PG Certification Program
in Business Analytics

UGC approved | 11months | IBM certification | Pay After Placement

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PG Certification Program in Business Analytics

Post-Graduation Certification program with Specialization in Business Analytics from Innomatics Research Labs in collaboration with JAIN (Deemed-to-be University). It is a sublime program of getting an IBM certified Post-Graduation program in Business Analytics from Innomatics Research Labs. This 11-month program offers extensive and in-depth knowledge on Python, Statistics, Machine Learning Algorithms, implementations with the integration of Business Analytics, Data Analytics. The core of this program will be on the case studies and projects that include all the above concepts and includes one-to-one mentoring sessions.

    Enroll Here for Course

    Recognitions of JAIN

    Skilling Academy of JAIN
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    UGC Approved Online PGC Program

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    Ranked 6th Private University in India by India today

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    Awarded 5 stars in the Young Universities Category – KSURF

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    Certification
    from IBM

    Program Highlights

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    11 month PG program

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    Industry Expert’s Curriculum

    Business Problem Logo

    PG Certificate from JAINx

    Mentoring Logo

    Online learning with interactive mentoring

    Industrial Project Experience

    Internship opportunity for Industry Exposure

    Comprehensive placement Assistance

    Comprehensive placement Assistance

    PG Certification Program in Business Analytics Curriculum

    Course 1: Python Programming

      1. Introduction
      • What is Python?
      • Why Data Science requires Python?
      • Installation of Anaconda
      • Understanding Jupyter Notebook
      • Basic commands in Jupyter Notebook
      • Understanding Python Syntax

       

      1. Data Types and Data Structures
      • Variables
      • Strings
      • Lists
      • Sets
      • Tuples
      • Dictionaries
      1. Control Flow and Conditional Statements
      • Conditional Operators, Arithmetic Operators and Logical Operators
      • If, Elif and Else Statements
      • While Loops
      • For Loops
      • Nested Loops
      • List and Dictionary Comprehensions

       

      1. Functions
      • Code Optimization
      • Scope
      • Lambda Functions
      • Map
      • Filter
      • Reduce
      • Modules and Packages

       

      1. Object Oriented Programming (OOP)
      • Attributes and Class Keywords
      • Constructers and Destructors
      • Using Self Parameter
      • Class Object attributes and Methods
      • Overloading and Over Riding
      • Inheritance
      • Method Overloading
      • Operator Overloading
      • Abstraction
      • Super Keyword
      • Method Overriding

       

      1. Class and Objects
      • Create a class
      • Create an object
      • The __init__()
      • Modifying Objects
      • Object Methods
      • Self
      • Modify the Object Properties
      • Delete Object
      • Pass Statements
      1. File Handling
      • Create, Read, Write files
      • Operations in File Handling

       

      1. Exception Handling
      • What are exceptions?
      • Exceptions in Python
      • Detecting and Handling Errors and Exception

       

      1. Web App Creation
      2. Cloud Deployment

    Course 2: Introduction to Statistical Methods

      1. Data Types and Data Structures
      • Statistics in Data science
      • What is Statistics?
      • H ow is Statistics used in Data Science?
      • Population and Sample
      • Parameter and Statistic
      • Variable and its types

       

      1. Data Gathering Techniques
      • Data types
      • Data Collection Techniques
      • Sampling Techniques:
      • Convenience Sampling, Simple Random Sampling
      • Stratified Sampling ,Systematic Sampling and Cluster Sampling

       

      1. Descriptive Statistics
      • What is Univariate and Bi Variate Analysis?
      • Measures of Central Tendencies
      • Measures of Dispersion
      • Skewness and Kurtosis
      • Box Plots and Outliers detection
      • Covariance and Correlation

       

      1. Probability Distribution
      • Probability and Limitations
      • Discrete Probability Distributions
      • Bernoulli, Binomial Distribution, Poisson Distribution
      • Continuous Probability Distributions
      • Normal Distribution, Standard Normal Distribution

