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Professional Certificate Course in

Business Analytics

Leverage the power of business analytics and statistics in your career . Master Tableau for comprehensive Data Visualization for your business needs.
  • 72 Hours Classroom & Online Sessions
  • 140+ Hours Assignments & eLearning
  • 100% Job Assurance
  • 2 Capstone Projects
  • Industry Placement Training
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Application Deadline: 05th June

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Calendar-On-Campus Classes

  • 06th
  • 9:00 AM - 6:00 PM

Business Analytics

business analytics course duration

Total Duration

4 Months

business analytics course pre-requisites


  • Computer Skills
  • Basic Mathematical Concepts
  • Analytical Mindset

Business Analytics Certification Course Overview

The Professional Certification in Business Analytics is a foundation course for students and professionals who want to develop niche data skills for their chosen industry domain or function area. Become a Business Intelligence and Data Visualisation expert and surge ahead in your career. The nine-day Business Analytics certification course covers all the essential Analytical and Statistical techniques for effective business decision making. This programme introduces the student to the basic concepts of Python language.

Course Details

Business Analytics Training Learning Outcomes

Work with various data generation sources
Perform Text Mining to generate Customer Sentiment Analysis
Understand how to use various analytics tools and techniques
Develop an understanding of descriptive and predictive analytics
Apply data-driven, Machine Learning approaches for business decisions
Build prediction models for day-to-day applicability
Perform forecasting to take proactive business decisions
Use Data Visualization concepts to represent data for easy understanding
Create business reports, dashboards, stories, maps etc. to draw meaningful business insights

Professional Business Analytics Course Modules

Understand various data sources and why organizations are gearing up to store the data like never before. Learn on what are the various applications of data science in various industries ranging from FSI to LSHC to Retail and many more. Also one will appreciate the job opportunities in the space of data science, data modeling, and data analysis. Finally understand the golden rule on how to become a successful data scientist, data modeler, data analyst, etc.

Learn about the Project Management Methodology, CRISP-DM, for handling Data Science projects and various concepts used in defining business problems and then performing data collection in line with business problems. Understand the importance of documenting the business objectives and business constraints so that the entire project is performed to solve business problems. Project charter overview will help participants understand the real-world documentation aspect as well.

Learn about data preparation and data cleansing in data science projects to ensure that appropriate data is provided to the next step. Outlier analysis or treatment, handling missing values using imputation, transformation, normalization/standardization, etc., will be explained in thorough detail. Understand the various moments of a business decision and graphical representation so that structured descriptive analytics or descriptive statistics is performed. This exploratory data analytics is the first step in data analytics to draw meaningful insights.

Learn about applying domain knowledge to the data so that more meaningful variables are derived. Understand two main modules of feature engineering including feature extraction and feature selection. Knowing how to shortlist the critical inputs from trivial many inputs is the key to ensuring the high performance of the machine learning models. Understand about extracting features from structured as well as unstructured data such as videos, images, audio, textual files, etc.

Understand one of the key inferential statistical techniques called Hypothesis testing. Understand various parametric hypothesis tests. Learn about the implementation of a Regression method based on the business problems to be solved. Understand about Linear Regression as well as Logistic Regression techniques used to handle continuous as well as discrete output prediction. Evaluation techniques by understanding the measure of Error (RMSE), problems while building a Regression Model like Collinearity, Heteroscedasticity, overfitting, and Underfitting are explained in detail.

Understand the advanced regression models such as Poisson Regression, Negative Binomial Regression, Zero-Inflated models, etc., used to predict the count output variables. Learn about the various scenarios which trigger the application of advanced regression techniques. Understanding and evaluating the models using appropriate performance and accuracy measures of regression are explained in detail.

Data Mining branch called unsupervised learning is extremely important in solving problems, which require the application of only unsupervised learning tasks and also used to support predictive modeling. Clustering or segmentation has two prime techniques – K-Means clustering, as well as Hierarchical clustering and both, are explained in finer detail. Alongside, participants will also learn about handling datasets with large variables using dimension reduction techniques such as Principal Component Analysis or PCA. Finally one will learn about Association rules also called affinity analysis or market basket analysis or relationship mining.

The majority of unstructured data is in textual format and analyzing such data requires special techniques such as text mining or also called as text analytics. Techniques such as DTM/TDM using Term Frequency, Inverse Document Frequency, etc. are explained in this module. One will also learn about generating a word cloud, performing sentiment analysis, etc. Also, advanced Natural Language Processing techniques such as LDA, topic mining, etc., are explained using practical use cases. Also, the learning includes extracting unstructured data from social media as well as varied websites.

A major branch of study in data science is Machine Learning also called Data Mining Supervised Learning or Predictive Modelling. One will learn about K Nearest Neighbors (KNN), Decision Tree (Boosting), Random Forest (Bagging), Stacking, Ensemble models and Naïve Bayes. One will learn about the various regularization techniques as well as understand how to evaluate for overfitting (variance) and underfitting (bias). All these are explained using industry relevant use cases and mini-projects.

Black box machine learning algorithms are extremely important in the field of machine learning. While there is no interpretation in the models, accuracy is unmatched in comparison to other shallow machine learning algorithms. Learn about the Perceptron algorithm and Multi-layered Perceptron algorithm or MLP. Understand about Kernel tricks used within Support Vector Machine algorithms. Understand about linearly separable boundaries as well as non-linear boundaries and now to solve these using Deep learning algorithms.

Understand the difference between cross-sectional data versus time series data. Search about the forecasting strategy employed in solving business problems. Understand various forecasting components such as Level, Trend, Seasonality & Noise. Also, learn about various error functions and which one is the best given a business scenario. Finally, build various forecasting models ranging from linear to exponential to additive seasonality to multiplicative seasonality.

