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Business Analytics Course - Business Analytics 360!

Power-packed Analytics course with job oriented certification in Business Analytics!

Undoubtedly one of the best Business Analytics courses in India and globally. Apt for candidates who want to start from basic Business Analytics tools like Excel, SQL, Tableau and graduate to advanced tools like R, Python for Data Science. AnalytixLabs pioneers in Business Analytics training and launched the first version of the Analytics Course in Delhi NCR in 2011!

However, learning tools without techniques is half the job done in today's Analytics world. This Business Analytics course in India encompasses basic statistical concepts to advanced analytics and predictive modeling techniques. You will learn all the skills required for a promising career as a Business Analyst and solve real-world business problems. This certificate course will help you to develop comprehensive data science skills pertaining to data visualization descriptive and predictive analytics for driving smart business decisions. 

Evolved from our most popular Business Analytics training, this is the best course in India curated for candidates who are looking for job oriented business analytics certification but have no prior knowledge of any business intelligence or data analytics tools. Most extensive business analytics training in Bangalore, Gurgaon, Noida and with the flexibility of also attending the live online training and e-learning mode as well. 

This analytics certification course is for all those aspirants who want to switch into the field of data science and begin their career as a business analyst. With the aim to provide all the aspirant's world-class Data Science and Business Analytics skills irrespective of their location. You have the flexibility to attend this Business Analytics online course through fully interactive live and e-learning mode as well with extensive doubts support.

Classroom and Bootcamp options are available for candidates looking for a Business Analytics course in Bangalore, Noida and Gurgaon. We are proud to share that this program is also rated among top certifications by prestigious publications like AIM & Higher Education review. 

Business Analytics training duration: 300 hours (At least 100 hours live training + 40 hours eLearning module + around 10 hrs of weekly self-study and practice)

Delivery Formats:

  • Business Analytics course in Delhi NCR (Gurgaon & Noida) and Bangalore is available in classroom and bootcamps batches as well. 

  • Fully interactive live online training (Global access)

  • Self-paced e-learning modules (Global access)

Useful Blogs:

  1. What is Business Analytics?

  2. Why is Business Analytics a good career option?

  3. The Complete Guide to Starting Your Career as a Business Analyst

Who Should do this course?

Beginner candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Business Management who are looking for Business Analytics training to start their career in the field of Analytics and Data Science.


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Course Duration 300 hours
Classes 40
Tools Excel, VBA, Tableau, SQL,R, Python
Learning Mode Live/Video Based
Next Batch04th October, 2020 (Online)

Course Outline

  • Introduction to Bridge Course & Analytics Software’s Basic Excel
  • Basic Programming Elements
  • Introduction to Basic Statistics
  • RDBMS & SQL (Basics)
  • Introduction to Analytics & Data Science
  • Introduction to Mathematical Foundations

  • Quick Recap of Basics of Excel
    • Data manipulation using functions
    • Descriptive functions
    • Logical functions: IF, and, or, not 
    • Date and Time functions
    • Text functions
    • Array functions
    • Use and application of lookup functions
    • Limitations of lookup functions
    • Using Index, Match, Offset, reverse vlookup
  • Data analysis and reporting
    • Data Analysis using Pivot Tables - use of row and column shelf, values and filters
    • Difference between data layering and cross tabulation, summary reports, advantages and limitations
    • Change aggregation types and summarization
    • Creating groups and bins in pivot data
    • Concept of calculated fields, usage and limitations
    • Changing report layouts - Outline, compact and tabular forms
    • Show and hide grand totals and subtotals
    • Creating summary reports using pivot tables
  • Data Visualization in Excel
    • Overview of chart types – column/bar charts, line/area , pie, doughnut charts, scatter plots
    • How to select right chart for your data
    • Creating and customizing advance charts - thermometer charts, waterfall charts, population pyramids
  • Overview of Dashboards
    • What is dashboard & Excel dashboard
    • Adding icons and images to dashboards
    • Making dashboards dynamic
  • Create dashboards in Excel - Using Pivot controls
    • Concept of pivot cache and its use in creating interactive dashboards in excel
    • Pivot table design elements - concept of slicers and timelines
    • Designing sample dashboard using Pivot Controls
    • Design principles for including charts in dashboards - do's and dont's
  • Business Dashboard Creation
    • Management Dashboard for Sales & Services
    • Best practices - Tips and Tricks to enhance dashboard designing