       

      1. Inferential Statistics
      • Sampling variability and Central Limit Theorem
      • Confidence Intervals
      • Hypothesis Testing
      • Z -test, t-test
      • Chi – Square Test
      • F -Test and ANOVA

    Course 3: Data Analytics

      1. NumPy – Numerical Python
      • Introduction to Arrays
      • Creating and Printing of ndarray
      • Basic Operations in Numpy
      • Indexing
      • Mathematical Functions of Numpy
      • Array Processing

       

      1. Data Manipulation with Pandas
      • Series and Data Frames
      • Data Importing and Exporting through Excel, CSV Files
      • Data Understanding Operations
      • Indexing and slicing and more filtering with Conditional Slicing
      • Groupby, Pivot table and Cross Tab
      • Concatenating and Merging Joining
      • String Manipulation
      • Missing Values Handling

       

      1. Data Visualization

       

      • Introduction to Matplotlib
      • Basic Plotting
      • Properties of plotting
      • Subplots
      • Line plots
      • Histograms
      • Pie chart and Box Graphs
      • Box and Violin Plots
      • Scatterplot
      • Heatmaps

       

      1. Case study on Exploratory Data Analysis (EDA) and Visualization
      • What is EDA?
      • Uni – Variate Analysis
      • Bi – Variate Analysis
      • More on Seaborn Based Plotting Including Pair Plots, Catplot, Heat Maps, and Count plot along with matplotlib plots.
      1. Web Scraping 
      • Regular Expressions
      • Structure and Unstructured Data
      • Literals and Meta Characters
      • Regular Expressions using Pandas
      • Inbuilt Methods
      • Pattern Matching
      • Data Collection
      • Data Preprocessing
      • Data Mining

       

    Course 4: Business analytics using SQL and Tableau

    • SQL for Data Science
      • Introduction to Databases
      • Basics of SQL
        • DML, DDL, DCL and Data Types
        • Common SQL commands using SELECT, FROM and WHERE
        • Logical Operators in SQL
      • SQL Joins
        • INNER and OUTER joins to combine data from multiple tables
        • RIGHT, LEFT joins to combine data from multiple tables
      • Filtering and Sorting
        • Advanced filtering using IN, OR and NO T
        • Sorting with GROUPBY and ORDER BY
      • SQL Aggregations
        • Common Aggregations including COUNT, SUM, MIN and MAX
        • CASE and DATE functions as well as work with NULL values
      • Subqueries and Temp Tables
        • Subqueries to run multiple queries together
        • Temp tables to access a table with more than one query
      • SQL Data Cleaning
        • Perform Data Cleaning using SQL

       

      Tableau for Data Science

      • Install Tableau for Desktop 10
      • Tableau to Analyze Data
      • Connect Tableau to a variety of dataset
      • Analyze, Blend, Join and Calculate Data
      • Tableau to Visualize Data
      • Visualize Data In the form of Various Charts, Plots and Maps
      • Data Hierarchies
      • Work with Data Blending in Tableau
      • Work with Parameters
      • Create Calculated Fields
      • Adding Filters and Quick Filters
      • Create Interactive Dashboards
      • Adding Actions to Dashboards

    Course 5: Predictive Analytics

    Supervised Learning:

    1. Introduction
    • What is Machine Learning?
    • Supervised Versus Unsupervised Learning
    • Regression Versus Classification Problems Assessing Model Accuracy

     

    1. Linear Algebra
    • Basics of matrices (notation, dimensions, types, addressing the entries etc.)
    • Solving systems of linear equations using matrices and inverse matrices, including Cramer’s rule to solve AX = B
    • Vector spaces, including dimensions, Euclidean spaces, closure properties and axioms
    • Eigenvalues and Eigenvectors, including how to find Eigenvalues and the corresponding Eigenvectors

     

    Regression Techniques:

     

    1. Linear Regression
    2. Simple Linear Regression:
    • Estimating the Coefficients
    • Assessing the Coefficient Estimates
    • R Squared and Adjusted R Squared
    • MSE and RMSE

