Why Ivy
IVY Pro School is a Top Ranked Data Science and
Analytics schools in the country consistently since the last
5 years (Analytics India magazine, Silicon India Magazine).
We are the official training partners of companies like
Honeywell, Cap Gemini, Genpact, HSBC, Cognizant, eBay/Paypal
etc and more than 100 Analytics companies have recruited our
students. Our prized faculty are associated with esteemed
organizations like ISI, IIMs IITs and prestigious US
universities.
Course Outline of Business Analytics
Certification Course
Dashboarding and
Automation Using Advanced Excel
- Data Hygiene – Clean, structure, and prepare your dataset
- Formatting – Number and table formatting for clarity
- Filtering & Sorting – Auto filters, advanced filters, custom sorting
- Cell Referencing – Relative, absolute references & formula handling
- Basic Functions – SUM, AVERAGE, COUNT, PRODUCT, etc.
- Date Functions – TODAY, DATEDIF, EOMONTH, WEEKDAY, etc.
- Conditional Functions – SUMIFS, COUNTIFS, AVERAGEIFS
- Database Functions – DSUM, DAVERAGE using criteria tables
- Dynamic Arrays (Google Sheets) – FILTER, SORT, UNIQUE, TEXTJOIN
- Pivot Tables – Layout, grouping, calculated fields, summarizing
- Charts – Chart creation, types, usage, sparklines
- Dashboards – Planning, business insights, slicers, summaries
- Logical Functions – IF, AND, OR, IFERROR, SWITCH etc
- Lookup Functions – VLOOKUP, XLOOKUP, MATCH, INDIRECT etc
- Text Functions – LEFT, MID, FIND, SUBSTITUTE, CONCAT etc
- Conditional Formatting – Color scales, data bars, formulas
- Data Validation – Lists, error alerts, dependent dropdowns
- Goal Seek & Solver – Solve optimization and reverse calculations
- Gen AI – How to Use Gen AI in the Microsfor Excel, Use Lab.Generative.
- Chatgpt – How to use Prompt engeerning for Data Analysis
SQL Queries &
Relational Database Management
- SQL Basics: Introduction to Database Management System (DBMS), Introduction to Google BigQuery and MySQL, CRUD Operations (Create, Read, Update, Delete)
- Data Manipulation and Transformation: Logical, Numerical, and Mathematical Operators, Conditional Statements (CASE, IF, etc.)
- Data Cleaning: Type Casting and Data Type Conversion, Date and Time Formatting, Text Formatting (TRIM, LOWER, UPPER, REPLACE, etc.)
- Pattern Matching: LIKE Operator, REGEXP Function (Regular Expressions)
- Joins and Relational Databases: Primary Key, Foreign Key, Different Types of Joins (Inner, Left, Right, Full, Self, Cross), Indexing and Performance Optimization, Database Normalization
- Aggregation and Grouping: Aggregate Functions (SUM, AVG, COUNT, etc.), GROUP BY and HAVING, Pivoting and Rollup, Common Table Expressions (CTEs), User Defined Variables
- Window Functions: How to use Window Functions for Advanced Aggregation (RANK, DENSE_RANK, ROW_NUMBER, LEAD, LAG, etc.)
- Data Reusability: Creating and Using Views, Stored Procedures and Parameters, User Defined Functions (UDFs)
- Subqueries: Simple Subqueries, EXISTS and NOT EXISTS, Correlated Subqueries
- Cloud Services : Using Google BigQuery for Large-Scale Querying, Exporting and Sharing Query Results in Google BigQuery, Setting Permissions and Access Roles in Cloud Databases, Running Scheduled Queries in BigQuery
- Online SQL Platforms : Connecting to Cloud Databases via MySQL Workbench, Writing and Testing SQL in Online IDEs (Mode, POPSQL, DB Fiddle, etc.), Integration with Cloud Storage (e.g., Google Cloud Storage, AWS S3)
Tableau Essentials
- Introduction to Tableau: Approaching Business Problem, Different Sections of Tableau
- Connecting and Shaping Data: Connecting Data Source to Tableau, Pivoting, Calculated Field, Dimension vs. Measure
- Introduction to Basic Charts
- Working With Marks Card
- Different Filters in Tableau
- Introduction to Calculated Field: Summarization Function, String Manipulation Function, Date Functions, Logical Functions
- Combining Tables: Joins, Unions, & Blending
- Table Calculations: Primary and Secondary Calculations
- Parameters: Dimension, Measure, Sort, TopN, Date
- Groups & Sets
- Analytics: Forecasting, Trend Line, Clustering
- Dashboard Building & Actions: Filter, Highlight Go to, Set, Parameters
- LODs: Include, Exclude, Fixed, Table Scoped
- Projects: RFM (Recency, Frequency, Monetary value) Analysis, Customer Retention Dashboard
Business Statistics
- Types of Data
- Correlation
- Measures of Central Tendency
- Measures of Dispersion
- Probability
- Probability Distributions
- Sampling and Estimation
- Hypothesis Testing
- Data Modeling
Predictive Modeling
with R
- Introduction to R – R console, Rstudio
- Introduction to R Vectors,Matrices
- Introduction to Programming – condition, loops and function
- Introduction to R DataFrames and manipulation of data
- Introduction to Advanced R Programming with packages (dplyr etc.)
