Business Analytics 360 Course

A power-packed Business Analytics course with job-oriented global certification!

The Business Analytics Course is a full package with global certification and is targeted toward preparing you for the IT job market.

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  • 360 Hours
  • 33 Classes
  • Home
  • Courses details
  • business-analytics
  • Upcoming batches
    • 02Jul

      Bangalore

    • 09Jul

      Gurgaon

    Learning Modes
    class

    Fully Interactive Online/
    Offline Classroom

    INR 45000*/-
    play

    Self-Paced Blended
    eLearning

    INR 32000*/-
    Courses
    Course
    ₹ 16000*/-

    Data Visualization & Analytics

    • 120 hours
    • 12 Classes
    • Classroom
    • Online
    • eLearning
    Courses
    Course
    ₹ 20000*/-

    Data Science With Python

    • 130 hours
    • 20 Classes
    • Classroom
    • Online
    • eLearning
    Courses
    Course
    ₹ 4000*/-

    Data Science using R

    • 124 hours
    • 18 Classes
    • Classroom
    • Online
    • eLearning
    date

    No of classes X Hours

    33 x 3 = 99 hrs
    + 62 hrs e-learning

    study

    Self Study Hours

    160 (8-10 hrs/ week)

    15

    Assignments & Projects
    recruitmen

    Placement Readiness Program

    25 Hours
    (towards course completion)

    Understand the domain of business analyst with the Best business analyst course by Ai Campus. The curriculum you get here is made by the top educators in the industry, keeping in mind what the current demands of the IT sector are. Learners get equipped with sought-after skills in data mining, analytics reporting, and predictive analytics.

    Acquire a thorough understanding of time series analysis, econometrics, and forecasting to predict future results based on past trends. Beyond the fundamentals, our Business Analytics 360 Courses provide practical instruction using commonly used tools like Excel, SQL, and PowerBI. With a solid foundation in R and Python, you can dive into advanced analytics and make sure you know all of the newest tools and methods available.

    Impact of Learning Business Analytics

    • Transform raw data into actionable insights, playing a pivotal role in shaping overall business decision-making.
    • Gain expertise with a range of analytical tools so that you can use and explore them with ease.
    • Gain a deep understanding of primary and secondary data derived from user activities, contributing to the refinement of business processes for enhanced productivity. 
    • Take a central role in leading businesses by staying abreast of market trends and employing the latest analytic tools, driving improvements in efficiency and increasing traffic. 
    • Examine the wide range of applications and industries that business analytics may be used in, offering prospects for expansion and use. 

    Learning Objectives of Business Analytics Course

    Enroll in our Business Analytics Course with Placement assistance and acquire the skills to efficiently extract valuable insights from vast data sets within minutes. With the help of this extensive program, you will acquire the knowledge and skills necessary to successfully negotiate the challenges of data analytics and make decisions that are well-founded in statistical principles. The learning objectives of this course is very clear, have a look:

    • Gain expertise in drawing business conclusions from data.
    • Bridge the gap by providing analytical solutions to real business problems.Demonstrate your ability to create data models and use a range of data analysis tools.
    • Empower yourself to make informed, data-driven decisions that contribute to smart business strategies.
    • Develop your communication abilities to successfully communicate analysis results and technical information to audiences that are both technical and non-technical.
    • Gain the expertise to assess the precise operational, financial, and ethical impacts of data-driven solutions in both structured and unstructured environments.

    Different types of Analytics in our Business Analytics Course

    Business analytics creates future projections by symbiotically integrating past and present data, giving organizations a roadmap based on data. During the course at Ai Campus, students gain proficiency in the four essential categories of analytics:

    • Descriptive analytics : Mastery in describing a business's past or present status.
    • Diagnostic analytics : The ability to identify, evaluate, and examine the variables influencing current or prior circumstances.
    • Predictive analytics Acquiring the skills to make near-accurate predictions for businesses utilizing diverse analytical tools.
    • Prescriptive analytics : The ability to suggest particular courses of action that steer a company in the best path.