     

    1. Multiple Linear Regression
    • Estimating the Regression Coefficients
    • OLS Assumptions
    • Multicollinearity
    • Feature Selection
    • Gradient Discent

     

    1. Evaluating the Metrics of Regression Techniques
    • Homoscedasticity and Heteroscedasticity of error terms
    • Residual Analysis
    • Q-Q Plot
    • Cook’s distance and Shapiro-Wilk Test
    • Identifying the line of best fit
    • Other Considerations in the Regression Model
    • Qualitative Predictors
    • Interaction Terms
    • Non-linear Transformations of the Predictors

     

    1. Polynomial Regression
    • Why Polynomial Regression
    • Creating polynomial linear regression
    • evaluating the metrics

     

    1. Regularization Techniques
    • Lasso Regularization
    • Ridge Regularization
    • ElasticNet Regularization

     

    Classification Techniques:

     

    1. Logistic Regression
    • An Overview of Classification
    • Difference Between Regression and classification Models.
    • Why Not Linear Regression?
    • Logistic Regression:
    • The Logistic Model
    • Estimating the Regression Coefficients and Making Predictions
    • Logit and Sigmoid functions
    • Setting the threshold and understanding decision boundary
    • Logistic Regression for >2 Response Classes
    • E valuation Metrics for Classification Models:
    • Confusion Matrix
    • Accuracy and Error rate
    • TPR and FPR
    • Precision and Recall, F1 Score
    • AUC – ROC
    • Kappa Score

     

    1. Naive Bayes
    • Principle of Naive Bayes Classifier
    • Bayes Theorem
    • Terminology in Naive Bayes
    • Posterior probability
    • Prior probability of class
    • Likelihood
    • Types of Naive Bayes Classifier
    • Multinomial Naive Bayes
    • Bernoulli Naive Bayes and Gaussian Naive Bayes

     

    Tree Based Modules:

     

    1. Decision Tree
    • Decision Trees (Rule Based Learning):
    • Basic Terminology in Decision Tree
    • Root Node and Terminal Node
    • Regression Trees and Classification Trees
    • Trees Versus Linear Models
    • Advantages and Disadvantages of Trees
    • Gini Index
    • Overfitting and Pruning
    • Stopping Criteria
    • Accuracy Estimation using Decision Trees
    • Resampling Methods:
    • Cross-Validation
    • The Validation Set Approach Leave-One-Out Cross-Validation
    • k -Fold Cross-Validation
    • Bias-Variance Trade-Of or k-Fold Cross-Validation

     

    1. Ensemble Methods in Tree Based Models
    • What is Ensemble Learning?
    • What is Bootstrap Aggregation Classifiers and how does it work?

     

    1. Random Forest
    • What is it and how does it work?
    • Variable selection using Random Forest

     

    1. Boosting: AdaBoost, Gradient Boosting
    • What is it and how does it work?
    • Hyper parameter and Pro’s and Con’s

     

    Distance Based Modules:

     

    1. K-Nearest Neighbors
    • K-Nearest Neighbor Algorithm
    • Eager Vs Lazy learners
    • How does the KNN algorithm work?
    • How do you decide the number of neighbors in KNN?
    • Curse of Dimensionality
    • Pros and Cons of KNN
    • How to improve KNN performance

     

    1. Support Vector Machines
    • The Maximal Margin Classifier
    • Hyperplane
    • Support Vector Classifiers and Support Vector Machines
    • Hard and Soft Margin Classification
    • Classification with Non-linear Decision Boundaries
    • Kernel Trick
    • Polynomial and Radial
    • Tuning Hyper parameters for SVM
    • Gamma, Cost and Epsilon
    • SVMs with More than Two Classes

     

    Unsupervised Learning:

    1. Introduction
    • Why Unsupervised Learning
    • How it Different from Supervised Learning
    • The Challenges of Unsupervised Learning

