Additional information
| Select Batch | Weekend Batch : 24th April, 08:30 AM To 12 PM ( 25 Seats Left), Weekday Batch : 12th May, 8 PM To 10:00 PM ( 20 Seats Left) |
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FEATURES
Course Features
Course Prerequisite
There is no Prerequisite for this course as we cover programming and statistics from basics. We provide special classes & support for professionals from non-programming / non-technical backgrounds.
Special Support to Non Programmers
we provide special Python programming fundamentals classes designed for non-programmers and non-technical domain professionals which helps you to learn programming concepts easily.
Program Eligibility
Work Experience :
- Professionals working as project managers and team leads with 8 to 15 years of experience in any domain (technical or non technical) Work.
To know which Program is best suitable for your profile, Apply for Career counselling session with our expert.
Who Should Apply
- Team managers, Project managers, Technical lead, professionals having 8 to 15 years of work exp. looking to start their career in data science & analytics manager.
- Have a good understanding of any programming language(Working knowledge)
Good understanding in mathematics.
To know which Program is best suitable for your profile, Apply for a Career counseling session with our expert.
About Instructors
- Our instructors are working professionals who graduated from premier institutes like BITS Pilani, IIT Roorkee, and working in companies as Data Scientist/Machine Learning Engineer and Artificial Intelligence expert.
- Students will be guided by our mentors until the end of a course. A student can seek help by our mentors even after course completion for knowledge and placement purposes.
Fees and Duration
Weekday Program Batches: 11 Months
Monday – Friday – 2 hours every day
Weekend Batches: 13 Months
Saturday & Sunday – 4 hours every day
Program Fee: Rs. 95,000/-
INTEREST-FREE INSTANT LOAN WITHOUT CREDIT CARD
NO COST EMI ON MAJOR CREDIT CARDS
Hybrid Model Classes
Attain 2 Capstone & 15+ Real Time AI and ML Projects in Classroom-mode OR Live-online led by industrial experts.
Locations: Pune, Mumbai, Delhi, Hyderabad, Chennai, Bengaluru
It is completely optional to attain project sessions in classroom. You can also prefer Live-Online Interactive Mode guided by Industrial Expert.
Introduction to Programming 3hr
What is a programming language ? Source code Vs bytecode Vs machine code
Compiler Vs Interpreter ,C/C++, Java Vs Python
Jupyter notebook basics 1hr
Different type of code editors in python introduction to Anaconda and
jupyter notebookFlavours of python.
Python Programming Basics 2hr
Variable Vs identifiers Vs strings, Operators Vs operand Procedure
oriented Vs modular programming
Statistics basics 2hr
Introduction to statisticsMean,median, mode, Standard deviation,
AverageIntroduction to probability,permutations and
combinationsIntroduction to linear Algebra
Git and GitHub 2hr
Learn the key concepts of the Git source control system,Configure SSH for authentication,Create and use a remote repository on GitHub,Git Overview
Tools Covered
Programming Basics &Environment Setup hr
Installing Anaconda, Anaconda, Basics, and Introduction, Get familiar with version control, Git, and GitHub.Intro to Jupyter Notebook environment. Basics Jupyter notebook Commands.Programming language basics.
Python Programming Overview hr
Python 2.7 vs Python 3,Writing your First Python Program
Lines and Indentation,Python Identifiers,Various Operators and Operators
Precedence ,Getting input from User,Comments,Multi line Comments.
Strings, Decisions And Loop
Control hr
Working With Numbers, Booleans and Strings,String types and
formatting, String operations Simple if Statement, if-else Statement
if-elif Statement. Introduction to while Loops. Introduction to for Loops,Using
continue and break.