  • Quick Recap of RDBMS & Basic SQL
  • Data based objects creation (DDL Commands)
    • Creating databases and tables. Understanding data types
    • Inserting values into the table
    • Altering table properties
    • Introduction to Keys and constraints
    • Creating, Modifying & Deleting Tables
    • Create Table & Create Index statements
    • Drop & Truncate statements – Uses & Differences
    • DDL Statements with constraints
    • Import and Export wizard to get the data in SQL server from excel files or delimited files
  • Data manipulation (DML Commands)
    • Data Manipulation statements
    • Insert, Update & Delete statements
    • Select statement – Sub setting, Filters, Sorting, Removing Duplicates, grouping and aggregations etc
    • Operators, predicates and built in functions(Top, distinct, Limit)
    • Where, Group By, Order by & Having clauses
    • SQL Functions – Number, Text, Date, etc
    • SQL Keywords – Top, Distinct, Null, etc
    • SQL Operators -  Relational (single valued and multi valued), Logical (and, or, not), Use of wildcard operators and wildcard characters, etc
  • Accessing data from Multiple Tables using SELECT
    • Append and Joins
    • Union and Union All – Use & constraints
    • Intersect and Except statements
    • Table Joins - inner join, left join, right join, full join
    • Cross joins/cartisian products, self joins, natural joins etc
    • Inline views and sub-queries & it's types
    • Optimizing your work
    • Update operations with and without join
  • Advanced SQL
    • Creating table copy and database copy
    • Views
    • Transactions
    • Stored Procedures in SQL
    • Crud operations using stored procedures
    • Window functions in SQL
    • Miscellaneous Topics: Rollup and cube

  • Getting Started
    • What is Tableau?
    • Tableau product suite
    • How Does Tableau Work?
    • Tableau Architecture
    • Connecting to Data & data source concepts
    • Understanding the Tableau workspace
    • Dimensions and Measures
    • Data Types & Default Properties
    • Tour of Shelves & Marks Card
    • Using Show Me
    • Saving and Sharing your work-overview
  • Data handling & summaries
    • Date Aggregations and Date parts
    • Crosstab & Tabular charts
    • Totals & Subtotals
    • Bar Charts & Stacked Bars
    • Line Graphs with Date & Without Date
    • Tree maps/Scatter Plots
    • Individual Axes, Blended Axes, Dual Axes & Combination chart/Edit axis
    • Parts of Views
    • Sorting
    • Trend/Reference Lines/Forecasting
    • Filters/Context filters
  • Data handling & summaries
    • Sets (In/Out Sets/Combined Sets
    • Grouping/Bins/Histograms
    • Drilling up/down – drill through
    • Hierarchies
    • View data
    • Actions (across sheets)
  • Building Advanced Reports/ Maps
    • Explain latitude and longitude
    • Default location/Edit locations
    • Building geographical maps
    • Using Map layers
  • Calculated Fields
    • Aggregate vs. Disaggregate data
    • Explain - #Number of Rows
    • Basic Functions (String/Date/Numbers etc)
    • Usage of Logical conditions
  • Table calculations
    • Explain scope and direction
    • Percent of Total, Running / Cumulative calculations
    • Introduction to LOD (Level of Detail) Expressions
    • User applications of Table calculation
  • Parameters
    • Using Parameters in calculated fields
    • Bins/Reference Lines
    • Filters/Sets
    • Display Options (Dynamic Dimension/Measure Selection)
    • Create What-If/ Scenario analysis
  • Building Interactive Dashboards
    • Combining multiple visualizations into a dashboard (overview)
    • Making your worksheet interactive by using actions
    • Filter/URL/Highlight
    • Complete Interactive Dashboard for Sales & Services
  • Building Stories
    • Story Points
    • Options in Formatting your Visualization
    • Working with Labels and Annotations
    • Effective Use of Titles and Captions
  • Working with Data
    • Multiple Table Join
    • Data Blending
    • Difference between joining and blending data, and when we should do each
    • Toggle between to Direct Connection and Extracts
  • Sharing work with others
    • Sharing Workbooks
    • Publish to Reader/PDF
    • Publish to Tableau Server and sharing on the web

  • Introduction R/R-Studio - GUI
  • Concept of Packages - Useful Packages (Base & Other packages)
  • Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists)
  • Importing Data from various sources
  • Exporting Data to various formats
  • Viewing Data (Viewing partial data and full data)
  • Variable & Value Labels – Date Values