     

    1. Principal Component Analysis
    • Introduction to Dimensionality Reduction and it’s necessity
    • What Are Principal Components?
    • Demonstration of 2D PCA and 3D PCA
    • Eigen Values, Eigen Vectors and Orthogonality
    • Transforming Eigen values into a new data set
    • Proportion of variance explained in PCA

     

    1. K-Means Clustering
    • Centroids and Medoids
    • Deciding optimal value of ‘k’ using Elbow Method
    • Linkage Methods

     

    1. Hierarchical Clustering
    • Divisive and Agglomerative Clustering
    • Dendrograms and their interpretation
    • Applications of Clustering
    • Practical Issues in Clustering

     

    1. Association Rules
    • Market Basket Analysis
    1. Apriori
    • Metric Support/Confidence/Lift
    • Improving Supervised Learning algorithms with clustering

    Course 6: Analytics in Industry

    1. Data Analytics in ecommerce
    • Inventory Management
    • Fraud Detection
    • Recommendation Systems
    • A/B Testing
    1. Banking and Financial Services
    • Acquisition Analytics
    • Engagement Analytics
    • Risk Analytics
    1. Healthcare Analytics
    • Analytics in the Pharmaceutical Industry
    • Customer Segmentation
    • Market Forecasting

    Capstone Projects

    Capstone Projects on
    House Price Prediction
    Recommender systems
    Fraud Detection
    Credit card Attrition
    Customer Segmentation
    Budget Optimization
    Prediction of Human activity recognition
    Cancer Diagnosis
    Taxi Demand Prediction in New York
    Stock Price Prediction

    Internship

    • An Internship is offered after the completion of the PGCP course in Business Analytics, which gives you industry experience by working as an intern.
    • This internship helps you gain practical knowledge, build professional skills and make you confident.

    Languages & Tools covered

    Certification

    You will be receiving a Post – Graduation in Business Analytics Certification from JAIN (Deemed-to-be University) in collaboration with Innomatics Research Labs.

    Meet Our Expert Advisors

    Eligibility Criteria

    Candidates must have a bachelor’s degree (minimum of 3 years).

    At least 50% marks or equivalent CGPA from a recognized university.

    Applicants must possess sufficient knowledge and understanding of the English Language.

    Who can Learn this course

    Undergrads, Professionals, and entrepreneurs who want to excel in Data Analytics, Business Analytics, and Data Science.

    Professionals who want to upskill themselves and learn in-depth on relevant technologies like Python, NumPy, Pandas, Matplotlib, and Data Visualization tools like PowerBI and Tableau, SQL, Machine Learning, Artificial Intelligence, and Business Intelligence.

    Career Guidance

    We provide Job assistance by recommending you to all the top industries that are looking for Data Analysts, Business Analysts, and Data Scientists.

    You will be escorted with Mock interviews (Technical and HR), resume building sessions, training in interpersonal skills, presentation skills and communication skills with all the professional etiquettes that helps you be confident and professional.

    Program Outcomes

    Become an Expert

    Become an expert

    Become an expert in implementing Business Intelligence techniques and AI techniques in analyzing the data and developing applications.

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    Create diverse applications

    Create diverse applications by implementing Data Analytics, Business Analytics methodologies that help for better understanding of Business problems.

    Business Problem Logo

    Solve business problems

    Solving and implementing the latest technologies in a innovative way.

    Become Resourceful

    Become Resourceful

    Become resourceful and develop one’s individual and professional knowledge.

    Pay after Placement Assistance:

    Pay After Placement is one of the unique feature that is made available for all the registrants of Post -Graduation Certificate Program in Business Analytics. You can pay only when you get the job, i.e. through the salary from your employment. If you are not placed by us, there would be no need of paying the post-placement fee.