Python Data Types hr
List,Tuples,Dictionaries,Python Lists,Tuples,Dictionaries,Accessing Values,Basic Operations,Indexing, Slicing, and Matrixes,Built-in Functions & Methods,Exercises on List,Tuples And Dictionary
Functions And Modules hr
Introduction To Functions – Why Defining Functions. Calling Functions
Functions With Multiple Arguments.Anonymous Functions – Lambda
Using Built-In Modules, User-Defined, Modules, Module Namespaces,Iterators And Generators
File I/O And Exceptional Handling and Regular Expression hr
Opening and Closing Files,open Function,file Object Attributes
close() Method ,Read,write,seek.Exception Handling, try-finally Clause
Raising Exceptions, User-Defined Exceptions. Regular Expression- Search
and Replace. Regular Expression Modifiers. Regular Expression Patterns,re module.
Data Analysis Using Numpy And
Pandas hr
Introduction to Numpy. Array Creation, Printing Arrays, Indexing, Slicing and Iterating, Shape Manipulation -Changing the shape, stacking, and splitting of the array. Vector stacking, Broadcasting.
Pandas : Introduction to Pandas,Importing data into Python
Pandas Data Frames, Indexing Data Frames, Basic Operations With Data frame,
Renaming Columns, Subletting, and filtering a data frame.
Data Visualisation using Python:
Matplotlib and Seaborn hr
Matplotlib: Introduction,plot(),Controlling Line Properties,Subplot with Functional Method, MUltiple Plot, Working with Multiple Figures,Histograms
Seaborn :Intro to Seaborn And Visualizing, statistical relationships , Import and Prepare data.Plotting with categorical data and Visualizing linear relationships Seaborn Exercise
Tools Covered
Fundamentals of Math and
Probability hr
Basic understanding of linear algebra,Matrics, vectors. Addition and
Multimplication of matrics.Fundamentals of Probability
Probability distributed function and cumulative distributed function.
Descriptive Statistics hr
Describe or sumarise a set of data Measure of central tendency and
measure of dispersion.The mean,median,mode, curtosis and skewness. Computing Standard deviation and Variance.Types of distribution.
Inferential Statistics hr
What is inferential statistics,Different types of Sampling techniques
Central Limit Theorem,Point estimate and Interval estimate
Creating confidence interval for,population parameter
Characteristics of Z-distribution and T-,Distribution. Basics of Hypothesis
Testing. Type of test and rejection,region. Type of errors in Hypothesis yesting,
Hypothesis Testing hr
Hypothesis Testing Basics of Hypothesis Testing ,Type of test and Rejection Region,Type o errors-Type 1 Errors,Type 2,Errors. P value method,Z score
Method. The Chi-Square Test of, Independence.Regression. Factorial Analysis of
Variance. Pearson Correlation, Coefficients in Depth. Statistical,Significance, Effect Size
Data Processing & Exploratory
Data Analysis hr
What is Data Wrangling, Data Pre-processing and cleaning?
How to Restructure the data? What is Data Integration and Transformation
Introduction To Machine Learning 8hr
What is Machine Learning? What is the Challenge?
Introduction to Supervised Learning, Introduction to Unsupervised
Learning, What is Reinforcement Learning? Machine Learning applications
Difference between Machine Learning and Deep Learning
Supervised Learning 8hr
Support Vector Machines,Linear regression,Logistic regression
Naive Bayes, Linear discriminant analysis, Decision tree
the k-nearest neighbor algorithm, Neural Networks (Multilayer perception) Similarity learning
Linear Regression 8hr
Introduction to Linear Regression,Linear Regression with Multiple
Variables, Disadvantage of Linear Models, Interpretation of Model Outputs
Understanding Covariance and Colinearity, Understanding Heteroscedasticity
Case Study – Application of Linear Regression for Housing
Price Prediction
Logistic Regression hr
Introduction to Logistic Regression.–Why Logistic Regression .
Introduce the notion of classification, The cost function for logistic regression
Application of logistic regression to multi-class classification.
Confusion Matrix, Odd’s Ratio, And ROC Curve
Advantages And Disadvantages of Logistic Regression.
Decision Trees hr
Decision Tree – data set How to build decision tree? Understanding Kart Model
Classification Rules- Overfitting Problem. Stopping Criteria And Pruning. How to Find the final size of Trees? Model A decision Tree.
