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Advanced Certification in Business Analytics

This Advanced Certification in Business Analytics online course by the Fore School of Management and the University of California, Riverside will help you to gain strong skills in SQL, Python Programming, Microsoft Excel, Machine Learning, PowerBI, etc. This business analytics program follows a hands-on approach through real-life projects for learners to earn practical experience under the 24/7 guidance of our SMEs. Enroll in this course and become a certified Business Analyst expert.

Only Few Seats Left No Prior Programming Knowledge Required

Upskill for Your Dream Job

Learning Format

Online Bootcamp

Live Classes

8 Months



3 Guaranteed Interviews

by Intellipaat


Hiring Partners

About Program

This highly comprehensive Advanced Certification in Business Analytics Course will make you a master of all the basic and advanced level skills that are crucial in the field of Business Analytics. You will learn about KNIME, Domain Knowledge, Azure SQL database, Probability, OOPs, and many more crucial concepts in this program.

Key Features

400 Hrs of Applied Learning
218 Hrs of Self-Paced Learning
50+ Industry Projects & Case Studies
One-on-One with Industry Mentors
24*7 Support
Soft Skills Essential Training
50+ Live sessions for a period of 8 months
Learn from UCR & FSM Faculty
Learn & Mentored by top Industry Practitioners
3 Guaranteed Interviews by Intellipaat
Designed for Working Professionals & Fresher's
No Cost EMI Option
Dedicated Learning Management Team
Career Services by Intellipaat

Free Career Counselling

We are happy to help you 24/7

About Fore School of Management and University of California Riverside

The FORE School of Management (FSM) and University of California Riverside (UCR) are one of the best educational institutions leading the Path of providing quality Academic Training to individuals. FSM and UCR were founded in 1981 and 1907 respectively and both these institutions have been working for decades with industry and academia forRead More..

Key Achievements of Fore School of Management and University of California Riverside

  • FSM is ranked 19th based on Graduation Outcome
  • UCR is ranked among the top 15 public universities in USA
  • FSM is accredited by the National Board of Accreditation
  • UCR has been ranked number one university in US fours years in a row for social mobility
  • UCR has had 2 nobel prize winners

Upon the completion of this program, you will receive:

  • Advanced Certification in Business Analytics from FORE School of Management and University of California Riverside

Career Transition

55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

400+ Hiring Partners

Who can apply for the course

  • IT Professionals
  • Sales Professionals
  • Supply Chain Network Managers
  • Marketing Managers
  • Business Analyst Aspirants
  • Freshers and Undergraduates can apply for the course as well
Who can aaply

What roles does Advanced Certification in Business Analytics Course Provide?

Business Analyst

Work in collaboration with the UI/UX designers to create necessary documents, such as wireframes, UI, and design flow as needed.

Marketing and Sales Analyst

Improve user experience with the help of personalization and help clients leverage analytical and digital technologies.

Operations Analyst

Offer support, reports, and validate budgeting and forecasting processes.

Data Analyst

Conduct analysis on a portfolio to find improvements and patterns in the regions of overall risk mitigation and loss frequency.

BI Developer

Create the required infrastructure to get optimal data extraction, transformation, and loading from a range of data sources.

Business Analyst Manager

Business analyst manager specializes in performing research and analysis to devise strategies for optimal business operations and services, ensuring efficiency and increased productivity.

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Skills to Master



Python Programming

MS Excel


Machine Learning

Data Mining



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Tools to Master

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Live Course Self Paced

SQL Basics –

  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables

Advanced SQL –  

  • SQL Functions, Sub Queries, Rules, Views
  • Nested Queries, String Functions, Pattern Matching
  • Mathematical Functions, Date-time Functions, etc.

Deep Dive into User Defined Functions

  • Types of UDFs, Inline Table Value, Multi-Statement table.
  • Stored Procedures, Rank Function, SQL ROLLUP, etc.

SQL Optimization and Performance

  • Record Grouping, Searching, Sorting, etc.
  • Clustered Indexes, Common Table expressions.

Introduction to Python and IDEs – The basics of Python Programming Language, how you can use various IDEs for python development like Jupyter, Pycharm, etc.

Python Basics – Variables, Data Types, Loops, Conditional Statements, Functions, Decorators, Lambda Functions, File Handling, Exception Handling, etc.

Object Oriented Programming – Introduction to OOPs concepts like Classes, Objects, Inheritance, Abstraction, Polymorphism, Encapsulation, etc.

Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding.

Extract Transform Load

  • Web Scraping, Interacting with APIs 

Data Handling with NumPy

  • NumPy Arrays, CRUD Operations, etc.
  • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a Matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

  • Loading the data, Dataframes, Series, CRUD operations, Splitting the data, etc.