    Together, these four components make up the full business analytics lifecycle, which includes data collection, analysis, and prediction and ultimately leads to improved decision-making procedures.

    Flexible Learning Methods

    At Ai Campus, we offer multiple learning modes for learners keeping in mind that each student is unique and connects to a different kind of mode based on the time they can spare and what suits their schedule. Select the one that fits you the best.

    • Self-paced Blended eLearning: Take use of your learning management system's on-demand e-learning video sessions. Enjoy the flexibility of regular weekend interactive live case study and doubt sessions, accompanied by additional support through mail, online platforms, or in-person assistance

    • Classroom & Bootcamp: Experience hands-on learning with in-person mentorship. Select from weekend or weekday classroom boot camps to receive hands-on, interactive instruction.

    • Interactive live online: Learn in our live online sessions with our faculty of the IT industry. Participate in fully interactive sessions with real-time conversations and question-and-answer exchanges from any location at your convenience.

    Scope of Business Analytics Course

    When learners decide on pursuing a specific course, Business Analytics Course in this case they want to know the scope of it. This section of the website will help you understand the job roles you can take up after this course and the scope of business analytics.

    Talking about statistics: the U.S. Bureau of Labor Statistics (BLS), has reported that the demand for business analysts is expected to surge by 25% by 2030. Other job roles that are related to Business analytics are also going to expand. Market research analysts, for example, are expected to have a 22% increase in employment by 2030. In contrast, growth rates for jobs like management analysts and budget analysts are expected to be 14% and 5%, respectively.

    Business Analytics Career Path:

    Role

    Years of Experience

    Salary Range in India [INR]

    Business / Data Analyst

    2-4yrs

    6-12 LPA

    Senior Business Analyst

    2-4yrs

    9-18 LPA

    Team Lead/ Manager

    5-7yrs

    4-18 LPA

    Principal Business Analyst

    8+ yrs

    16-29 LPA

    Senior Principal BA

    8+ yrs

    18-29 LPA

    Director, BA

    10+ yrs

    19-29 LPA

    Senior Director, BA

    12+ yrs

    25-31 LPA

    VP, Business Analytics

    12-14+ yrs

    30-41 LPA

    Senior VP, BA

    15+ yrs

    40-45 LPA

     

    Let us take a look at the curriculum offered by Ai Campus for the Business Analytics Certification Course. Students will study the modules and content given below:

    • Data Visualization and Analytics : Learn how to effectively convey data insights using graphics. This module focuses on utilizing common visuals like charts, infographics, plots, and animation to convey complex data relationships. Explore the possibilities of PowerBI/Tableau (optional) to produce eye-catching visualizations that make complex data insights simple.
    • R for Data Science (Optional eLearning): This optional eLearning course will teach you how to utilize the R programming language for business analytics. Learn the value of R for managing, storing, and analyzing data while taking advantage of its rich graphical features and statistical analytic environment.
    • Data Analytics with Python: Python is a popular and easily readable language that is widely used in the field of data analytics. This module equips you with the skills to harness Python's capabilities for effective data analytics and reporting.
    • Machine learning and Predictive Modeling: From analyzing patterns to predicting accurate outcomes, this module guides you through the mathematical processes involved. Grasp the fundamentals of machine learning libraries and techniques to improve your modeling and data analytics skills with Python.
    • Placement Readiness Program Overview: Ai Campus offers you placement assistance like never before.