Naive Bayes. Random Forests and Support Vector Machines.Interpretation of Model Outputs
Unsupervised Learning hr
Hierarchical Clustering k-Means algorithm for clustering –groupings of unlabeled data points.Principal Component Analysis(PCA)-Data. Independent components analysis(ICA) Anomaly Detection Recommender System-collaborative
filtering algorithm
Case Study– Recommendation
Engine for e-commerce/retail chain
Natural language Processing hr
Introduction to natural Language Processing(NLP).
Word Frequency Algorithms for NLP Sentiment Analysis
Case Study :Twitter data analysis using NLP
Introduction to Time Series Forecasting hr
Basics of Time Series Analysis and Forecasting ,Method Selection in Forecasting,Moving Average (MA) Forecast Example,Different Components of
Time Series Data, Log Based Differencing, Linear Regression For Detrending
ARIMA and Multivariate Time
Series Analysis hr
Introduction to ARIMA Models,ARIMA Model Calculations,Manual ARIMA
Parameter Selection,ARIMA with Explanatory Variables Understanding Multivariate Time Series and Their Structure,Checking for Stationarity and Differencing the MTS
Case Study : Performing Time Series
Analysis on Stock Prices
Introduction to Deep Learning And Tensor Flow hr
Neural Network Understaing Neural Network Model Installing TensorFlow Simple Computation ,Contants And Variables. Types of file formats in TensorFlow. Creatting A Graph –Graph Visualization. Creating a Model– Logistic Regression Model Building using tensor flow TensorFlow Classification Examples
Introduction to Tensor Flow hr
Installing TensorFlow Simple Computation,Contents And Variables Types of file formats in TensorFlow Creatting A Graph – Graph Visualization Creating a Model – Logistic Regression Model Building TensorFlow Classification Examples
Understanding Neural Networks With Tensor Flow hr
Basic Neural Network, Single Hidden Layer Model,Multiple Hidden Layer Model,Backpropagation – Learning,Algorithm and visual representation,
Understand Backpropagation – Using Neural Network Example TensorBoard
Project on backpropagation
Convolutional Neural Network(CNN) hr
Convolutional Layer Motivation ,Convolutional Layer Application,
The architecture of a CNN, Pooling Layer Application Deep CNN, Understanding and Visualizing a CNN,Project: Building a CNN for Image Classification
Introduction to NLP & Text Analytics hr
Introduction to Text Analytics, Introduction to NLP
What is Natural Language Processing?, What Can Developers Use NLP Algorithms For?
NLP Libraries. Need of Textual Analytics Applications of Natural Language
Procession ,Word Frequency Algorithms for NLP Sentiment Analysis
Text Pre Processing Techniques hr
Need of Pre-Processing Various methods to Process the Text
data Tokenization ,Challenges in Tokenization,Stopping ,Stop Word Removal
Stemming – Errors in Stemming,Types of Stemming Algorithms -Table
lookup Approach ,N-Gram Stemmers
Distance Algorithms used in Text Analytics hr
string Similarity,Cosine Similarity Mechanishm -Similarity between Two text
documents. Levenshtein distance -measuring the difference between two sequences. Applications of Levenshtein distance LCS (Longest Common Sequence ) Problems and solutions ,LCS Algorithms
Information Retrieval Systems hr
Information Retrieval -Precision,Recall,F- score,TF-IDF,KNN for document retrieval,K-Means for document retrieval,Clustering for document retrieval
Topic Modelling & Dirchlett Distributions hr
Introduction to Topic Modelling,Latent Dirchlett Allocation,Adavanced Text Analytics & NLP,Introduction to Natural Language,Toolkit,POS Tagging NER
Projects And Case Studies hr
a. Sentiment analysis for twitter, web
articles
b. Movie Review Prediction
c. Summarization of Restaurant
Reviews
RDBMS And SQL Operations hr
Introduction To RDBMS. Single Table,Queries – SELECT,WHERE,ORDER,BY,Distinct,And ,OR. Multiple Table
Queries: INNER, SELF, CROSS, and OUTER, Join, Left Join, Right Join, Full
Join, Union,Advance SQL Operations:Data Aggregations and summarizing the data
Ranking Functions: Top-N Analysis, Advanced SQL Queries for Analytics
NoSQL Databases hr
Topics – What is HBase? HBase Architecture, HBase,Components,Storage Model of HBase,HBase vs RDBMS,Introduction to Mongo DB, CRUD,Advantages of MongoDB over
RDBMS,Use cases
Programming with SQL hr
Mathematical Functions,Variables,Conditional Logic Loops,Custom Functions
Grouping and Ordering, Partitioning, Filtering Data, Subqueries
MongoDB Overview hr
Where MongoDB is used? MongoDB Structures,MongoDB Shell vs MongoDB Server
Data Formats in MongoDB,MongoDB Aggregation Framework,Aggregating Documents
What are MongoDB Drivers?
