DATA SCIENCE PRODEGREE
➤In Collaboration with Genpact, a Global Leader in Analytics
➤180 Hours of Learning Delivered in Classroom and Online Format
➤Course Covering Multiple Analytics Tools such as R, Python, SAS and Tableau
➤Hands-on Learning with 14 Industry Projects Across Diverse Industries
➤Learn to Build Complex Data Science Models and Solve Real-world Business Problems
➤Eligibility: Recent Graduates and Working Professionals
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Classroom Training
र 80,000/-
Online Instructor-Led Training
र 60,000/-
Online Self Paced Videos
Genpact Endorsed
Cutting-edge program designed and delivered in collaboration with Genpact, a global leader in Analytics solutions
Leading Tools
Master Data Science using leading tools such as R, Python, SAS and Tableau
Experiential Learning
Hands-on learning through 14 industry projects developed by industry experts, across multiple tools and industries
Placement Assistance
Extensive support via resume building, interview prep, mentorship and interview opportunities
Data Science Course Curriculum
The Data Science Prodegree has been designed in conjunction with multiple industry leaders to ensure that you learn exactly what employers need.
Batch Launch
- Intro to Program
- Curriculum Overview
- Learning Methodology
- Guest Lecture
Introduction to Data Science
- What is Data Science?
- Analytics Landscape
- Life Cycle of Data Science Projects
- Data Science Tools & Technologies
R for Data Science
- R Installation, R Studio, Understanding Data Structures in R – Lists, Matrices, Vectors
- Intro to R Programming
- R Base Software
- Understanding CRAN
- RStudio the IDE
- Basic Building Blocks in R
- Understanding Vectors in R
- Basic Operations Operators and Types
- Handling Missing Values in R
- Subsetting Vectors in R
- Matrices and Data Frames in R
- Logical Statements in R
- Lapply, Sapply, Vapply and Tapply Functions
Data Visualization using R
- Grammar of Graphics
- Bar Charts
- Histograms
- Pie Charts
- Scatter Plots
- Line Plots and Regression
- Word Clouds
- Box Plots
- GGPLOT2
Statistical Learning
- Measures of Central Tendency in Data
- Measures of Dispersion
- Understanding Skewness in Data
- Probability Theory
- Bayes Theorem
- Probability Distributions
- Hypothesis Testing
Analysis of Variance and Covariance
- One-Way Analysis of Variance
- Assumption of ANOVA
- Statistics Associatedwith One-Way Analysis of Variance
- Interpreting the ANOVA Results
- Two-Way Analysis of Variance
- Interpreting the ANOVA Results
- Analysis ofCovariance
Exploratory Data Analysis with R
- Merge, Rollup, Transpose and Append
- Missing Analysis and Treatment
- Outlier Analysis and Treatment
- Summarizing and Visualizing the Important Characteristics of Data
- Univariate, Bivariate Analysis
- Crosstabs, Correlation
Linear Regression
- What is Regression Analysis
- Covariance and Correlation
- Multivariate Analysis
- Assumptions of Linearity Hypothesis Testing
- Limitations of Regression
- Implementing Simple & Multiple Linear Regression
- Making Sense of Result Parameters
- Model Validation
- Handling Other Issues/Assumptions in Linear Regression
- Handling Outliers, Categorical Variables, Autocorrelation, Multicollinearity, Heteroskedasticity Prediction and Confidence Intervals
Logistic Regression
- Implementing Logistic Regression
- Making Sense of Result Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test Goodness of Fit Measures
- Model Validation: Cross Validation, ROC Curve, Confusion Matrix
Decision Trees
- Introduction to Predictive Modelling with Decision Trees
- Entropy & Information Gain
- Standard Deviation Reduction (SDR)
- Overfitting Problem
- Cross Validation for Overfitting Problem
- Running as a Solution for Overfitting
Linear Discriminant Analysis
- Multi-class classification