    • Base Module
        • Introduction to bridge course & analytics software.
        • Learn the basics of Excel.
        • RDBMS & SQL (Basics).
        • Introduction to analytics & data science.
        • Get a better understanding of programming elements.
        • Introduction to basic statistics.
        • Basics of mathematics.
    • Excel for Data Analytics & Visualization
        • A brief review of Excel basics
        • Data manipulation with functions
        • Data analysis and reporting
        • Data visualization in Excel
        • Overview of Dashboards
        • Excel dashboard creation using pivot controls
        • Development of Business Dashboards
    • Analyzing data with SQL
        • SQL & RDBMS foundations.
        • A quick rundown of basic SQL and RDBMS.
        • Data-based object creation using DDL commands.
        • Data manipulation using DML commands.
        • Accessing data from multiple tables with SELECT.
        • Advanced SQL techniques.
        • Using real-world examples in a business case study.
    • PowerBI for Data Analytics & Visualization
        • Initial setup.
        • Data handling and summaries.
        • Creating advanced reports/maps.
        • Utilizing calculated fields.
        • Putting table calculations into practice.
        • Incorporating parameters.
        • Creating interactive dashboard designs.
        • Crafting stories with data.
        • Collaborative data work.
        • Collaborating with others on projects.
    • VBA for Data Analytics (Optional e-learning)
          • Introduction to VBA.
          • VBA's synergy with Excel.
          • Learn the programming language’s key components.
          • Frequently used code snippets.
          • Programming constructs in VBA.
          • Functions and procedures – modularizing programs.
          • Objects and memory management in VBA.
          • Error handling.
          • Controlling code accessibility with access specifiers.
          • Code Reusability: Including components and references.
          • Interaction with users.
    • Data Importing/Exporting with R
        • Introduction to R/R-Studio GUI.
        • Concept of packages – utilizing base and other packages.
        • Data structure and types (lists, data frames, matrices, factors, and vectors).
        • Importing data from diverse sources.
        • Data exportation to various formats.
        • Viewing data: partial and complete views.
        • Variable and value labels – date values.
    • Dimensionality reduction & collaborative filtering
        • Feature extraction & selection in dimensionality reduction.
        • Challenges of collaborative filtering.
    • Data Manipulation
        • Creation of a new variable.
        • Dummy variable generation.
        • Application of transformations.
        • Managing missing/duplicate values.
        • Sorting and filtering.
        • Subsetting and appending (rows/columns).
        • Merging/joining.
        • Data type conversions.
        • Renaming.
        • Formatting.
        • Reshaping data.
        • Sampling.
        • Operators.
        • Control structures (if, if else).
        • Loops (conditional, iterative).
        • Apply functions.
        • Arrays.
        • R Built-in functions.
        • R user-defined functions.
        • Aggregation/summarization.
    • Data Analysis
        • Introduction to Exploratory Data Analysis
        • Summarization, frequency tables, and descriptive statistics
        • Uni-variate analysis (Data distribution)
        • Bivariate analysis (Cross tabs, distributions, & relationships)
    • R Integration with Databases
        • Incorporating R with relational databases.
        • Establishing connections to relational databases via RJDBC and RODBC.
        • Designing and querying databases.
        • Data modification and employing stored procedures.
        • Using R for in-database analytics.
    • Data Visualization with R
        • Foundation visualization tools.
        • Special visualization tools.
        • Creating maps in R.
        • Building interactive web pages.
    • Statistics Fundamentals
        • Basics and foundation of statistics.
        • Inferential statistics.
        • Statistical methods.
    • Linear Regression
        • Introduction and applications.
        • Assumptions of linear regression.
        • Constructing linear regression models.
        • Understanding common measures like global hypothesis, variable significance, etc.
        • Evaluating the model's overall effectiveness.
        • Model validation.
        • Common business outputs like drivers, error distribution histogram, etc.
        • Interpretation of results, business validation, and implementation of new data.
    • Supervised Learning (I)