Basics and CRUD Operation hr
Databases, Collection & Documents,Shell & MongoDB drivers
What is JSON Data Create, Read, Update, Delete Finding, Deleting, Updating,
Inserting Elements Working with Arrays Understanding Schemas and Relations
Introduction to MongoDB hr
What is MongoDB?Characteristics and Features,MongoDB Ecosystem,Installation process Connecting to MongoDB database,Introduction to NoSQL
Introduction of MongoDB module,What are ObjectIds in MongoDb
Introduction To R hr
Installation Setup Quick guide to RStudio User Interface
RStudio’s GUI3 Changing the appearance in RStudio
Installing packages in R and using the library Development Environment Overview
Introduction to R basics Building blocks of R Core programming principles
Fundamentals of R
Programming with R hr
Creating an object,Data types in R,Coercion rules in R
Functions and arguments, Matrices, Data Frame, Data Inputs and Outputs with R, Vectors and Vector operation Advanced Visualization, Using the script vs. using the console
Manipulating Data hr
Data transformation with R – the Dplyr package – Part
Data transformation with R – the Dplyr package – Part
Sampling data with the Dplyr package,Using the pipe operator in R
Tidying data in R – gather() and separate(),Tidying data in R – unite() and
spread()
Visualizing Data hr
Intro to data visualization,Introduction to ggplot2
Building a histogram with ggplot2, Building a bar chart with ggplot2
Building a box and whiskers plot with ggplot2, Building a scatterplot with ggplot2
Introduction to Tableau hr
Connecting to data source Creating dashboard pages
How to create calculated columns,Different charts
Hands-on :Hands on on connecting data
source and data cleansing
Hands on various charts
Visual Analytics hr
Getting Started With Visual Analytics,Sorting and grouping Working with sets, set action,Filters: Ways to filter, Interactive Filters,Forecasting and Clustering
Hands-on: Hands on the deployment of Predictive
model in visualization
Dashboard and Stories hr
Working in Views with Dashboards and Stories,Working with Sheets
Fitting Sheets, Legends and Quick Filters, Tiled and Floating Layout Floating Objects
Mapping hr
Coordinate points,Plotting Latitude and Longitude
Custom Geocoding,Polygon Maps,WMS and Background Image
Getting Started With Power BI hr
Installing Power BI Desktop and Connecting to Data,Overview of the Workflow in Power BI Desktop,Introducing the Different Views of the Data Mode Query Editor Interface Working on Data Model
Programming with Power BI hr
Working with Timeseries,Understanding aggregation and granularity
Filters and Slicers in Power BI Maps, Scatterplots and BI Reports
Connecting Dataset with Power BI Creating a Customer Segmentation
Dashboard Analyzing the Customer Segmentation Dashboard
Introduction To Hadoop hr
Distributed Architecture – A Brief Overview Understanding Big Data,
Introduction To Hadoop ,Hadoop,Architecture,HDFS ,Overview of MapReduce
Framework,Hadoop Master – Slave Architecture,MapReduce Architecture
Use cases of MapReduce
Apache Spark Analytics hr
What is Spark Introduction to Spark RDD,Introduction to Spark SQL and Dataframes,Using R-Spark for machine learning
Hands-on:installation and configuration of Spark
Using R-Spark for machine learning
programming
Apache Spark Analytics hr
Getting to know PySpark,Pyspark Introduction,Pyspark Environment Setup
pySpark – Spark context,RDD , Broadcast and Accumulator,Sparkconf and Sparkfiles,Spark MLlib Overview,Algorithms and utilities in Spark
Mlib
Case Study hr
Hands-on:
Map-reduce Use Case 1: Youtube
data analysis
Map-reduce Use Case 2: Uber Data
Analytics
Hands-on: Spark RDD programming
Hands-on: Spark SQL and Dataframe programming
Introduction To GCP Cloud ML Engine hr
Introduction to Google CloudML Engine,CloudML Engine in Machine,Learning WorkFlow,+3Components of Cloud ML Engine -Google Cloud Platform Console.
