitations of RDBMS & Motivation for NoSQL
  • Nosql Design goals & Advantages
  • Types of Nosql databases (Categories) – Cassandra/MongoDB/Hbase
  • CAP theorem
  • How data stored in a NoSQL data storage
  • NoSQL database queries and update languages
  • Indexing and searching in NoSQL Databases
  • Reducing data via reduce function
  • Clustering and scaling of NoSQL Database

  • Overview & Architecture of MongoDB
  • Depth understanding of Database and Collection
  • Documents and Key/Values etc.
  • Introduction to JSON and BSON Documents
  • Installing MongoDB on Linux
  • Usage of various MongoDB Tools available with MongoDB package
  • Introduction to MongoDB shell
  • MongoDB Data types
  • CRUD concepts & operations
  • Query behaviors in MongoDB

  • Data modeling concepts & approach
  • Analogy between RDBMS & MongoDB data modeling
  • Model relationship between documents (one-one, one-many)
  • Model tree structures with parent references and with child references
  • Challenges in modeling
  • Model data for Atomic operations and support search
  • Query building

  • API and drivers for MongoDB, HTTP and REST interface,
  • Install Node.js, dependencies
  • Node.js find & display data, Node.js saving and deleting data

  • Indexing concepts, Index types, Index properties, aggregation

  • MongoDB monitoring, health check, backups & Recovery options, Performance Tuning
  • Data Imports & Exports to & from MongoDB
  • Introduction to Scalability & Availability
  • MongoDB replication, Concepts around sharding, Types of sharding and Managing shards
  • Master – Slave Replication
  • Security concepts & Securing MongoDB

  • Creation of MongoDB app

  • What is Cloud Computing? Why it matters?
  • Traditional IT Infrastructure vs. Cloud Infrastructure
  • Cloud Companies (Microsoft Azure, GCP, AWS ) & their Cloud Services (Compute, storage, networking, apps, cognitive etc.)
  • Use Cases of Cloud computing
  • Overview of Cloud Segments: IaaS, PaaS, SaaS
  • Overview of Cloud Deployment Models
  • Overview of Cloud Security
  • Introduction to AWS, Microsoft Azure Cloud and OpenStack. Similarities and differences between these Public / Private Cloud offerings

  • Creating Virtual machine
  • Overview of available Big Data products & Analytics
  • Services in Cloud
  • Storage services
  • Compute Services
  • Database Services
  • Analytics Services
  • Machine Learning Services
  • Manage Hadoop Ecosystem & Spark, NOSQL in the Cloud Services
  • Creating Data pipelines
  • Scaling Data Analysis & Machine Learning Models

  • Introduction to Predictive Modelling
  • Types of Business problems - Mapping of Techniques
  • Relevance in industry and need of the hour
  • Difference between jargons i.e. data science, data analysis, data analytics, data mining
  • What is Machine Learning?
  • What is the goal of Machine Learning?
  • Applications of ML (Marketing, Risk, Operations, etc.)
  • Key components of ML
  • Overall process of executing the ML project (Data Pre-processing, Sampling, Model Building, Validation)
  • Common mistakes done in ML project and how to overcome
  • Different terms to know for ML

  • Major Classes of Learning Algorithms -Supervised vs.
  • Unsupervised Learning vs. Semi supervised vs. Reinforcement Learning
  • Important Consideration like fitment of techniques
  • Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
  • Concept of optimization - Gradient descent algorithm
  • Concept of feature engineering
  • Regularization (LASSO, LARS, Elastic net and Ridge regression)
  • Types of Cross validation (Train & Test, K-Fold validation etc.)
  • Cost & optimization functions

  • Introduction - Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Important steps in Model building
  • Need of Data preparation
  • Data Audit Report and Its importance
  • Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
  • Variable Reduction Techniques - Factor & PCA Analysis
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc.)
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
  • Interpretation of Results - Business Validation -
  • Implementation on new data