Basics of Python for Data Science
- Python Basics
- Data Structures in Python
- Control & Loop Statements in Python
- Functions & Classes in Python
- Working with Data
Data Frame Manipulation
- Data Acquisition (Import & Export)
- Indexing
- Selection and Filtering Sorting & Summarizing
- Descriptive Statistics
- Combining and Merging Data Frames
- Removing Duplicates
- Discretization and Binning
- String Manipulation
Exploration of Data Analysis
- Data Visualization & EDA
Time Series Forecasting
- Understand Time Series Data
- Visualizing Time Series Components
- Exponential Smoothing
- Holt’s Model
- Holt-Winter’s Model
- ARIMA
- ARCH & GARCH
Unsupervised Learnings
- K-Means Clustering
Dimensionality Reduction
- Principal Component Analysis (PCA)
- Scree Plot
- One-Eigen Value Criterion
- Factor Analysis
Introduction to Machine Learning
- Machine Learning Modelling Flow
- How to treat Data in ML
- Parametric & Non-parametric ML Algorithm
- Types of Machine Learning
- Performance Measures
- Bias-Variance Trade-Off
- Overfitting & Underfitting
- Optimization Techniques
- Scikit-Learn Library
Supervised Learning
- Linear Regression
- Linear Regression with Stochastic Gradient Descent, Batch GD
- Optimizing Learning Rate
- Momentum
Logistic Regression
- Logistic Regression with Stochastic Gradient Descent, Batch GD
- Optimizing Learning Rate
- Momentum
K Nearest Neighbour
- Understanding KNN
- Voronoi Tessellation
- Choosing K
- Distance Metrics – Euclideam, Manhattan, Chebyshev
Decision Tree & Random Forest
- Fundamental Concepts of Ensemble
- Hyper-Parameters
Support Vector Machines
- What is SVM?
- When to use SVM?
- Understanding Hyperplane
- What is Support Vector?
- Understanding Langragian Multiplier, Karush Kuhn Tucker Conditions
- SVM Kernels – Radial Basis Function, Gaussian Kernel, Linear Kernel
- Optimizing the C Parameter
- Regularization
Introduction to SAS and SAS Programs
- What is SAS?
- Key Features
- Submitting a SAS Program
- SAS Program Syntax Examining SAS Datasets Accessing SAS Libraries
- Sorting and Grouping Reporting Data
- Using SAS Formats
Reading and Manipulating Data
- Reading SAS Datasets
- Reading Excel Data
- Reading Raw Files
- Reading Database Data
- Creating Summary Reports
- Combining Datasets
Data Transformation
- Writing Observations
- Writing to Multiple Datasets
- Accumulating Total for a Group of Data
- Data Transformations
Macros
- Introduction to Macro Variables
- Automatic Macro Variables
- User Defined Macro Variables
- Macro Variable Reference
- Defining and Calling Macros
- Macro Parameters
- Global and Local Symbol Table
- Creating Macro Variables in the Data Step
SQL
- Introduction to SQL
- How Does RDBMS Work?
- SQL Procedures
- Specifying Columns
- Specifying Rows
- Presenting Data
- Summarizing Data
- Writing Join Queries using SQL
- Working with Subqueries
- Indexes and Views
- Set Operators
- Creating Tables and Views Using Proc SQL
Tableau Basics
- Introduction to Visualization
- Working with Tableau
- Visualization in Depth
- Data Organisation
- Advanced Visualization
- Mapping
- Enterprise Dashboards Data Presentation
Best Practices for Dashboarding and Reporting and Case Study
- Have a Methodology
- Know Your Audience
- Define Resulting Actions
- Classify Your Dashboard
- Profile Your Data
- Use Visual Features Properly
- Design Iteratively
Training Methodology
With a strong emphasis on ‘learning by doing’, our programs are developed with the goal of creating well-rounded, job-ready professionals that can add immediate value to any organization.
STEP 1: Instruction through blended learning
The course is delivered in two modes: Classroom and Online Live Virtual Classes to cater to your learning preferences. Our state-of-the-art LMS provides self-paced videos on conceptual topics which are perfectly complemented with live lectures.