        • K-nearest neighbors
        • Decision trees
        • Random forests
        • Evaluating random forests' reliability
        • Pros & cons of decision trees
    • Machine learning overview
        • Machine learning languages, types, and examples
        • Distinguishing machine learning from statistical modeling
        • Supervised versus unsupervised
        • Supervised learning classification
        • Exploration of unsupervised learning
    • Supervised Learning Advanced (II)
        • Algorithms for regressions
        • Model evaluation techniques
        • Overfitting & underfitting in model evaluation
        • Understanding various evaluation models
    • Unsupervised Learning
        • K-Means Clustering: Benefits and Drawbacks
        • Hierarchical Clustering: Benefits and Drawbacks
        • Distances Between Clusters - Single Linkage and Algorithms for Hierarchy Clustering
        • Density-Based Clustering
    • Essentials of Python Core
    • Operations with Numerical Python
        • Understanding NumPy
        • Overview of functions & methods in NumPy
        • Data structures in NumPy
        • Array creation and initialization
        • Reading arrays from files
        • Special initializing functions
        • Indexing and slicing
        • Reshaping arrays
        • Combining Arrays
        • Math Operations in NumPy
    • Pandas Overview
        • Comprehending Pandas: Its features and operations
        • Pandas data structures
        • Building data structures
    • Using Python for data cleansing
        • Understanding data
        • Subsetting, filtering, and slicing data
        • Table mutation
        • Binning numerical variables into categorical variables
        • Renaming rows and columns
        • Sorting
        • Type conversions
        • Setting index
        • Handling duplicates, missing data, and outliers
        • Creating dummies from categorical data
        • Applying functions to all variables in a data frame
        • Making use of data manipulation tools
    • Python for Data Analysis
        • Exploratory data analysis
        • Descriptive statistics, frequency tables, and summarization
        • Uni-variate analysis
        • Bi-Variate analysis
    • Operations with Numerical Python
        • Understanding NumPy
        • Overview of functions & methods in NumPy
        • Data structures in NumPy
        • Array creation and initialization
        • Reading arrays from files
        • Special initializing functions
        • Indexing and slicing
        • Reshaping arrays
        • Combining Arrays
        • Math Operations in NumPy
    • Using Python for Data Visualization
        • Getting introduced to data visualization
        • Overview of Matplotlib
        • Fundamentals of plotting with Matplotlib
        • Line plots
    • Fundamental data visualization tools
        • Histograms
        • Area Plots
        • Pie Charts
        • Bar Charts
        • Bubble Plots
        • Box Plots
        • Scatter Plots
    • Advanced-level data visualization tools
        • Regression Plots
        • Waffle Charts
        • Seaborn Plots
        • Word Clouds
    • Geospatial Data Visualization Highlights
        • Introduction to Folium
        • Crafting maps with markers
        • Choropleth maps
    • Overview of Statistical Methods and Hypothesis Testing
        • Descriptive vs. inferential statistics
        • Probability distribution explained
        • Significant distributions
        • In-depth exploration of normal distributions and properties
        • Types & concepts of sampling
        • Comprehending the central limit theorem and standard error
        • Delving into hypothesis testing
        • Exploring hypothesis testing statistical techniques - Z/t-tests, ANOVA, Correlation, and Chi-Square
    • Introductory module
    • Mastering linear regression problems
        • Introduction and applications
        • Premises of the linear regression
        • Constructing a linear regression model
        • Grasping standard metrics
        • Assessing the overall effectiveness of the model
        • Model validation
        • Standard business outputs
        • Interpreting results, business validation, and implementing new data
    • Understanding domain
        • Overview of data sources for different industries
        • Analytics project management introduction
        • Deep dive risk analytics understanding in marketing analytics
        • Risk analytics exploration
        • Operation analytics unveiled
        • Digital analytics briefing
        • Social network analytics insights
        • Sectors Explored: Banking & Financial Services, Insurance, Retail & ECommerce, Pharma & Health Care, Telecom & Network
    • Placement Readiness Program Overview

