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Description

Course Description:

Data surrounds us completely. The amount and variety of data that we collect and store becomes more and more every day, from simple retail transaction data to complex and confidential medical records of millions. There is a growing demand for people who can control and manage the way data is used. These individuals desire an understanding of mathematics and computer science, as well as a familiarity with the data requirements and workings of a number of different sectors, including government, healthcare, environment, and business. Progressing from a core in applied statistics, the Masters in Data Science and Analytics online program equips students with advanced analytical training to inculcate the ability to derive insights from big data and build automated artificial intelligence processes.

The Masters in Data Science and Analytics program is highly practical in nature, integrating project-based learning, business strategy, case studies, simulations, and specific electives addressing the analytical requirements of different industry sectors. Tie-ups with corporate partners assist students with access to real-world data sets and the chance to focus on current business issues. Outside the classroom, these corporate tie-ups also signify beneficial networking connections and career opportunities.

The curriculum offers an in-depth exploration of advanced methods, analytics, and data-gathering tools.

This unique online Masters degree in data science and analytics program helps students to grow their interdisciplinary skills and gain a thorough understanding of scientific and applied knowledge in data science and analytics. Graduates go on to become well trained and qualified data scientists who are empowered to pursue careers in government, industry, or research.

Audience Profile -

Aspiring professionals possessing an educational background with a somewhat analytical frame of mind are the most appropriate candidates for pursuing the Data Science and Analytics course, including:

  • IT Professionals
  • Analytics Managers
  • Business Analysts
  • Banking and Finance Professionals
  • Marketing Managers
  • Supply Chain Network Managers
  • Beginners or Recent Graduates in Bachelors or Master’s Degree

Eligibility Standard -

  • Minimum graduation in any discipline
  • 0-5 years of work experience (freshers may also apply).
  • No coding experience is required.

Program Objectives -

Masters in Data Science and Analytics is based on the purpose to make you understand the following:

  • Data managing and mining with SQL and Python
  • Data Visualization with Tableau and Python
  • Statistical Data Analysis with Excel, Python, and R
  • Predictive Statistical Modeling Algorithms
  • Machine Learning and Deep Learning Models on Text & Visuals
  • Applying Algorithms at Scale with Big Data Systems
  • Generating Business Values and Effective Storytelling 

Learning Outcome -

  • Improving the decision-making process (quality and significance)
  • Speeding up of the decision-making process
  • Better alignment with strategy
  • Realizing cost-efficiency
  • Improving competitiveness
  • Producing a single, unified outlook of enterprise information
  • Synchronizing financial and operational strategy
  • Increase revenues
  • Sharing information with a wider audience 

Why Careerera?

  • 125000+ Students
  • 4 Million Hours of Learning Delivered
  • Top 10 Ranked Programs
  • 500+ Industry Specialists
  • 25+ India’s Best Business Analytics Faculty

What Makes Our Program Outstanding?

  1. The program is cautiously crafted by eminent academics and industry experts to assist candidates to develop knowledge of the essential and superior topics in Business Analytics.
  2. The program aims for candidates to use the analytics skills-sets to resolve actual-world business issues.
  3. Various tools and methodologies discussed in the curriculum to gear the candidate skills and prepare them for the field.
  4. Weekend classroom sessions help candidates to acquire an understanding of the program.
  5. Never miss a class as you may switch to a new batch, depending on your availability to guarantee consistent growth.

Scopes upon accomplishment of the program

Major Job roles after the Masters in Data Science and Business Analytics are:

  • Data Scientist (Emphasis on a computer)
  • Data Scientist (Emphasis on analytics)
  • Quantitative Analyst
  • Data Analyst
  • Business Analyst (Manager/Consultant)

Admission Process

  • Apply and fill the application form online on our site.
  • The faculty panels will assess all the applications and select candidates depending on their profiles.
  • Selected candidates will go through an interview which will then be assessed by the faculty panel.
  • An admission proposal will be then made to the selected candidates.

Curriculum

  • Introduction to Business Analytics
  • R for Data Science
  • Introduction to R and R-Studio
  • Dealing with Data using R
  • Visualization using R
  • Descriptive Statistics
  • Introduction to Probability
  • Probability Distributions
  • Hypothesis Testing and Estimation
  • Goodness of Fit
  • Fundamentals of Finance
  • Working Capital Management
  • Capital Budgeting
  • Capital Structure
  • Core Concepts of Marketing
  • Customer Life Time Value

Analytics techniques

  • Analysis of Variance
  • Regression Analysis
  • Dimension Reduction Techniques
  • Introduction to Supervised and Unsupervised Learning
  • Clustering
  • Decision Trees
  • Random Forest
  • Neural Networks
  • Multiple Linear Regression
  • Logistic Regression
  • Linear Discriminant Analysis
  • Introduction to Time Series
  • Correlation
  • Forecasting
  • Autoregressive models
  • Handling Unstructured Data
  • Machine Learning Algorithms
  • Bias Variance trade-off
  • Handling Unbalanced Data
  • Boosting
  • Model Validation
  • Linear programming
  • Goal Programming
  • Integer Programming
  • Mixed Integer Programming
  • Distribution and Network Models