STEP 2: Application through real-world examples and projects
Learn about real-world use cases and bring your learning into action through multiple in-class projects.
STEP 3: Reinforcement through capstone project and assessments
Each topic is followed by tests and quizzes. You get to work on a capstone project at the end of the course that is assessed by industry experts.
24/7 Support
Get 24/7 access to your Data Science course material on our state of the art learning management system; extended access to all course material after the batch ends, and a dedicated student hotline with 24/7 support to help resolve queries.Hands on Projects
PROJECT 1
PROJECT 2
PROJECT 3
PROJECT 4
PROJECT 5
PROJECT 6
PROJECT 7
PROJECT 8
PROJECT 9
PROJECT 10
PROJECT 11
PROJECT 12
PROJECT 13
PROJECT 14
Career
The Career Assistance Services (CAS) team works hand in hand with you to further your career aspirations. We thoroughly prepare you to be interview-ready through resume building sessions and interview preparation workshops.
“Studying in the Data Science domain at Imarticus has been an outstanding learning experience for me. The trainers and teaching staff have always been supportive and efficient in their respective fields. They continuously inculcate valuable knowledge and guide us throughout. The collaboration of thorough practical knowledge along with theoretical studies makes the Imarticus team highly suitable for those looking to gain important knowledge. I am extremely satisfied by acquiring the knowledge and experiences related to Data Science from Imarticus.”
– Priti Motiwani
“Belonging to a non-technical background, the whole idea of studying Data Science was extremely traumatic for me. But with Imarticus, the overall experience was very satisfying. The in-depth teaching and additional practical knowledge provided at Imarticus about Data Science has helped me achieve great heights in my career. The teaching staff and the learning atmosphere were very supportive. Especially, both the faculties of R and Python were well-experienced, knowledgeable and simultaneously helpful. This highly boosted up my knowledge regarding Data Science.”
– Rajashree Pakhare
“My experience at Imarticus Learning was really helpful and informative. They establish a strong platform for their students with the help of their knowledgeable curriculum, experienced and helpful trainers, and a unique learning environment. Along with the in-depth knowledge about the Data Science concepts and theories, they provide you with efficient placement skills. The amalgamation of theoretical and practical exposure make the Imarticus platform suitable one. In addition to this, the job opportunities provided encourage us to establish a steady career.”
– Nachiket Thakur
Business Intelligence Analyst
Web & Social Media Analyst
Data Scientist
Research Analyst
Business Analytics Tech Consultant
Data Analyst
CRM Analyst
Data Science Consultant
LARGE IT COMPANIES WHO HAVE AN ANALYTICS PRACTICE
ANALYTICS KPOs
IN-HOUSE ANALYTICS UNITS OF LARGE CORPORATES
NICHE ANALYTICS FIRMS
Imarticus provides career assistance throughout the program to guide you on the right career path and assist you with job readiness from Day 1.
GETTING STUDENTS JOB READY
RESUME BUILDING
Refining and polishing the candidate’s resume with insider tips to help them land their dream job
INTERVIEW PREP
Preparing candidates to ace HR and Technical interview rounds with model interview questions and answers
MOCK INTERVIEWS
Preparing candidates to face interview scenarios through 1:1 and panel mock interviews with industry veterans
ACCESS TO OUR PLACEMENT PORTAL
Access to all available leads and references from open and private networks on our placement portal
Certification
On completion of the Data Science Prodegree, aspirants will receive an industry endorsed Certificate of Achievement, which is co-branded by Genpact and Imarticus Learning.
Collaboration with Genpact
The Data Science Prodegree is co-created with Genpact as the Knowledge Partner and comes with a cutting-edge industry aligned curriculum and learning methodology. You will benefit in terms of:
Certification
All candidates earn a certificate, co-branded by Genpact as a knowledge partner and Imarticus
Industry Approved Curriculum
Genpact, with its vast global experience in the Analytics domain, has assisted with the curriculum design to ensure complete industry alignment, and maximum learning outcomes
About Genpact
Genpact is a global leader in digitally-powered business process management and services across technology, analytics, AI, and organizational design. The company boasts net revenues of US$3 billion with more than 85,000 employees spread across 25 countries and 1/5th of the Fortune Global 500 companies as its clients.