  • Introduction - Applications
  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model
  • Important steps in model building
  • Understanding standard model metrics (Concordance, Variable significance, Gini, KS, Misclassification, etc.)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers, etc.)
  • Interpretation of Results - Business Validation
  • Implementation on new data and Tracking the model

  • Overview of Decision Trees
  • Types of decision trees (Regression Trees, Classification trees, Oblique Decision Trees)
  • Types of decision tree algorithms (CART vs. CHAID vs. C50 etc.)
  • Concept of objective segmentation
  • How to use decision trees to solve regression, classification & segmentation problems)
  • Rule Based Knowledge: Logic of Rules, Evaluating Rules, Rule Induction and Association Rules
  • Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node;
  • Splitting criteria: Entropy; Information Gain, Gini Index, Chi Square; ANOVA)
  • Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
  • Pruning decision tree
  • Cost as a consideration
  • Fine Tuning model using tuning parameters
  • Model validation
  • Over fitting - Best Practices to avoid
  • Implementation of Solution

  • What is concept of Ensemble learning (Stacking, Mixture of Experts)?
  • Types of ensemble models (homogeneous, heterogeneous)
  • Logic, Practical Applications
  • Ensemble learning techniques
  • Bagging
  • Random Forest
  • Boosting
  • AdaBoost
  • Gradient Boost
  • XGBoost
  • Fine tuning the model using tuning parameters

  • What is concept of Instance based learning?
  • What is KNN?
  • KNN method for regression & classification
  • KNN method for missing imputation
  • Computation of Distance Matrix
  • The Optimum K value
  • Model Building, validation & Evaluation of Model
  • Advantages & Disadvantages of KNN Models
  • Applications of KNN in collaborative filtering, digit recognition
  • KNN in collaborative filtering, digit recognition

  • Fundamentals of Probability; Conditional and Marginal Probability; Bayes Theorem and Its Applications
  • Probabilities - The Prior and Posterior Probabilities
  • Bayesian Belief nets, MAP, Naïve Rule and Naïve Bayes
  • Naïve Bayes for classification - Data Processing - Discretization of Features
  • Applications of Naïve Bayes in Text Mining, Spam Engines and Classifications
  • Model Building, Validation and Evaluation of model
  • Pros/Cons of Naïve Bayes Models

  • What is Support vector machines?
  • Understanding SVM
  • Concepts of Linearly separable vs. non separable data
  • Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
  • Train/Test/Tune the Model using SVM
  • Applications and Interpretation

  • Introduction to Segmentation
  • Types of Segmentation (Subjective Vs. Objective, Heuristic Vs. Statistical)
  • Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
  • Concept of Distance and related math background
  • Segmentation Techniques
  • K-Means/K-Medians Clustering
  • Density Based clustering (DBSCAN)
  • Identifying number of segments (Pseudo F-value, Silhouette score, elbow method etc.)
  • Cluster evaluation and profiling
  • Identifying the characteristics of segmentation
  • Interpretation of results - Implementation on new data
  • Overview of other unsupervised learning techniques (Factor analysis, Hidden Markov models, Gaussian mixture models etc.)

  • Forecasting overview
    • What is forecasting?
    • Applications of forecasting
  • Basics of Time Series
    • Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
    • Types of Seasonality (Hourly, daily, weekly, monthly, quarterly etc.)
    • Classification of Techniques (Pattern based - Pattern less)
    • Important terminology: lag, lead, Stationary, stationary tests, auto correlation & white noise, ACF & PACF plots, auto regression, differencing
      Classification of Time Series Techniques (Univariate & Multivariate)
  • Stationary Time Series Methods
    • Moving Averages
    • Weighted moving averages
    • Exponential Smoothening
    • Comparison between MA & ES
  • Trend Based Time Series
    • Linear Regression
    • Double exponential smoothening (Holt’s Method)
    • Comparison between Regression & DS
  • Seasonal Time Series
    • Decomposition - CMA Method
  • Advanced Techniques
    • Box Jenkins Methodology
    • AR, MA, ARMA Models
    • ARIMA/SARIMA
    • ARIMAX, SARIMAX
  • Evaluation of Forecasting
    • Understanding Forecasting Accuracy
    • Goodness Metrics: MSE, MAPE, RMSE, MAD