Data visualization refers to the graphical representation of data and the results of data analysis, to derive meaningful insights for better decision making. In this module, you will learn everything that is to be known about Data Visualization. You will be able to answer questions like: What is Data Visualization?, Why do we need Data Visualization?, How to create views?., etc. You will be introduced to Data Visualization principles and best practices.

Tableau is a Data Visualization tool that has been in the Leader’s Quadrant in Gartner’s Magic Quadrant for many years. Tableau Desktop is the authoring and publishing tool that is used to create and share views. In this module, you will get familiar with Tableau User Interface, different components, and terminologies in Tableau. You will learn different methods available to save and share the work created in Tableau. You will understand Tableau Architecture.

Visual Analytics is the science of generating charts, graphs, views, and maps. This module will help you create basic charts in Tableau like Text Tables, Highlight Tables, Bar Charts, Side by Side Bars, Stacked Bars, Line Charts, Area Charts, Circle Views, and Pie Charts. You will also learn about Exploratory data analysis using Tableau.

Customer data these days need not come from a single system, it could be split across multiple databases in multiple systems (heterogeneous systems). This module will help you master the data connectivity options available in Tableau which includes Single Database Joins, Multiple Database Joins, Unions, Data Extraction and Data Blending.

The scope of Data Visualization is seamless. This module will introduce you to Advanced and Custom charts in Tableau like Scatter Plots, Histograms, Box Plots, Pareto Charts, Animation Charts, etc. You will also learn about Statistical Analysis features of Tableau like Trend Analysis and Distribution Analysis.

Applying filters limits the amount of data to include specific fields and records that meet certain criteria. You will learn everything that is to be known about Filters in Tableau here. Dimension Filters, Measure Filters, Date Filters applied at different levels like Tableau Data source level, Extract level and Worksheet level. You will learn about Tableau Quick Filters and Interactive Filters.

Creating groups will help you put similar members together. Set is a collection of well-defined objects which can be created based on computations. Calculations will help you manipulate the data and transform the data into the desired format. LOD (Level of Detail) expressions will help perform calculations at a desired level of detail, different from the default used in Tableau, to meet the requirements given.

Anything and everything there is to know about Geographic Maps in Tableau is included in this Module. A Geo-Spatial analysis is a great way to represent geographic data, as it adds rich context to the data being represented through Maps. Tableau has its own Geocoding data which helps it handle Geoinformation beautifully. You will learn how to add context to the data that might not be geographical by representing it on Custom Background Images.

The Dashboard is a real-time, easy to read user interface that presents data graphically. It is a collection of views, filters, parameters, and images that work closely and are arranged on a single canvas. The story is a narrated walkthrough or a guided analytics platform where the business users can see their data and get answers to all their questions. You will learn how to create Dashboards, how to make them Interactive, adding objects to the dashboard, designing Dashboards for different devices and creating stories.

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The Business Analytics market in Singapore is projected to reach $37.5 billion by 2022. Each year the data industry is said to contribute around S$1 billion to Singapore's economy.

(Source: https://www.edb.gov.sg)

Block Your Time

business analytics course

72 hours

Classroom Sessions

business analytics course

140 hours

Assignments &

business analytics course

140 hours

Live Projects

Who Should Sign Up?

  • Science, Math and Commerce graduates
  • IT professionals who want to specialise in digital tech
  • Professionals who want to move into Data Analytics
  • Professionals who want to add Data Analytics to current job skills
  • Academicians and researchers working in Data Analytics

Tools Covered

pythonr toolr studiotableau tool

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business analytics course modules

Professional Business Analytics Panel of Coaches


Bharani Kumar Depuru

  • Areas of expertise: Data Analytics, Process Management, Quality Management.
  • Over 13 years of professional experience
  • Trained over 2,500 professionals from eight countries
  • Corporate clients include Hewlett Packard Enterprise, Computer Science Corporation, Akamai, IBS Software, Litmus7, Personiv, Ebreeze, Alshaya, Synchrony Financials, Deloitte
  • Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, AgilePM (DSDM Atern)
  • Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business
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Bhargavi Kandukuri

  • Business Analytics, Quality Management, Data Visualization with Tableau, COBOL, CICS, DB2 and JCL.
  • Electronics and communications engineer with over 13 years of industry experience
  • Senior Tableau developer, with experience in analytics solutions development in domains such as retail, clinical and manufacturing
  • Trained over 750+ professionals across the globe in three years
  • Worked with Infosys Technologies, iGate, Patni Global Solutions as technology analyst
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Dr Nitin Mishra

  • Areas of expertise: Data Sciences, Machine Learning, Business Intelligence and Data Visualization.
  • Over 15 years of industry experience in Data Science and Business Intelligence.
  • Trained professionals from Fortune 500 companies and students from prestigious colleges.
  • Experienced in Cognos, Tableau, Big Data, NoSQL, NewSQL
  • Corporate clients include Time Inc., Hewlett Packard Enterprise, Dell, Metric Fox (Champions Group)
Read More >
business analytics certification course
business analytics certification course


Gain industry recognition for niche skills with the Professional Certification in Business Analytics. The certificate illustrates your ability to extract actionable insights from business data for improved decision - making.

**All certificate images are for illustrative purposes only. The actual certificate may be subject to change at the discretion of the Certification Body.

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360DigiTMG - Data Science, IR 4.0, AI, Machine Learning Training in Malaysia

Level 16, 1 Sentral, Jalan Stesen Sentral 5, Kuala Lumpur Sentral, 50470 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia

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