    Our BA Analyst Course is ideal for prospective students with a variety of different backgrounds, such as Engineering, Finance, Mathematics, and Business Management. This online course is designed to meet the needs of people who want to learn new skills, improve their current ones, transition to a different domain, or upskill.

    However, learners who do not belong to the streams that are mentioned above can pursue a career in data analytics as well. The course remains accessible to individuals from various backgrounds, demonstrating that with dedication and determination, anyone can thrive in the field of business analytics.

      Every learner joins IT courses with a goal in their mind. This section will help you understand what the future holds after completion of your Business Analytics Certification Course. Below are the job opportunities you can venture into:

      • Business Analyst
      • Analytics Consultant
      • Statistical Analyst
      • Data Analyst
      • Data Scientist
      • Data Visualization Analyst
      • Data Science Consultant
      • MIS Analyst

      To be in the job roles mentioned above there are multiple skills that students pick up in this course. Have a look at them:

      • Data Visualization
      • MIS Reporting Analytics
      • Analytical Thinking
      • Data Blending & Manipulation
      • Problem-solving Skills
      • Reporting Analytics
      • Statistical Analysis & Modelling
      • Predictive Modeling
      • R Programming
      • Python for Data Analysis
      • Data Mining & Analysis
      • Detail-oriented
      • Visual Thinking

      Ai Campus focuses on teaching through practical work rather than just theoretical classes. Learners are engaged in multiple assignments and capstone projects that teach business analytics more than books can. Capstone projects add a heavy weight to the CV as well and we offer in-built projects, hence at the time of placement learners can show they have more experience than just open-source projects.

      Assignments
      • Assignments to practice Python
      • Case studies where learners use R
      • Exercises involving analysis tools like Excel, SQL & PowerBI
      • Quick exercises using Pandas and NumPy
      • Case studies on Pandas
      • Exercises for statistical analysis
      Projects
      • Sales and marketing data analysis
      • Analysis and visualization of pricing data for consumer electronics
      • Sports equipment retail data analysis and visualization
      • Telecom churn prediction
      • Credit card spending prediction
      • Sports event analysis and reporting
      • Peer group lending analysis & prediction
      • Airlines data analysis and reporting
      • Peer group lending analysis & prediction
      live
      Interactive Live online

      Online mode is also available for learners. Here interactive live online sessions happen. Join our expert-led live online sessions from any location. Engage in fully interactive discussions with real-time

      bootcamp
      Self-paced eLearning

      In this mode, students can use the access they have to our learning management system and study through it. This mode allows them to access and ask for on-demand video lectures. Get comprehensive doubts to support via mail, online, or in person.

      play
      Classroom & Bootcamp

      In this learning mode, learners get to meet the mentors and have in-person study sessions. This mode is available on weekdays and weekends. Learners who wish to attend in-class sessions should go for it.

      • Learners get the best global certification with our Business Analytics Certification Course. As one of India's top institutes for IT courses like business analytics, we take great pride in upholding the validity of our certification procedure and the weightage it holds.
      • The certification is obtained by successfully submitting and objectively assessing the required project work that is an essential part of the course. There is no pass/fail grading for these assignments and projects. In order to provide an impartial evaluation process, trainees are encouraged to ask for help when they need it for progress. Assignments and projects aren't graded on a pass/fail basis aside from multiple-choice questions. Plagiarism is not permitted during the evaluation process, nevertheless
      • Our main goal is to provide the learners with a strong base before they enter the job market for analytics, which is why we focus on multiple practice exercises. The certification Ai Campus provides is globally recognized as it is from IABAC.
      business-analytics- 360

      Learners can also select the learning mode as per the charges of that particular mode. Below each mode is explained with their installments and other details.

      live-streaming Weekend Batches

      ₹ 45000 + taxes

      Key Features:

      • Join our expert-led live online sessions, accessible from any location. Engage in fully interactive sessions for real-time discussions and questions/answers.
      • A great return on investment and a reasonably priced way to learn useful skills.
      • Access student loans with easy EMIs
      • Stay updated with the latest industry-relevant content, covering 360 hours of learning.
      • Get thorough assistance with your doubts via mail, internet, or in person.

      play-1 Self-paced Blended

      ₹ 32000 + taxes

      key features:

      • Using your learning management system, access e-learning video sessions on demand.
      • Student loans with easy EMIs are available.
      • An affordable option to master essential skills with a high return on investment
      • Learn from the most recent, industry-relevant content throughout 360 hours of instruction.
      • Receive extensive doubts support via mail/online/in-person

      social Weekday Bootcamp

      ₹ 48000 + taxes

      Key Features:

      • Flexibility in payments, with three installment options.
      • EMIs for student loans are simple to manage.
      • An affordable opportunity to master essential skills with a high return on investment.
      • Get access to the most recent, industry-relevant content—360 hours of learning.
      • Receive comprehensive doubts support via mail/online/in-person.
      profile

      Profile Building

      Get individualized help from a group of skilled experts to create an effective resume and online profiles that are suited to your experience and educational background.

      interview

      Interview Preparation

      Take advantage of one-on-one sessions for interview preparation, which might include practice interviews if needed.

      jobpreperation

      Job Referrals

      Access diverse job opportunities through requirements from organizations, clients, HR consultants, and a vast network of Ai Campus alumni working across various companies.

      support

      Continuous Support

      Enjoy ongoing support for as long as needed. Many of our students take advantage of the abilities they have learned in the course to receive numerous interview calls and appealing career alternatives.

      • 02Jul

        Bangalore

      • 09Jul

        Gurgaon

      Want to see how it works?
      demo
      FAQ

      Frequently asked question

      • What can taking a course in data analytics accomplish for my career?

        You can start along the road to becoming a successful data analyst across several disciplines by enrolling in a well-designed data analytics course. Over the past ten years, the demand for data specialists has increased tremendously across industries, and getting training in data analytics is one of the greatest ways to keep up.

        Be it analyzing a sporting event or detecting early-stage cancer; a data analyst has a role in everything. The scope of data analytics as a discipline has been growing since its inception. Every task, every process, every little gadget connected to the internet produces data that can be turned into vital information. The newfound utility of data has increased the importance of analytics and data science in India by manifolds. Therefore, a reputed data analytics certification course in India can boost your career to get a flying start in the industry.

      • What distinguishes a data science course from an analytics course?

        A subset of data science called data analytics has a much narrower field of study. You can learn how to handle data collecting, cleansing, and organisation as well as descriptive and predictive data analysis through a data analytics certification training programme.

        Finding patterns in data, posing pertinent questions, and setting up machine learning algorithms to provide answers are the main objectives of a data science training course. Depending on your experience, knowledge, and, of course, the mentors at Ai campus' expert advice, you can select a course.

        Data science and data analytics careers can take many different forms. For instance, the business analytics 360 course will assist you in starting from scratch to become a business analyst. You can increase your understanding of algorithms and predictive modelling with the aid of a machine learning course. For each of them, Ai campus offers the top data analytics education in Bangalore, Noida, and Delhi.

      • What does the professional transition look like after data science training?

        Since data science is a broad field, it is challenging to provide a definitive response. Nevertheless, we may consider some real-world situations that our alumni have encountered. To confirm their claims, look them up on LinkedIn.

        After receiving training from Ai Campus, we transformed from a leader in the manufacturing industry into a global data science pioneer. Aggarwal's learning curve was very easy for a non-programmer. He was able to devote a sufficient amount of time to practising programming while still paying attention to other important factors thanks to the course's carefully planned framework. His development was quick, focused, and successful. He is currently making contributions to analytics and customer data science.

        The journey taken by Archish Rai Kapil from history graduate to data analyst at Air India is pretty inspiring. He began with advanced Excel expertise and accumulated certifications in a variety of analytics technologies. His lack of CS experience never got in the way, and Archish graciously gives Ai Campus some of the credit for providing him with "just what he needed."

        These two examples were chosen from a long list with the intent of demonstrating that anyone can make the transition to data science and highlighting the fact that active professionals can, with a little extra effort, obtain a certification in data analytics and do so successfully.

      • What distinguishes Ai Campus as a data analytics institute?

        In order to offer IT/ITES professionals a comprehensive range of data science training programmes, Ai Campus was founded in 2011. There are numerous organisations operating with a broadly comparable objective in mind. Ai Campus has become a trusted reputation over the past ten years by acting in specific ways correctly.

        The mission of Ai Campus is to empower young people in India to take advantage of opportunities in the future. The finest data science institute in India, according to Analytics India Magazine, is Ai Campus.

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