gcloud command-line tool and Rest API
API
Training Machine Learning
Model hr
Developing a training application Packaging a training application
Running and monitoring a training job Using hyperparameter tuning Using GPUs for training models in the cloud
Deploying Machine Learning Model hr
Deploying Models ,Understanding training graphs and serving graphs,
Check and adjust model size Build an optimal prediction graph Creating input function creating a model version Getting Online Prediction
Building AI-ML and Analytics
capabilities in organisation/Projects 6 hr
Use data science and AI to create and implement business strategies.
Incorporate AI on top of existing products and services. Understand the
requirements of clients and projects using data driven-methods and perspective.
Transform business problems to data analytics problems. Improve organization
processes using analytics.
Building a data science and AI Team
6hr
Defining the data science team, Various job roles in data science – Data analyst, data engineer, data scientist, ML engineer, data science manager. Understand the qualification and expertise of these job roles. Interviewing process for data science job positions. On-boarding the Data Science Team, working with other teams and stakeholders. Challenges and difficulties.
Case Study 1: Strategy for implementing ML in web-based retail product (existing project)
Case study 2: Convert a business problem to a data problem from scratch
(BFSI domain)
Capstone Project-3 : Deliver a machine learning and AI Project from scratch starting from strategy, planning, team building, modeling and deployment. You will be assigned 4 machine learning engineers and you need to deliver this project under mentorship of our expert in 3 months time.
Understanding the data science workflow and Pipeline6 hr
Organize a data science project workflow from scratch, workflow of a
data science project, Identifying various data sources, Cleaning your data, EDA, creating ML model, Model deployment, Monitoring. Building data
pipelines, Type of data, evolution of pipelines.
Managing data science Projects6 hr
Determine Business Objectives,Determine Data Mining Goal, Understand
data, Verify Quality of data,Getting familiar with CRISP-DM methods and
process, Business understanding, Data understanding, data preparation,modeling, evaluation, Deployment.
Case study : Solve a retail business problem and follow CRISP-DM process and methods.
Architecture of AI & ML Systems6 hr
Strategy for building artificial intelligence systems for business.
Understand AI infrastructure requirements, the Importance of machine
learning system architectures, and their various components. Build a machine learning system architectures for a chat-bot, recommended systems, etc.
Case Study/Practice: Live interview of 2 candidates for data analyst and
ML engineer Job Roles.
Banking & Finance analytics Domain6 hr
Retail Sector4 hr
Healthcare domain4 hr
E-Commerce Domain
4 hr
Supply Chain Management
4 hr
Manufacturing Domain
3 hr
Automotive Domain
3 hr
Project : Loan Default Prediction
The bank wants to improve its services by finding interesting
groups of clients. Fortunately, the bank stores data about their clients, the accounts(transactions within several months), the loans already granted, the credit cards issued. This process of loan default prediction can be done with machine learning algorithms.
Project : Analyzing Health Data and
tracking human activity
The goal is to breakdown all the data that the Samsung Health app has collected and see what useful insights we can gain by analyzing it.
Project : IBM HR Analytics
Applying analytic processes to the human resource department of an organization in the hope of improving employee performance and therefore getting a better return on investment. This is especially concerning if your business is customer-facing, as customers often prefer to interact with familiar people.