- Probability Distributions and Hypothesis Testing
- Overview of ggplot2
- Linear Regression in R (Project: Predict Academic Performance of School Students)
- Logistic Regression in R (Project: Predict Customer Churn for a Telecom Firm)
- Decision Trees (Project: Predict Loan Approval)
- Forecasting & Time Series Analysis (Project: Sales Forecasting for Retail)
- Clustering (Project: Airline Customer Segmentation)
Data Science with
Python
- Introduction to Python: Setting up Python, different IDEs.
- Data Types – integer, float, complex, strings
- Advanced Data Types – List, Tuple, Set, Dictionary
- Program Control Flow – conditions, loop
- Function and Numpy
- Pandas DataFrame Basics: Accessing, Filtering, and Cleaning
- Pandas DataFrame Operations: GroupBy, Merging, and Transformations
- Exploratory Data Analysis: Univariate, Bivariate & Multivariate Analysis
- Linear Regression (Project: Ad Revenue Prediction)
- Logistic Regression (Project: Customer Click Prediction)
- Decision Tree (Project: Used Car Price Prediction)
- Forecasting & Time Series Analysis (Project: Retail Store Sales Forecasting)
- Clustering (Project: Customer Profiling)
- Deployment, API Integration, and GitHub for Project Sharing
Machine Learning
Essentials
- Recap of Linear and Logistic Regression, Decision Tree (Project: Predicting Car Prices)
- Ensemble Learning – Random Forrest (Project: Loan Risk Prediction)
- Boosting Algorithms – Gradient, Ada and Xgboost (Project: Predicting House Prices, Disease Diagnosis Prediction)
- Support Vector Machines (Project: Youtube Video Analysis)
- Naive Bayes (Project: Customer Churn Prediction)
- K-Nearest Neighbors (Project: Customer Feedback Categorization)
- PCA, K-Means Clustering, Hierarchical Clustering, DBSCAN (Project: Retail Customer Profiling)
- Text Preprocessing and Cleaning with Regular Expressions
- Text mining, wordcloud and Sentiment Analysis (project: Indigo tweets)
- Text Classification using NLP and AI (Project: IT Ticket Classification)
- Building and Deploying a GEN AI Model for Custom Text Classification using Transformers
- Introduction to Neural Networks (Project: Predicting Customer Lifetime Value, Credit Risk Assessment)
- >Recurrent Neural Networks with LSTM (Project: Infosys Stock price Prediction)
- Convolutional Neural Networks (Project: Face Image Classification)
Course Duration Business Analytics
Certification Course
Total – 195
hours
Course Fee for Business Analytics
Certification Course
INR 39,000 + GST
Inclusive of All Taxes
No
Cost EMI Avaliable
Cost EMI Avaliable
We
also facilitate loans to students
also facilitate loans to students
Detailed course brochure.
Eligibility
Under-grad or Post-grad in Maths/
Stats/ Economics/ Commerce/ Finance
MBA Students (All Streams)
3rd and Final Year B.E./ B.Tech
Banking/ Finance/ IT/ KPO Working
Professional
B.Sc and M.Sc
Industry Projects
Real Industry Projects &
Internships in SAS / R
- B2B Chemical Sales: Predict Customer Churn
Score for a Chemical Manufacturing Company - Banking & Finance: Create a Customer
Ranking Scorecard for a Cross-selling Insurance Product in a
Bank - Banking & Finance: Predict Probability
of Default of Customers for a Money Lending Firm - Pharma: Predict Drug Efficacy for a
Pharmaceutical Company - Automobile Insurance: Predict Customer
Total Claim Amount - Retail: Predict Product Affinities for 100+
Products - Rental / Real-Estate: Predict Rental Rates
considering 100+ Parameters - Aviation: Predict Airlines Revenue &
Ticketing Pricing Mix - HR: Predict Employee Attrition
- Retail Marketing: Analyse Marketing
Campaign Effectiveness
Enquire / Enroll for the course
Launch Your Global Analytics Career with IVY
Why Analytics?
As organisations and SMEs become increasingly data-driven, there arises
the need to make sense of all that data and convert it into meaningful insights. In a
competitive environment, analytics becomes the driving force that facilitates decision
making – to leverage the insight for innovative strategies as well as for an effective ROI
on investment.
the need to make sense of all that data and convert it into meaningful insights. In a
competitive environment, analytics becomes the driving force that facilitates decision
making – to leverage the insight for innovative strategies as well as for an effective ROI
on investment.