Industry Advisors
The Data Science program is developed in consultation with senior Industry experts to ensure a high degree of relevance in accordance to the needs and demands of the industry.
IQBAL KAUR
Ex- Target and Genpact
AMIT KHANNA
Managing Partner – YDatalytics (Antuit & Y Group)
AKASH BHATIA
Founder And CEO,
Infinite Analytics, Kyazoonga
Admissions
The Data Science Prodegree is ideal for students and experienced professionals who are interested in working in the analytics industry, and are keen on enhancing their technical skills and business understanding of data science.
Experienced Professionals
Professionals who are looking to up-skill or change career paths. Technical experience is a plus.
Job Seekers
Recent Graduates in Bachelors or Masters in Science, Math, Statistics, Engineering, Finance or Computer Applications/IT.
Global Certifications
Those looking to enhance their resumes & build a portfolio of demonstrable work in one of the most coveted professions of this century.
Faculty
-
Tharangini Vijay Kumar
Basic and Advanced Statistical Techniques, Predictive Modelling
Data Science Course Videos
Genpact talks about the Imarticus Data Science Prodegree and the current skill gap while hiring for data scientists
Importance of Data Science and why Imarticus partnered with Genpact to deliver it
Learn more about the curriculum for the DSP program
FAQs
What is the Genpact collaboration about?
Genpact is a global leader in digitally-powered business process management and services and works with over 1/5th of the Fortune Global 500 companies across technology and analytics with revenues of $2.46 billion and 70,000 employees spread across 25 countries.
Genpact is involved in the Data Science training course through curriculum design, project reviews, guest lectures and mentorship. A partnership with such an industry leader ensures that the curriculum is timely and industry relevant.
What is the format of the Data Science course?
The Data Science Prodegree is a 180 hour classroom or online training program, that provides aspirants with an in-depth understanding of Data Science, Statistics, as well as hands-on learning of leading analytical tools such as SAS, R, Python and Tableau. The delivery hours include 140 hours of live instructor led training, and 60 hours of self paced instructor videos.
Classroom batches: Classroom training by expert faculty at our Imarticus centers in Mumbai, Bangalore Koramangala, Bangalore Marathahalli Chennai, Gurgaon, Hyderabad, Ahmedabad, Pune and Thane
Online batches: Live Instructor-led Virtual Classes (Webinars) with expert faculty for real-time learning and interaction with batch mates.
Class times for both formats are fixed and you are required to be available for your classes at a predefined time each week. Both formats come with 60+ hours of engaging Instructor videos that you can watch as per your convenience before attending your lecture (be it in class or virtually).
What is the duration of the Data Science course?
The DSP is a 4-month program done part time (on weekends), and a 2 month program if done full time on the weekends. Please contact the nearest center for more information.
What certificate will I get?
Post the successful completion of the program, you will be awarded the Data Science Prodegree Certification, co-branded with Genpact, as a Knowledge Partner.
What should be my expectation from the Data Science training program?
The program is designed for aspirants with no or limited data analytics experience. As a result, the curriculum covers basics to advance topics. The program is rigorous and requires a time commitment from any participant (about 8-12 hours a week including delivery hours).
Do you guarantee placements?
As a policy, we do NOT guarantee placements. Our Career Services team will provide you with the ideal platform to launch your career through resume building sessions, mock interviews, mentorships, interview preparation. Our team will also work towards finding you interview opportunities, and help you start your analytics career.
What is the fee of the Data Science training course?
The Data Science training course is Rs.80,000 for the classroom mode, and Rs.60,000 for the online mode. You can pay by Credit card, Debit Card or Net banking from all leading banks to the nearest Imarticus center, or online. In the event you are unable to pay, please contact 18001037480 for further assistance.