Project : Clustering Customers
Big Bazaar has retail outlets across major metropolitan cities in India.
With the help of machine learning algorithms, we can better understand
customer behavior and understand their buying needs better.BigBazaar runs various loyalty programs, festive offers which provide their customer more
opportunities to avail discounts.
Project : Identify fraudulent credit card transactions
To recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. It involves various
processes like Data Cleaning, Data Visualization, Insights generation, Model generation, Feature Engineering, and so on.
Project : Consumer Reviews of
Amazon Products
The goal is to analyze Amazon’s most successful consumer electronics product launches; discover insights into consumer reviews and assist with machine learning models. What are the most reviewed Amazon products?
How do the reviews in the first 90 days after a product launch?
Project :Airbnb New User Bookings
The goal is to predict which country a new user’s first booking destination will be. By accurately predicting where a new user will book their first travel experience, Airbnb can share more personalized content with their community, decrease the average time to first booking, and better forecast demand.
Project :Netflix Movies and TV Shows
Explore what all other insights can be obtained from the list of tv
shows and movies available on Netflix as of 2019. Understanding what content is available in different countries. Identifying similar content by matching text-
based features. Network analysis of Actors / Directors and find interesting insights.
Project :Sales Forecasting
This dataset contains the sales for each department from the Walmart dataset containing data of 45 Walmart stores, selected holiday markdown events are
also included. These markdowns are known to affect sales, but it is challenging to predict which departments are affected and the extent of the impact.
Project :-BMW Pricing Challenge
To find a good statistical model to describe the value of a used
car depending on the basic description How does the estimated value
of a car change over time? Can you detect any patterns? How big is the influence of the factors not represented in the data on the price?
Project :-Bosch Production Line Performance
To predict internal failures using thousands of measurements and
tests made for each component along the assembly line. This would enable Bosch to bring quality products at lower costs to the end-user.The goal is to predict which parts will fail quality control
Project :-Trending YouTube Video Statistics
The dataset of this project are a daily record of the top trending YouTube
videos, to generate insights like Sentiment analysis in a variety of forms
Categorizing YouTube videos based on their comments and statistics
Training ML algorithms like RNNs to generate their own YouTube comments.
Project :-Generating Chatbot
In this project we will build a simple retrieval based chatbot based on NLTK
library in python, to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.
Project :-Identify And Predict Customer churn in telecom industry
The goal is to develop a churn prediction model that assists telecom operators to predict customers who are most likely subject to churn. Also to understand the customer behavior and reasons for churn. Apply multiple
classification models to predict customer churn in the telecom industry.
Project :-Smart Supply Chain for Big Data Analysis
A DataSet of Supply Chains used by the company DataCo Global is
used for the analysis. Dataset of Supply Chain, which allows the
use of Machine Learning Algorithms and R Software.It also allows the correlation of Structured Data with Unstructured Data for knowledge generation.
300+
Content Hours Available
15+
Industry Projects
1:1
doubt-clearing session
2+
Capstone Projects
14
Programming Tools/ Languages
MODULES
Course Modules
Python for Data Science
Statistics Mathematics
Machine Learning
Deep Learning
NLP / Text Analytics
Computer Vision
Project Management
R Programming
Domain Training
Google Cloud
mySQL
Mongo DB
Power BI
Tableau
Apache Spark
Reinforcement Learning
Real Time Projects
AI/ML Project Management
Program Schedule
For Managers and Leaders
Module 0: Special Sessions For Non-Programmers
Time duration: 4 days (8 hours)
One Week before your Batch Starts
We believe that professionals coming from non-programming backgrounds need special assistance and support. we provide special programming fundamentals classes designed for non programmers and non technical domain professionals which helps you to learn programming concepts easily.
