Program introduction 

As organizations have been adopting and investing in new technologies to leverage their data to take strategic business decisions and the increase in demand for data scientists has been observed in past few years. Basis the demand, data science has emerged as a new profession that is expected to derive sense from the vast stores of data across the world

Organizations are using data science tools to stay ahead of the cutting-edge competition and knowing their customer's behaviour and patterns to ensure that a better decision can be taken.

EY Learning Solutions has curated the “Certificate program in Advanced Data Science and Business Analytics”. The 200 hours of comprehensive program may help the participants enhance their career path by gaining proficiency, skills and the ability to build solutions through data to complex business problems. For the re-enforcement of the learning two major real-life projects are to be provided to each participant.

Key highlights of the program

  • Experiential learning
  • 3 simulated projects
  • Ongoing assessments
  • Internship opportunity with EY
  • 200 hours of virtual live training
  • 90% practical learning 
  • Training by EY consultants and Industry specialists 

Prerequisites

Bachelor’s degree in any discipline with a minimum of 50% aggregate marks in graduation or equivalent

Disclaimer | Terms & Conditions

  • Training programs are subject to a minimum number of participants. If a training program does not meet this criteria, then EY FAAS Learning Solution is entitled to cancel it at its discretion, without liability. In such instances, the EY FAAS Learning Solution team can offer the participants alternative options or a refund as applicable
  • Cancellation and refund policy
    A full refund less an administration fee of INR 5000 will be given for cancellation requests received up to 5 working days before the training. Cancellations must be made via emails only, before 5 working days deadline. Delegates who cancel less than 5 working days before the training, or who don’t attend, are liable to pay the full course fee, and no refunds can be given. However, if you wish to attend a subsequent scheduling of the course, and you have paid your course fee in full, the same could be done subject to availability of the seats in the subsequent batches. Replacement participants are always welcome

Module 1

  • Applied Statistics and Analytics using Excel
  • Sample and population data
  • Fundamentals of descriptive statistics
  • Measure of central tendency, variability
  • Standard normal distribution
  • Central limit theorem and standard error
  • Estimators and estimates
  • T-Distribution and margin of error
  • Confidence interval
  • Inferential statistics
  • Hypothesis testing
  • Type I error vs. Type II error
  • Descriptive statistics, ranks and ANOVA
  • Statistical test and regression
  • Excel dashboard with pivot tables
  • Create Excel dashboards using macros
  • Goal seek
  • What if analysis and solver
  • Introduction to VBA
  • Simple VBA codes for macros

 

Module 2

  • Database design and system
  • Fundamental database concepts
  • The entity relationship model (=ER)
  • The relational data model
  • Logical database design      
  • SQL: Querying and manipulating data
  • SQL data definition language
  • Single block queries
  • Aggregation
  • Joins and outer joins
  • Nesting
  • Negation
  • Data storage and indexing
  • File organization and indexes
  • Tree-structured indexing: B+-trees
  • Hash-based indexing
  • Indexes in PostgreSQL
  • RDBMS operators
  • Primary and foreign keys
  • Index in RDBMS
  • RDBMS normalization
  • Data abstraction
  • RDBMS extensions and intensions
  • Data independence in RDBMS
  • SQL querying (DDL) adware, rootkits, backdoors, zero-day attacks

 

Module 3

  • Exploratory analytics in Python
  • Exploratory analytics in Python
  • Introduction to Python for data science
  • Introduction to SQL
  • Statistics and data analysis
  • Data wrangling
  • Data visualization
  • Python libraries and data structures
  • Exploratory analysis in Python using Pandas
  • Introduction to series and data frames
  • Data munging in Python using Pandas
  • Multithreading
  • Fundamentals of data analytics
  • Web development with Python 
  • Project development with Python 

Module 4

  • Predictive modelling in Python
  • Supervised machine learning
  • Logistic regression
  • Decision tree
  • Random forest
  • Gradient decent, cost function
  • Linear regression with single and multiple variable to solve predictive problem

 

Module 5

  • Machine learning and artificial intelligence in Python
  • Unsupervised learning -k means clustering algorithm in machine learning
  • Practice dataset and visualization of dataset
  • Logistic regression-model (implement titanic data set)
  • KNN Methos
  • Support vector machines (SVMs)
  • Naïve Bayes theorem
  • Decision tree
  • Example of random forest for weather dataset
  • Deep learning
  • Neural networks
  • NLP ( Natural Language Processing)
  • Computer vision: Insufficient logging and monitoring

Module 6

  • Data visualization and story telling in Power BI
  • Introduction to Power BI
  • Introducing Power Pivot
  • Working with data (cleaning and transforming the data)
  • Enhancing the data model
  • Analyzing data through Power Pivot
  • Custom graphics and charts and maps
  • Data Analysis Expression (DAX)
  • Working with calculated tables
  • Using parameter tables
  • Publishing and managing Power Pivot ModelsPCI
  • Importing data with Power Query
  • Analyzing data with Power View and Power Map
  • Power BI and Excel together

 

Module 7

  • Blockchain and its use cases
  • Introduction to Blockchain
  • Blockchain objectives
  • Immutable ledger
  • Distributed P2P network
  • Bitcoin and blockchain data structures
  • Blockchain extended applications
  • Ehereum and smart contracts
  • Emerging trends in blockchain
  • Hyperledger
  • Ripple
  • R3
  • Blockchain and cloud computing

Course deliverables

33 sessions of 6 hours each during weekends

Three simulated projects will be provided to each participant, course material and data sets

Assessment after every module and final assessment

Hands-on learning on the tools – Excel, MySQL, Python, Power BI to enhance business acumen for participants

Query support through dedicated email post-training completion
6 months of online access to recorded videos  
 

 

Course schedule

Weekend proposition (8 hrs per day)

12:00 AM to 4:00 AM EST (Eastern Standard Time )

  • Saturday, 6 March, 2021
Level : Intermediate
Language : English
Training Mode : Live Virtual instructor-led training (VILT)
Audience profile
  • Engineering and science graduates/ post graduates / freshers
  • Working professionals¬† and entrepreneurs
  • Working professionals who intend to build their career in the field of web development and coding
  • Working professionals who wish to build their career in the field of machine learning and artificial intelligence
  • Working professional who intend to build their career in analytics
  • Fresh graduates and young professionals
  • Mid-level managers
  • Professionals working with MIS and operations

Neha Tuteja
neha.tuteja@in.ey.com
+ 91- 9873560293