  • Creating New Variables (calculations & Binning)
  • Dummy variable creation
  • Applying transformations
  • Handling duplicates/missing's
  • Sorting and Filtering
  • Sub setting (Rows/Columns)
  • Appending (Row/column appending)
  • Merging/Joining (Left,right,inner,full,outer)
  • Data type conversions
  • Renaming
  • Formatting
  • Reshaping data
  • Sampling
  • Operators
  • Control Structures (if, if else)
  • Loops (Conditional, iterative loops)
  • Apply functions
  • Arrays
  • R Built-in Functions
  • Text, Numeric, Date, utility
  • R User Defined Functions
  • Aggregation/Summarization

  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Uni-variate Analysis (Distribution of data)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships)

  • R and Relational Databases
  • Connecting to Relational Databases using RJDBC and RODBC
  • Database Design and Querying Data
  • Modifying Data and Using Stored Procedures
  • In-Database Analytics with R

  • Basic Visualization Tools
    • Bar Charts/Histograms/Pie Charts
    • Scatter Plots
    • Line Plots and Regression
  • Specialized Visualization Tools
    • Word Clouds/ Radar Charts
    • Waffle Charts/ Box Plots
  • How to create Maps
    • Creating Maps in R
  • How to build interactive web pages
    • Introduction to Shiny
    • Creating and Customizing Shiny Apps
    • Additional Shiny Features

  • Overview of Python- Starting with Python
  • Why Python for data science?
  • Anaconda vs. python
  • Introduction to installation of Python
  • Introduction to Python IDE's(Jupyter,/Ipython)
  • Concept of Packages - Important packages
    • NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels – Date & Time Values
  • Basic Operations – Mathematical/string/date
  • Control flow & conditional statements
  • Debugging & Code profiling
  • Python Built-in Functions (Text, numeric, date, utility functions)
  • User defined functions – Lambda functions
  • Concept of apply functions
  • Python – Objects – OOPs concepts
  • How to create & call class and modules?

  • What is NumPy?
  • Overview of functions & methods in NumPy
  • Data structures in NumPy
  • Creating arrays and initializing
  • Reading arrays from files
  • Special initializing functions
  • Slicing and indexing
  • Reshaping arrays
  • Combining arrays
  • NumPy Maths

  • What is pandas, its functions & methods
  • Pandas Data Structures (Series & Data Frames)
  • Creating Data Structures (Data import – reading into pandas)

  • Understand the data
  • Sub Setting / Filtering / Slicing Data
    • Using [] brackets
    • Using indexing or referring with column names/rows
    • Using functions
    • Dropping rows & columns
  • Mutation of table (Adding/deleting columns)
  • Binning data (Binning numerical variables in to categorical variables)
  • Renaming columns or rows
  • Sorting (by data/values, index)  -By one column or multiple columns  - Ascending or Descending
  • Type conversions
  • Setting index
  • Handling duplicates /missing/Outliers
  • Creating dummies from categorical data (using get_dummies())
  • Applying functions to all the variables in a data frame (broadcasting)
  • Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc.)

  • Exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Uni-variate Analysis (Distribution of data & Graphical Analysis)
  • Bi-Variate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

  • Introduction to Data Visualization
  • Introduction to Matplotlib
  • Basic Plotting with Matplotlib
  • Line Plots

  • Basic Visualization Tools
    • Area Plots
    • Histograms
    • Bar Charts
    • Pie Charts
    • Box Plots
    • Scatter Plots
    • Bubble Plots
  • Advanced Visualization Tools
    • Waffle Charts
    • Word Clouds
    • Seaborn and Regression Plots

  • Introduction to Folium
  • Maps with Markers
  • Choropleth Maps

  • Descriptive vs. Inferential Statistics
  • What is probability distribution?
  • Important distributions (discrete & continuous distributions)
  • Deep dive of normal distributions and properties
  • Concept of sampling & types of sampling
  • Concept of standard error and central limit theorem
  • Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square

  • Concept of model in analytics and how it is used?
  • Common terminology used in modeling process
  • Types of Business problems - Mapping of Algorithms
  • Different Phases of Predictive Modeling
  • Data Exploration for modeling
  • Exploring the data and identifying any problems with the data (Data Audit Report)
  • Identify missing data
  • Identify outliers data
  • Visualize the data trends and patterns

  • Introduction - Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
  • Assess the overall effectiveness of the model
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
  • Interpretation of Results - Business Validation -
  • Implementation on new data