Term 1: Python For Data Science
Time duration: 5 Weeks (40 hours)
1.5 Months
Python is one of the most commonly used programming languages in the field, in this section, we start Basic python & Environment Setup and advanced Python scripts such as List, Tuples, Dictionaries, File Operations, Regular Expressions, dealing with binary data, and using the extensive Python module features. Python libraries and tools,Numpy ,Pandas,Matplotlib and Seaborn etc
Term 2: Statistics And Machine Learning,Capstone Project 1 – ML
Time duration: 9 Weeks (72 hours)
2 Months
Statistics are the use of mathematics to conduct technical predictive analytics, IN statistics covering math and probability fundamentals, hypothesis testing, data processing, and exploratory data analysis, etc. Machine learning curriculum that will provide you to learn techniques such as supervised learning, unsupervised learning, and natural language processing, etc.
Term 3: TensorFlow, Deep Learning, NLP & Text Analytics, Capstone Project 2 -NLP
Time duration: 6 Weeks ( 48 hours)
2 Months
TensorFlow is a platform built by Google. Deep Learning is a machine learning category. TensorFlow and deep learning cover topics such as Neural Network Model, Graph Visualization, Logistic Regression, CNN Architecture, Deep CNN Pooling Layer Application, etc.
Term 4: SQL,MongoDB,Tableau,Power BI,Big Data ,Spark Analytics,R And GCP
Time duration:9 Weeks (72 hours)
2 Months
Power BI and Tableau is the data visualization and business intelligence tool. Big data is the data sets that are so voluminous and complex that traditional data processing application software is inadequate to deal with them. Topics in Big data you must cover that is- Hadoop, MapReduce, Hive, Sqoop, Spark e.t.c.
Term 5 :Project Life Cycle, Domain Knowledge & Capstone project
Time duration:8 Weeks (60 hours)
1.5 Months
Each step in the lifecycle of data science projects depends on different skills and tools. Awareness of the industrial scenario,domain knowledge would allow professionals working in this sector to understand the real-world scenario and the specific problems of the industry
Projects And Job Assistance
Integrated with the curriculum
When: After every module
Candidates Effective completion of Term 1, Term 2, and Term 3, become eligible for the Career Assistance Program, In this programmer, we have offered Interview Prep Session & Mock Interview, Participating in Live Kaggle Competitions. Guaranteed Career References for Data Science/ML positions of an engineer.
Take part in guided workshops from various domains for real-time projects and get project support/mentorship from expert instructors.
TRANSITION STEPS
Learning Track
Step 1
Learn Full Stack DS & AI
Learn basics Python, Statistics, ML, NLP , DL, SQL and other modules in depth with real time case studies.
Step 2
Choose domain & get trained
You can select multiple domains and get trained based on your work experience, academics and career goal.
Step 3
Work on real time projects
Work on multiple real time industry projects and capstone from multiple domains with mentorship of our expert.
Step 4
Resume Prep & Mock Interview Session
Update your resume and practice mock interviews. Get 100% guaranteed job referrals for data scientist & ML Engineer.
FAQs
Frequently Asked Questions
What are the Prerequisites for data science certification Course?
There are no prerequisites for this course as all the modules are covered from basics and scratch. If you are from a non-technical and non-programming background, We provide special classes and assistance so that you can learn to program.
What if i miss few classes?What is flexi pass Option ?
In case you miss classes, You will be provided backup classes in other batches. If you miss more classes, You can request for batch change and join our next batch. To solve this issue, We provide a flexi-pass option for our data science course.
Those who enroll for live classroom training are eligible for a Flexi-pass of 6 months. With this option, We share your access to all ongoing batch details for a period of 6 months, so that you can attend live classroom sessions from any batches and learn at your own pace. This option also helps you to finish the course early as you can attend multiple batches simultaneously.
This option is best for someone working in shifts or needs to work on weekends sometimes.
What is the duration of this Course?
Duration Of this course is around 13 months, weekends only (4 hours on Sat and Sunday)
Course Duration: 300+ hours (Approx)
Course Duration: 300+ hours (Approx)
The total duration of this course is 13 months but after 9 Months( once 70% Of Course Completion is over you can start working on Real-Time Projects and attend job interviews).
What is the mode of training?