    Terms and Conditions:

    1. The associate must be ready to work with any company and location across India.
    2. The associate must not reject any offer made by any company unless otherwise there are any hazardous conditions in the company.
    3. The associate must attend all the training programs, quizzes, and interviews conducted by the Institute.
    4. The associate must make his own arrangements w.r.t travel and logistics and must be ready to attend the interview with one day’s notice in advance.

    Program Fee Details:

    The registration fee: Rs.1,10,000/-

    Fee after Placement: Rs.1,30,000/-

    Total Course Fee: Rs.2,40,000/-

    EMI Options: The EMI Options are available for both the fee structures.

    EMI Options for the ProgramEMI Options for Pay After Placement
    6 Months: Rs.18,334 /month6 Months: Rs.21,667/month
    9 Months: Rs.12,223/month9 Months: Rs.14,445/month
    10 Months: Rs.11,000/month10 Months: Rs.13,000/month

    The Advantage of Being INNOMINION

    Recognized and rewarded by Times of India as the Best Training Institute in Hyderabad for the Digital Marketing & Data Science Training. With the relentless hard work, sophisticated equipment, advanced classrooms, cutting-edge training methodologies, practical training approach, and making individuals skillful. We have trained and placed.

    5000+ students
    100+ batches,
    100+ Hackathons,
    50+ Industry Experts,
    50+ meetups, and
    500+ Industry connections.

    Innomatics Research Labs is India’s leading professional learning Edu-tech Company. By joining us you will be able to receive access to the extensive pool of Industry experts and dedicated career assistance.

    1. 4 years LMS access with all the recorded sessions
    2. Placement Assistance with all the professional etiquettes training
    3. An innovative and creative perspective on projects
    4. One to one mentoring
    5. Industrial project experience
    6. 30+ projects and 3-4 capstone projects

    THE JAIN LEAD: Why should you choose us

    Believes In Sharing The Knowledge

    Get interactive with World-Class Faculty and Technical Experts.

    Students will get a diverse advanced curriculum curated by the Best Academicians and Industry Experts.

    Get certified from JAIN (Deemed-to-be University) and Innomatics Research Labs.

    Innovative learning by interacting with high-class instructors and Mentors.

    One to One Mentoring sessions for everyone with the industry experts.

    Practical Learning by working on case studies and Projects individually and interacting with peers for better exposure.

    Post-Graduation Certification in Business Analytics

    Mentoring Sessions on Resume Building customized for different job roles.

    Training on Presentation and Communication Skills with live presentations.

    Internship opportunity to enable the real-time project experience.

    Job Roles in Business Analytics & Data Analytics

    Career Scope in Business Analytics & Data Analytics

    Market Need:

    • According to recent Dice report, the demand for Business Analysts and Data Scientists in 2020 has increased by an average of 51% across Health care, Telecommunications, Media/Entertainment, Banking, Financial Services and Insurance Sectors etc.
    • An estimation of 2.7 Million job opportunities in Data Science and Analytics are globally declared.
    • India is the second-highest country next to the US to have generated the demand to recruit about 75,000 Data Scientists for 2020 and 2021.

    Industry Demand:

    According to a report in 2020, the job requirements for data analysts and business analysts are projected to boom from 364,000 openings to 2,720,000. According to the US Bureau of Labor Statistics, 11.5 million new jobs will be created by the year 2026.

    Frequently Asked Questions

    What is the eligibility criteria?

    Candidates must have a bachelor’s degree (minimum of 3 years degree), with at least 50% marks or equivalent CGPA from a recognized university.

    How to apply for the program?

    You can apply through our application form and Enrol at our website or call on our number +91-9951666670 for assistance. Alternatively, you may write to: info@innomatics.in

    Is this program UGC approved?

    Yes, This PGP in Data Science with Online MBA program from JAIN approved by the University Grants Commission.

    Do you offer placement assistance?

    Yes, We offer placement assistance with JAIN (Deemed-to-be University) Online’s Virtual Job Fair. This is an exclusive PG program in data science with Online MBA from JAIN. At our Virtual Job fair the screening, interview and hiring take place simultaneously with zero participation cost.