  • Introduction - Applications
  • Linear Vs. Logistic Regression Vs. GLM
  • Building Logistic Regression Model (Binary Logistic)
  • Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
  • Interpretation of Results - Business Validation -
  • Implementation on new data


With rise of digitization, organizations are flooded with ever increasing flow of information. A business analyst or a data analyst uses a combination of skills to derive actionable insights which can be vital for making smart decisions. And in this scenario, businesses rely upon Data Analysis skills more than ever before to stay competitive. This is why business analytics demand has surged globally, creating a huge demand for business analysts. Analytics Industry in India is expected to grow 7 times in next 7 years (from 2019 to 2025), as per the finding of ‘Analytics Industry Study 2018’ conducted by AIM & AnalytixLabs. So, like all previous years the demand of Business Analyst will stay strong with promising opportunities across various domains. 

If we go with trends suggest by LinkedIn and Glassdoor some of the top organizations hiring for business analytics in India are Tata Consultancy Services (TCS), Accenture, Cognizant, Deloitte, American Express, Evalueserve, Ernst & Young, Amazon and Flipkart.

In terms of salary packages, Business Analysts are some of the highest paid professionals as compared to their peers. As per Glassdoor, the average beginner level salary for a business analyst in India is INR 6,44,857 per annum. However, post some experience analysts earn up to INR 12,00,000 per year, especially those who are adept at the latest techniques and tools. With higher experience and well established domain expertise, salaries can range anywhere between INR 25,00,000 to INR 40,00,000 annually.


No, coding skills are not mandatory for Business Analytics. Most of the analysis can be performed using the tools like Excel, Tableau, Power BI without coding, whereas while working on SQL one needs basic understanding of SQL commands. However, to leverage advanced analytics it is helpful to have working knowledge of specialized tools, like R and Python. But even in this case with right coaching, candidates with no-prior technical background can work effectively on these tools.

For most of the aspirants a professional certification can effectively help in up-skilling and acquiring job relevant skills in usually around 6 months (with part-time training).  Apart from this there are several post-graduate programs available, with duration of 1 or 2 years and average investment ranging from 6-12 lakhs. In short to medium term, part-time certification offer better ROI, but this may vary based on candidate’s overall profile and career stage. 

In best case scenario, an undergraduate degree in quantitative or technical stream, possible followed by an MBA and then a certified course in BA would ensure your access to the upper echelons of BA jobs. Going with an institute of certain repute is one of the best options, but candidates should consider some key aspects:

• Domain expertise: Given the popularity, plethora of institutes have jumped into this foray. But only handful of institutes bring domain expertise, deep intellectual property and significant experience to deliver industry relevant program. 

• Industry recognition: A top institute would have strong industry presence in the particular domain, with prestigious clientele. This certainly adds meaningful value to the certificate and industry recognition.

• Mentors’ experience: It is utmost important that faculty members/ mentors have vast industry experience and also strong hands-on themselves.

• Industry relevant curriculum: Curriculum should cover latest tools and techniques with enough practical components. Frequent curriculum upgradation is key in such a fast evolving field.

• Projects and case studies: Projects and assignments should have extensive coverage of real world problems from key industries, so that it inculcates confidence in candidates to face interviews and on job challenges.

• Post class support: It is imperative that candidates have ample support available outside class hours for doubts and problems they face while self-studying and working on projects. Institutes with only part time instructors usually lack in this important aspect. 


Among numerous institutes, AnalytixLabs is certainly one of the finest and well renowned option. AnalytixLabs has been a pioneer in Data Science learning since 2011 and is constantly rated among India’s top institute with repeated clientele of several prestigious MNCs. Considering the value for money, industry relevant programs and stellar track record no one can overlook the value proposition AnalytixLabs offers when it comes to Business Analytics certification. 

Following are the 2 most popular courses which caters to business analytics career track:

• Analytics Edge: Main focus of this course is to help beginners build basic to intermediate level analytics skills for descriptive and statistical analysis. This course covers most widely used tools for BI/ descriptive analytics, like MS-Excel, SQL, Tableau and VBA. A major focus on this program is data science and predictive modelling skills using R.

• Business Analytics 360: One of our most coveted course with dual specialization, with combination of Analytics Edge + Data Science & Machine Learning using Python. This is a very extensive 7 month program that covers basis to advanced analytics including Data Science and Machine Learning using Python & R. 


All of our training courses are available in following 3 formats:

• Bootcamp/ Classroom contact: Offline classroom sessions with in-person training and mentor interactions and in-class practice.

