Post Graduate Diploma in Data Science – TimesTSW

Overview

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from the huge amount of data – structured, semi-structured and unstructured, that is being generated every second. Digging in deep at a granular level to mine and understand complex behaviours, trends and inferences, data science is about gaining hidden insights that help and enable companies to make smarter business decisions.

  • Online marketplace behemoths use Data Science to identify major customer segments and unique shopping behaviours, which helps them with recommendation engines for “targeted messaging” catering to different market audiences.
  • Streaming content providers use data science to study viewing patterns and thus understand what drives user interest to accordingly create and deliver curated content.
  • Multinational FMCG majors use data science to understand future demand, and thus plan for and optimise production and distribution.

Data scientists explore data, understand and discover patterns, apply quantitative techniques to process data, build testing and training models and recommend action based on data analysis and insights. This data-driven insight is central to providing strategic guidance on how to act on findings.

Intel in the AI space

Intel is an American multinational corporation headquartered in Santa Clara, California, in Silicon Valley. The brand works with ecosystem partners to provide technologies that power a comprehensive, advanced analytics solution, from providing big data infrastructure to contributing to open-source projects. Intel’s strategy is to help ensure that every data scientist, developer and practitioner has access to the best platform and easiest starting point to solve the AI problem being tackled from the data centre to the edge.

  • Data-based decisions not only make mathematical but also business sense
  • India’s analytics, data science and big data industry is currently pegged at $2.71 billion annually in revenues, growing at a consistent rate of 33.5% (CAGR)
  • Market leaders heavily invest in data science in various sectors such as:
    • Telecommunication
    • Healthcare
    • Fintech
  • Demand for data scientists and data analysts in India has grown at a whopping rate of ~400% from 2017 to 2018
  • Supply grew at a bleak 19%

In association with Intel Corporation, one of the leading technology giants as knowledge partner, TPL’s objective is to reduce the supply-demand gap by creating equipped and expert data scientists out of young professionals.

Program Features

During the course of this program, participants will learn to:

  • Work on batch and real-time data projects
  • Understand the functional aspects of advanced data science through state-of-the-art infrastructure by Times professional learning platform
  • Classroom teaching by industry experts
  • Hands-on real-time training
  • Weekend curriculum schedule that minimises disruption for working professionals
  • Build expertise in popular deep learning and machine learning techniques and problem-solving methodology

Program Highlights

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Designed & Delivered by Experts

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Shape your learning path with electives

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Core courses on ML, AI, DL, Big Data and IoT enabled devices

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Work on Real World industry projects

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Experts from Google, Intel, Flipkart, Fractal Analytics

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Minimized disruption of work with Digital Library available on the go

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Get opportunities with leading brands by enrolling in PGP Machine Learning &AI

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Career advancement consulting – before, during and after the course

Curriculum

Essential Statistics

  • Data, Syntax, Packages, Vector, Data frame
  • R Loops, functions, working with data
  • String Manipulation, Regular Expression
  • Data base management using R

Advanced Statistics

  • Descriptive statistics
  • Statistics & Parameters
  • Sampling Statistics
  • Hypothesis Testing
  • Analysis of variance
  • Power analysis
  • Correlation and Regression
  • Visualization & Going beyond Regression

Control on python

  • Data structure & Loops
  • Library and packages | Control Flow and OOPS
  • Data Wrangling & Munging-I
  • Data Wrangling & Munging-II
  • Feature Engineering with texts
  • Data pre-processing
  • Data visualization
  • Time series analysis
  • Statistics with SciPy
  • Model Building

DBMS

  • Data collection
  • Introduction to Database Management Systems
  • SQL, MySQL

Big data analytics

  • Data ingestion HBase
  • Processing frameworks Map-Reduce/Spark/Hive

Art of story telling

  • MS- EXCEL, MS BI
  • TABLEAU – Parameter, Hyper parameter, Use cases
  • TABLEAU – Advanced SQL with Real time Data practice

Supervised Learning

  • OLS Regression
  • Regularized regression
  • Logistic regression
  • Decision Tree

Model and Optimization

  • Bagging, Boosting and stacking models
  • SVM for classification, Regression and interpretation
  • SGD for classification, Regression and sparse data
  • KNN for classification, Regression and evaluation
  • Naive Bayes
  • NN for classification, Regression and auto encoder
  • Model selection and evaluation
  • Recommendation Engine
  • Feature creation and selection

Unsupervised Learning

  • Cluster Analysis
  • PCA
  • Classification & Feature Selection

NLP

  • Text classification
  • Sentiment analysis

Big data Hadoop implementation

  • OLS Regression
  • Regularized regression
  • Logistic regression
  • Decision Tree

Supervised Learning

  • Spark and R studio
  • Python and spark, PySpark data import and management
  • PySpark: Feature creation, model building and model storing

Deep Learning

  • CNN, DNN, RNN Architectures
  • Activation function, optimization algorithms and concept of back propagation
  • Single, Multi Neural Network and other types of neural network architectures

Tensorflow and Keras

  • Tensorflow operations, modules, calculating ratio model
  • Syntax calculations, Keras architecture
  • Tensorflow & Keras implementation in DNN for classification regression, clustering and forecasting

To download a detailed version of the curriculum – click here

Who Should Apply

  • Working professionals with a minimum of 2 years experience
  • Freshers with an analytical bent of mind and superlative academic credentials with Engineering background
  • Should possess a Bachelor’s or Master’s Degree in Science/ Engineering/ Mathematics/ Statistics/ Economics or an equivalent qualification with Mathematics/ Statistics as one of the subjects

Additional Benefits

Lab & Hands on Exercises

  • Hands on coding of 100 hrs by industry experts.
  • Access to INTEL content and project data sets.
  • Work on latest INTEL hardware platforms through Jupyter notebooks.
  • Participate in INTEL Hackathon’s, webinars and seminars by industry experts

Capstone project to create a usable/public data product that can be used to show your skills to potential employers. Live project’s with corporate partner with mini-project electives from various industries (IT, Retail, BFSI, Healthcare and telecom)

Global Industrial Certification This course is designed with 20+ mini projects. Successful completion of a minimum of 4 mini projects along with the capstone project entitles you to gain the certification from INTEL

Resume Preparation & Job Assistance Mentoring by experts to help you choose the right project that will add weightage to your resume. Placement Assistance by a team of dedicated specialists to help place you in the right job within the right industry.

Join the ranks of a strong alumni network of Times Professional Learning and Leverage the corporate network of the Times Group for career and placement services

4 Guaranteed interviews upon successful completion

Duration
300 hours – knowledge content of about 150 hours from experts, 100hrs of coding, 50hrs of mini and capstone project work.
Pedagogy
  • Intel & domain based case studies
  • Classroom teaching by industry experts with hands-on real-time training
  • Weekend curriculum schedule to minimise disruption for working professionals
  • Intel platform for deep learning and optimisation of codes
  • Individual and group assignments, discussions and feedback
Evaluation Methodology
  • Assignments
  • Mini Projects
  • Capstone project
  • Coding Workshop
  • Attendance
Admission Criteria
Successful clearance of Admission Test and Personal Interview
Total Fees
₹1,75,000 +18% GST

Placements

A partial list of companies where students from TPL are working.

pwc
wns
cocnizant
wipro
tcs
hsbc
nabler
tvs
society-generale
krib-logo

Testimonials

PGDDS—Alumni-Testimonials-2

For course related queries call 9136908922 or email admissions.ds@timesgroup.com

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