Domain exposure

  • Marketing and Retail Terminologies
  • Customer Analytics
  • KNIME
  • Retail Dashboard
  • Customer Churn
  • Association Rules Mining
  • Web Analytics: Understanding the metrics
  • Basic & Advanced Web Metrics
  • Google Analytics: Demo & Hands on
  • Campaign Analytics
  • Text Mining
  • Why Credit Risk-Using a market case study
  • Comparison of Credit Risk Models
  • Overview of Probability of Default (PD) Modeling
  • PD Models, types of models, Steps to make a good model
  • Market Risk
  • Value at Risk - using stock case study
  • Introduction to Supply Chain
  • Dealing with Demand uncertainty
  • Designing Optimal Strategy using Case Study
  • Inventory Control & Management
  • Inventory classification
  • Inventory Modeling
  • Costs Involved in Inventory
  • Economic Order Quantity
  • Forecasting
  • Advanced Forecasting Methods
  • Examples & Case Studies

Visualization and insights

  • Introduction to Data Visualization
  • Introduction to Tableau
  • Basic charts and dashboard
  • Descriptive Statistics, Dimensions and Measures
  • Visual analytics: Storytelling through data
  • Dashboard design & principles
  • Advanced design components/ principles : Enhancing the power of dashboards
  • Special chart types
  • Case Study: Hands on using Tableau
  • Integrate Tableau with Google Sheets
  • R and Python
  • Tableau
  • SAS (Online Module)
  • Hackathons
  • Group Presentation
  • Sl. No.
  • Chapter
  • Chapter - 1
  • Introduction to Analytics:-
  • Chapter - 2
  • Statistical Method for Decision Making:-
  • Chapter - 3
  • Business Finance:-
  • Chapter - 4
  • Marketing and CRM:-
  • Chapter - 5
  • Analytics techniques
  • Chapter - 6
  • Data Mining:-
  • Chapter - 7
  • Predictive Modeling:-
  • Chapter - 8
  • Time Series Forecasting:-
  • Chapter - 9
  • Machine Learning:-
  • Chapter - 10
  • Optimization Techniques:-
  • Chapter - 11
  • Domain Specialization
  • Chapter - 12
  • Visualization and insights
  • Chapter - 13
  • Capstone project

FAQ

  • Q1. What are the potential career growths?

    • Understanding the use of business analytics to progress and resolve business problems.
    • It helps in improving the decision-making process.
    • Better layout of strategies
    • Economic and Financial operational strategy.
  • Q2. What is Business Analytics?

  • Business Analytics is a method of exercising and inspecting previous data of an organization which is mostly used to make data-driven decisions.

    • With this, we may infer insights and manage the business direction and in turn, can automate routing.
    • With this, we can assess organization-level processes like development, sales, services, and marketing.
  • Q3. How is Data Science and Business Analytics different?

  • Data Science counters questions like the impact of geography, seasonal issues and customer inclinations on the business. Data Science involves a lot of coding capabilities. Business Analytics is particular to business-related problems like cost, profit, etc. This does not involve coding greatly.

  • Q4. How are Business Analytics techniques helping the business?

  • The techniques of business analytics are helping as follows:

    • Modifying the brands functioning manner by supporting them to take more tactical decisions.
    • Rapid processing capabilities
    • Influential decision-making models Better solutions provider.
  • Q5. What aspect of successful business analytics depends on?

  • The factors on which successful business analytics depends are:

    • Data Quality
    • Skilled Analysts
    • Organizational Commitment to using data to get insights that inform business decisions.
  • Q6. What job one may look out upon the program accomplishment?

  • Job after the PG Program in Business Analytics you may look for are:

    • Data Scientist (Emphasis on a computer)
    • Data Scientist (Emphasis on analytics)
    • Quantitative Analyst
    • Data Analyst
    • Business Analyst (Manager/Consultant)
    • Fraud Analyst
    • Retail Sales
    • Analyst Statistician
  • Q7. What are the essentials for the PG Program in Business Analytics?

  • The PG Program in Business Analytics is a management program structured and provided by Careerera. As this is an executive business analytics program that sets up experts for careers in Analytics, a graduate degree in any stream with a minimum of 50% collective marks and work experience is required.

  • Q8. How does business analytics work?

    • An analysis methodology is selected and data is obtained to support the analysis.
    • Involves mining from one or more business systems.
    • Cleansing and integration into a single store like a data warehouse or data mart.
  • Q9. What are the benefits of business analytics?

  • The business analytics helps the organization in

    • Analyzing competitor wisely
    • A quick and wise decision
    • cost control
  • Q10. Why choose Careerera for the program?

    • The program is cautiously crafted by eminent academics and industry experts to assist candidates to develop knowledge of the essential and superior topics in Business Analytics.
    • The program aims for candidates to use the analytics skills-sets to resolve actual-world business issues.
    • Various tools and methodologies discussed in the curriculum to gear the candidate skills and prepare them for the field.
  • Q11. What are the delivery modes accessible for the PG Program in Business Analytics?

  • The PG Program in Business Analytics is accessible in 2 modes:

    • Classroom-based learning- Classes will be held on weekends
    • Online learning- instructed online sessions will be held on Saturday or Sundays and accordingly.

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