Data Preprocessing

  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, Standardization, etc.
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, Cross validation techniques, Train-Test Split, etc.

Data Visualization

  • Bar Charts, Scatter Plots, Count pPots, Line Plots, Pie Charts, Donut Charts, etc, with Python Matplotlib.
  • Regression plots, categorical Plots, Area Plots, etc, with Python Seaborn.

Excel Fundamentals

  • Reading the Data, Referencing in formulas, Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
  • Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security
  • VBA Macros, Ranges and Worksheets in VBA
  • IF conditions, loops, Debugging, etc.

Excel For Data Analytics

  • Handling Text Data, Splitting, Combining, Data Imputation on Text Data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.  

Data Visualization with Excel

  • Charts, Pie charts, Scatter and Bubble charts
  • Bar charts, Column charts, Line charts, Maps
  • Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Excel Power Tools

  • Power Pivot, Power Query and Power View

Classification Problems using Excel

  • Binary Classification Problems, Confusion Matrix, AUC and ROC curve
  • ○ Multiple Classification Problems

Information Measure in Excel

  • Probability, Entropy, Dependence
  • Mutual Information

This module will cover the excerpts from real-life case studies and domain information on finance, Marketing, Supply Chain Management, E-commerce and Analytics regarding how business analytics has been shaping the respective domains through advancements and problem solving skills.

Descriptive Statistics –

  • Measure of central tendency, Measure of spread, five points summary, etc.


  • Probability Distributions, Bayes theorem, Central limit theorem.

Inferential Statistics –  

  • Correlation, Covariance, Confidence intervals, Hypothesis Testing, F-Test, Z-Test, T-Test, ANOVA, Chi-Square Test, etc.

Introduction to Machine learning

  • Supervised, Unsupervised learning.
  • Introduction to scikit-learn, Keras, etc.


  • Introduction classification problems, Identification of a regression problem, dependent and independent variables.
  • How to train the model in a regression problem.
  • How to evaluate the model for a regression problem.
  • How to optimize the efficiency of the regression model.


  • Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
  • How to train the model in a classification problem.
  • How to evaluate the model for a classification problem.
  • How to optimize the efficiency of the classification model.


  • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
  • How to train the model in a clustering problem.
  • How to evaluate the model for a clustering problem.
  • How to optimize the efficiency of the clustering model.

Supervised Learning

  • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
  • Decision Tree – Creating decision tree models on classification problems in a tree – like format with optimal solutions.   
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach.
  • Support Vector Machine – SVM or support vector machines for regression and classification problems.
  • Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function.
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

Unsupervised Learning

  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
  • Dimensionality reduction – Handling multi-dimensional data and standardizing the features for easier computation.
  • Linear Discriminant Analysis – LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
  • Principal Component Analysis – PCA follows the same approach in handling multi-dimensional data.

Performance Metrics

  • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
  • Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model.
  • r2, adjusted r2, mean squared error, etc.

Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools, Data Warehousing, Power BI components, Power BI Desktop, workflows and report, Data Extraction with Power BI.
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data, M Query and Hierarchies in Power BI.


  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics 

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity
  • Embedded Power BI API and Power BI Mobile
  • Power BI Advance and Power BI Premium

Introduction to KNIME – learn about the KNIME tool that can be quite efficient for data analytics, creating workflows, etc.

Working with data in KNIME – Learn about creating workflows, loading datasets in KNIME, etc.

Loops in KNIME – Learn about the loops in KNIME that enables efficient data transformation in KNIME

Web scraping in KNIME – Learn about techniques in KNIME that enable web scraping to collect data directly from the web.

Feature Selection, Hyperparameter optimization in KNIME – Learn about hyperparameter optimization, feature selection in KNIME that will enable efficient machine learning models.

Case Study:

This case study will cover the following concepts:

  • Using KNIME to create end- to- end machine learning models with various algorithms like linear regression, logistic regression, decision tree, random forest, etc.
  • Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
  • Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
  • Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
  • Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
  • Object Detection – A much more advanced yet simple case study that will guide you towards making a machine learning model that can detect objects in real time.
  • Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you with some really helpful and actionable insights for specific stocks.
  • Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
  • AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.
  • The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals:
    • Extracting, loading and transforming data into a usable format to gather insights.
    • Data manipulation and handling to pre-process the data.
    • Feature engineering and scaling the data for various problem statements.
    • Model selection and model building on regression problems using supervised/unsupervised machine learning algorithms.
    • Assessment and monitoring of the model created using the machine learning models.