• Interactive live online: Interactive live online format, which offers real-time interactive experience through in-built VoIP and chat with convenience of attending the class from anywhere globally.  

• Self-paced e-learning: Set of pre-recorded video lectures, with flexibility of completing course at your own pace. Candidates can periodically schedule doubt sessions in-person or remotely as per their convenience too. 

For all the three formats candidates get an access to online learning portal, to access study material and class recordings for future reference. 


Analytics job roles usually demand combination of skills and same is true when it comes to tools. To deliver a project effectively, one may need combination of different tools. We can categorize the most popular tools as following:

MIS Reporting and Descriptive Analytics – MS-Excel, Tableau, QlikView, Power BI and SQL

Advanced Analytics/ Statistical Modelling/ Machine Learning – SAS, R and Python

Over last few years new tools like Tableau, QlikView, Power BI with particular focus on data visualization have become very prominent, whereas incumbents like SAS have lost ground to open source tools like R & Python when it comes to advanced analytics.

A comprehensive Business Analytics certification program like Business Analytics 360, covers following keys aspects as part of the curriculum:

Analytics Methodologies: Develop conceptual understanding and learn practical application of various analytical methods, interpretation of different steps involved in end-to-end analytics projects, such as data handling, data extraction, descriptive & predictive analytics using statistical modelling and machine learning techniques. 

Analytics Tools: Build hands-on skills on most widely used tools like Excel, SQL, Tableau/ Power BI, R and Python to enable candidates to work effectively on real world problems and be job ready. 

Business problem solving: No tools and techniques are of use unless they don’t provide any real business value. An accomplished analyst must know how to define, structure and approach a given problem. With help of multiple case studies and projects spread across the course curriculum, candidates learn to effectively leverage combination of different tools and techniques to answer critical business questions and solve different type of problems. 

Some of the common designations offered to business analytics professional are:- Business Analyst, Data Analyst, Analyst, Analytics consultant, Analytics specialist. This depends on the organizational structure and candidates experience and education background. However, in the present world corporate structures are dynamic and designations can vary from organization to organization. One should mainly understand the nature of work and what type of responsibilities a given job role entails.

For a Business Analytics professional work primarily revolves around data, which could be gathered from varied resources, like market research, operations, internal CRM, ERP and BI systems. Business Analytics comprises of deciphering the past trends and/ or make predictions with combination of different analytics tools and methodologies. This ensures the right and most effective action is undertaken. The execution of these occurs by combining statistical software findings with business know-how while trying to reach ideal business solutions.  These solutions are shared with stakeholders who utilise this information to make operational and strategic decisions. A skilled Business Analytics professional gauges the quality of data, understand business objective, utilises relevant techniques to generate superior data-driven business decisions.

An experienced analytics professional is also expected to develop domain expertise over a period of time with deep business understanding of at least one industry, for example Retail, FMCG, e-Commerce, Insurance, Banking, Finance, Travel and Logistics etc.

Business Analytics provides a golden opportunity for young professionals to switch to the strategic side of the business. This is certainly a lucrative and sought after job, which involves a lot of hard work to internal and external client expectations.

Minimum qualification to pursue Business Analyst career track is graduation and for Analytics it helps to have graduate degree in quantitative streams, like engineering, eaths, statistics, econometrics, operation research. 

After graduation, one has choice of pursuing career in Business Analytics through a certification course. However, for some high paying jobs, which involves consulting and advisory roles, companies prefer candidates with Masters in Management (MBA) or other quantitative fields . Although this degree doesn’t essentially gets you in the field of Business Analytics, but it propels the career faster from base position with higher salary. 

Although it not a prerequisite, but good communication skill is important for a Business Analytics professional. As part of the work, a Business Analyst needs to understand the problem and ask the right questions to deliver the project effectively. As part of day to day responsibilities, Business analysts interacts with the client often, hence, it is important put forward different business and technical aspects articulately.

I joined the Base and Advanced SAS course with a duration of 1.5 months. The course was quite comprehensive, and being from a non analytics background, I was still able to grasp the concepts easily. The teaching manner is quite interactive, and lucid. The instructors are very approachable, and ready to give individual assistance when required. I also found the class study material very helpful, and it gave me confidence to approach the problems independently, thanks to the assignments and case studies. Besides, the structured approach provided towards SAS Global certification preparation is quite good. Definitely a fruitful course!

Shubhi Agarwal
(Deputy Manager (Analytics) at Bajaj Allianz General Insurance)

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