  • Motivation for Neural Networks and Its Applications
  • Understand Neural Networks
  • Structure of Networks
  • Perceptron and Single Layer Neural Network, and Hand Calculations
  • Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
  • The ANN Model
  • Types of Activate functions
  • Train/Test/Tune the ANN Model

  • Unstructured vs. Semi-structured Data
  • Text Mining - characteristics, trends
  • Domain presentation - discussion of various areas and their applications
  • Programming languages designated for working on Text Mining analysis
  • Data Scientist - a profession comprising mainly of working with Text Mining
  • Social Media – Characteristics of Social Media
  • Applications of Social Media Analytics
  • Examples & Actionable Insights using Social Media Analytics

  • Text processing using string functions & methods
  • Understanding regular expressions
  • Identifying patterns in the text using regular expressions  

  • Getting Started with NLTK
  • Introduction to NLP & NLTK
  • Introduction to NLTK Modules (corpus, tokenize, Stem, collocations, tag, classify, cluster, tbl, chunk, Parse, ccg, sem, inference, metrics, app, chat, toolbox etc)

  • Fundamentals of information retrieval
  • Reading data from file folder/from text file, from the Internet & Web scrapping, Data Parsing
  • Cleaning and normalization of data
    • Sentence Tokenize and Word Tokenize, Removing insignificant words,(“stop words”), Removing special symbols, removing bullet points and digits, changing letters to lowercase, stemming /lemmatisation /chunking
  • Creating Term-Document matrix
  • Finding associations
  • Removing rare terms (Sparse terms)
  • Measurement of similarity between documents and terms
  • Visualization of term significance in the form of word clouds
  • Tagging text with parts of speech
  • Word Sense Disambiguation

  • Sentiment analysis
  • vocabulary approach, based on Bayesian probability methods
  • Name entity recognition (NER)
  • Methods of data visualization
  • word length counts plot
  • word frequency plots
  • word clouds
  • correlation plots
  • letter frequency plot
  • Heat map
  • Grouping texts using different methods

  • Data-centric methods
  • K-means
  • Classification Models (spam detection, topic modelling)
  • K Nearest Neighbours
  • SVM (Linear Support Vector Classifier)
  • Naive Bayes
  • Decision tree
  • Semantic similarity between texts
  • Language Models and n-grams -- Statistical Models of Unseen Data (Smoothing)

Case Studies

Objective: The objective of the case study is to provide end to end steps to build and validate regression model to identify the key drivers of customer spend using Python-Spark. Problem Statement: One of the leading banks would like to identify key drivers for customer spending so that they can define strategy to optimize the product features.

Objective: The objective of the case study is to provide end to end steps to build and validate classification model using python-spark Problem Statement: One of the leading banks would like to predict bad customers (Defaulters) based on the customer data provided by them in their application

Objective: The objective of the case study to apply advanced algorithms like factor and cluster analysis for data reduction and customer segmentation based on the customer behavioural data Problem Statement: Build an enriched customer segmentation and profile them using different KPIs for one of the leading telecom company to define marketing strategy

Objective: The objective of the case study to given hands-on experience on how to apply/use different time series forecasting techniques (Averages/Smoothening, decomposition, ARIMA etc) Problem Statement: One of the leading travel companies would like predict number of air passengers travelling to Europe so that they can define their marketing strategy accordingly

FAQS

Don’t worry. You will always get a recording for the class in your inbox. Have a look at that and reach out to the faculty in case of doubts. All our live classes are recorded for self-study purpose and future reference, and these can also be accessed through our Learning Management System. Hence, in case you miss a class, you can refer to the video recording and then reach out to the faculty during their doubts clearing time or ask your question in the beginning of the subsequent class.