We provide both options i.e Classroom training( in Marathahalli – Bangalore ) and instructor-led live training option. Based on your requirement and need, you can choose training mode.
How long will i get support for Job Assistance?
For 1 Year (After your course completion)
What is Job Readiness Program?
Job Readiness program helps you to prepare for data science interviews.
Resume Preparation Session: Expert guidance for writing a resume for data scientist Role
Preparing Project For interviews: Will help you to prepare and write a project description in your resume
Interview Guidance And Prep Session: 6 hours of interview readiness session to help you to prepare for interviews
One on One Mock Interviews
What is the eligibility for job assistance program?
You should have completed the course with
- A minimum score of 70% in Assessment
- Should have completed Assignments and case studies
- Worked and successfully completed a min. the recommended number of real-time projects
Will i get referral for jobs?
Yes,We provide placement Assistance by referring your profile through our partnered consultancies and companies for relevant positions(Data Science/Data Analyst Roles).
Contact course manager/Counsellor to know the eligibility of job assistance program
Note: We don’t provide any 100% Job Guarantee
Can i Pay in installment?
There ai 0% EMI Available on Major credit cards.Please check with our sales team to know how 0% EMI on credit cards works.
Is there any discounts available ?What about group discounts?
We have promotions and early bird offer from time to time. Please check on the website or talk to our sales team to know more about the applicable discounts.
Group Discount: Group discount is applicable if you are joining with your friends.
- 5% extra discount: Group of 2
- 8% extra discount: Group of 3
- Max 10% group discount: Group of 4+ members
How many Real Time Projects will be covered in this Course?
There is a set of 15+ Real-time & 2 Capstone projects from multiple domains like Banking, Finance, Insurance, Retail, Healthcare, and manufacturing. Based on your interest and domain, You can choose the project to work on.
How Projects Will be executed?
Your Project group will be assigned a project mentor and will be given the chance to work on the project With guidance and Assistance.
About our mentors
We strive to deliver the best quality training as it is one of our serious priorities, all our mentors are field professionals and have at least 8+ years of experience in the respective field.
Until when the students gets the facility of mentorship?
Students will be guided by our mentors until the end of a course. A student can seek help from our mentors even after course completion for knowledge and placement purposes.
Can a student choose to study from their desired mentor?
Every batches have respective mentors but if a student is not satisfied with any mentor’s teaching method they can raise the issue with the management and get to the facility to choose their mentor.
Project mentors
We have specified mentors for teaching and training purposes, the mentors you seek during project sessions are professionals at carrying out the project courses.
How long a student will be supported for career and job opportunities?
Students will be supported by the management in fetching job opportunities starting from the end term of the course. If any student doesn’t succeed in fetching a job they will be supported by the management for an entire 6 Months after the completion of the course.
What is the eligibility for a job assistance program?
A student will be eligible for job assistance only if they have:
- Minimum score of 70% in the assessment
- Completed the assignments and case studies
- Worked on and successfully completed the minimum recommended number of real-time projects
Does students get a referral for jobs?
Yes, we run Placement Assistance in where we refer eligible student profiles to different IT companies through our partnered consultancies and companies for relevant positions (Data Science/Data Analyst Roles).
For further information, you may contact the course manager/Counselor to know the eligibility of the job assistance program
Note: We don’t provide 100% Job Guarantee
For further information, you may contact the course manager/Counselor to know the eligibility of the job assistance program
Note: We don’t provide 100% Job Guarantee
SCHEDULE ONE ON ONE PROFILE REVIEW SESSION
Career Counselling,
From Domain Expert
From Domain Expert
Understand your personalised transition into data science from your current domain.
Know if your prior experience will be considered relevant for your career in data science.
Profile review will help you to resolve your queries about real time projects, job assistance, course modules etc.
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Data Science and AI For Managers
₹95,000.00
Data Science Certification Course for senior leaders, project managers to Change Your Existing Domain and start your career as data science/analytics manager.This course will benefit you to master data science skills and will help you to to handle interview with more confidence if you are looking for job in data science domain.