Apart from Python, you must also be familiar with R programming to build a successful career in Data Science. This Data Science with R module covers the various techniques and concepts in R which play a vital role in Data Science.

18.1 Introduction to R
18.2 R packages
18.3 Sorting DataFrame
18.4 Matrices and vectors
18.5 Reading data from external files
18.6 Generating plots
18.7 Analysis of Variance (ANOVA)
18.8 K-means clustering
18.9 Association rule mining
18.10 Regression in R
18.11 Analyzing relationship with regression
18.12 Advanced regression
18.13 Logistic Regression
18.14 Advanced Logistic Regression
18.15 Receiver Operating Characteristic (ROC)
18.16 Kolmogorov-Smirnov chart
18.17 Database connectivity with R
18.18 Integrating R with Hadoop

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Program Highlights

50+ Live Session across 8 months
218 Hrs of Self-Paced Learning
50+ Industry Projects & Case Studies
24*7 Support

Interested in This Program? Secure your spot now.

The application is free and takes only 5 minutes to complete.

Project Work

Projects will be part of your Advanced Certification in Business Analytics course to consolidate your learning. It will ensure that you have real-world experience in Business Analytics.

Practice 20+ Essential Tools

Designed by Industry Experts

Get Real-world Experience

Career Services By Intellipaat

Career Services

Career Oriented Sessions

Throughout the course

Over 20+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up skilling objective.


Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & Linkedin Profile from our career services team and learn how to grab the attention of the hiring manager at profile shortlisting stage


Mock Interview Preparation

After 80% of the course completion.

Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.


1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, past experience, and future career aspirations.


3 Guaranteed Interviews

After 80% of the course completion

Guaranteed 3 job interviews upon submission of projects and assignments. Get interviewed by our 400+ hiring partners.


Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Our Alumni Works At

Master Client Desktop

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends – the possibilities are endless and our community has something for everyone!


Admission Details

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

Submit Application

Submit Application

Tell us a bit about yourself and why you want to join this program

Application Review

Application Review

An admission panel will shortlist candidates based on their application


Application Review

Selected candidates will be notified within 3 days

Program Fee

Total Admission Fee

$ 1,579

Upcoming Application Deadline 5th December 2022

Admissions are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.

Program Cohorts

Next Cohorts

Next Cohorts

Date Time Batch Type
Program Induction 26th Nov 2022 08:00 PM IST Weekend (Sat-Sun)
Regular Classes 26th Nov 2022 08:00 PM IST Weekend (Sat-Sun)

Frequently Asked Questions

Why should I sign up for this Advanced Business Analytics certification course?

This Advanced Certification in Business Analytics is conducted by leading experts who will assist you in kick-starting your career in these domains through the vast industry-relevant experience that they carry.

Also, the course curriculum along with videos, live sessions, and assignments will help you gain in-depth knowledge in Business Analytics, apart from providing hands-on experience in these domains through real-time projects.

To register for the program, you can reach out to our learning consultants or contact us through the above-given details on this page.

Based on the current understanding, Business Analytics is growing rapidly. Business analytics is expected to impact and influence various industries, such as marketing, sales, customer experience, finance, risk management, and HR, in the near future. Business analytics certification courses will help in getting better-paying career prospects in the near future.

To become a business analyst, you need to have strong communication skills, domain knowledge, and an in-depth understanding of business analysis tools. A strong forte in aspects of problem-solving is a plus. It would be correct to say that to succeed as a business analyst, you need to have strong analytical skills. Thus, coding or programming is a plus, but not a mandatory requirement.

Our Learning Management System (LMS) ensures a customized learning experience that includes both live sessions & self-paced videos. In case you miss a live session, you will be able to watch the recorded video of the session so you don’t have to worry about missing out on a lesson.

You will be awarded an industry-recognized certification after the completion of the program.

Intellipaat’s certification has lifetime validity and is recognized by top organizations around the world.

If you happen to miss a session, there is nothing to worry about. You won’t have to miss anything because you can watch the session’s recording on the LMS.

Intellipaat is offering 24/7 query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail of email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our support team. However, 1:1 session support is provided for a period of 6 months from the start date of your course.

Once you complete Intellipaat’s training program, work on real-world projects, quizzes, and assignments, and score at least 60% marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some Fortune 500 companies.

Intellipaat provides career services that include Guarantee interviews for all the learners who successfully complete this course. FSM and UCR are not responsible for the career services.

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What is included in this course?

  • Non-biased career guidance
  • Counselling based on your skills and preference
  • No repetitive calls, only as per convenience
  • Rigorous curriculum designed by industry experts
  • Complete this program while you work

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