You can also repeat any class you want in the next one year after your course completion.

1 year post your course commencement. If needed, you can also repeat any number of classes you want in the next one year after course completion. Batch change policies will however, apply in this case.

In case required because any genuine reasons, the recordings access can be extended further for upto 1 year post the completion of one year validity. Please note that given the constant changes in the Analytics industry, our courses continue to be upgraded and hence old courses might no longer hold relevance. Hence, we do not promise lifetime access just for marketing purposes. 

No. Our recordings can be accessed through your account on LMS or stream them live online at any point of time though.

Recordings are integral part of AnalytixLabs intellectual property by Suo Jure. The downloading/distribution of these recordings in anyway is strictly prohibited and illegal as they are protected under copyright act. Incase a student is found doing the same, it will lead to an immediate and permanent suspension in the services, access to all the learning resources will be blocked, course fee will be forfeited and the institute will have all the rights to take strict legal action against the individual.

The sharing of LMS login credentials is unauthorized, and as a security measure, if the LMS is accessed by multiple places, it will flag in the system and your access to LMS can be terminated.

Yes. All our course are certified. As part of the course, students get weekly assignments and module-wise case studies. Once all your submissions are received and evaluated, the certificate shall be awarded.

We follow a comprehensive and a self-sustaining system to help our students with placements. This is a win-win situation for our candidates and corporate clients. As a pre-requisite for learning validation, candidates are required to submit the case studies and project work provided as a part of the course (flexible deadline). Support from our side is continuous and encompasses help in profile building, CV referrals (as and when applicable)  through our ex-students, HR consultants and companies directly reaching out to us.

We will provide guidance to you in terms of what are the right profiles for you based on your education and experience, interview preparation and conducting mock interviews, if required. The placement process for us doesn’t end at a definite time post your course completion, but is a long relationship that we will like to build.

No institute can guarantee placements, unless they are doing so as a marketing gimmick! It is on a best effort basis.

In professional environment, it is not feasible for any institute to do so, except for a marketing gimmick. For us, it is on a best effort basis but not time – bound – in some cases students reach out to us even after 3 years for career support.

Yes we have classroom option for Delhi-NCR candidates. However, most of our students end up doing instructor led live online classes, including those who join classroom in the beginning. Based on the student feedback, the learning experience is same both in classroom and instructor led live online fully interactive mode.

We provide both the options and for instructor led live online classes we use the gold standard platform used by the top universities across the globe. These video sessions are fully interactive and students can chat or even ask their questions verbally over the VoIP in real time to get their doubts cleared.

To attend the online classes, all you need is a laptop/PC with a basic internet connection. Students have often shared good feedback of attending these live classes through their data card or even their mobile 3G connection, though we recommend a basic broadband connection.

For best user experience, a mic-headphone is recommended to enhance the voice quality, though the laptop’s in-built mic works fine and you can ask your question over the chat as well.

Through the LMS, students can always connect with the trainer or even schedule one-to-one time over the phone or online. During the course we also schedule periodic doubts-clearing classes though students can also ask doubts of a class in the subsequent class.

LMS also has a discussion forum where a lot of your doubts might get easily answered.

Incase you are having a problem still, repeat the class and schedule one-to-one time with the trainer.

  • Instructor Led Live online or Classroom - Within 7 days of registartion date and latest 3 days before batch start
  • Video-based - 2 days

Yes. While making the fee payment, most of the courses have the installment option.

For all the courses, we also provide the recordings of each class for their self-reference as well as revision in case you miss any concept in the class. In case you still have doubts after revising through the recordings, you can also take one-to-one time with the faculty outside classes during. Furthermore, if students want to break their courses in different modules, they get one year time to repeat any of the classes with other batches.

It is recommended to have 64-bit operating system with minimum 8GB RAM so that the virtual lab can be installed easily

One of the best training institute for data science courses in Delhi NCR. Highly recommend for all students who are trying to make a career in Data Analytics


Meenakshi Shukla
(Student)

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