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Business Analytics

Business Analytics

Turn Data into Decisions: Master Business Analytics in less than a semester.

Turn Data into Decisions: Master Business Analytics in less than a semester.

Turn Data into Decisions: Master Business Analytics in less than a semester.

DURATION

3 months

Intensive

MODE

LIVE Online

Intensive

FORMAT

Hybrid

Hands-on, Theoretic

STARTING

June, 2024

Program Details

Program Details

Program Details

Our Business Analytics Pre-Graduation program equips you with the most sought-after skills and tools in today's data-driven world. Master data manipulation, analysis, and visualization using industry-standard software like Tableau or Power BI. Dive into machine learning with libraries like scikit-learn or TensorFlow. Sharpen your Python or R programming abilities and explore big data analytics tools like Hadoop or Spark. Gain hands-on experience through 10+ industry projects, putting your newfound knowledge into action. Don't wait until graduation – kickstart your career in Business Analytics today!

Our Business Analytics Pre-Graduation program equips you with the most sought-after skills and tools in today's data-driven world. Master data manipulation, analysis, and visualization using industry-standard software like Tableau or Power BI. Dive into machine learning with libraries like scikit-learn or TensorFlow. Sharpen your Python or R programming abilities and explore big data analytics tools like Hadoop or Spark. Gain hands-on experience through 10+ industry projects, putting your newfound knowledge into action. Don't wait until graduation – kickstart your career in Business Analytics today!

Our Business Analytics Pre-Graduation program equips you with the most sought-after skills and tools in today's data-driven world. Master data manipulation, analysis, and visualization using industry-standard software like Tableau or Power BI. Dive into machine learning with libraries like scikit-learn or TensorFlow. Sharpen your Python or R programming abilities and explore big data analytics tools like Hadoop or Spark. Gain hands-on experience through 10+ industry projects, putting your newfound knowledge into action. Don't wait until graduation – kickstart your career in Business Analytics today!

Who should enroll?

Who should enroll?

Who should enroll?

-You have an interest in business analytics and want to start your journey.

-You aim to make your profile more attractive to employers in data-driven roles.

-You want to enhance your academic background with practical analytics skills.

-You are considering a transition to a business analytics career.

-You enjoy analyzing data to find solutions to complex business problems.

-You have an interest in business analytics and want to start your journey.

-You aim to make your profile more attractive to employers in data-driven roles.

-You want to enhance your academic background with practical analytics skills.

-You are considering a transition to a business analytics career.

-You enjoy analyzing data to find solutions to complex business problems.

Join our community to learn, connect with like-minded peers, and get updates on the scholarship test

Limited Seats in the Cohort

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TEACHING PLAN (3 Month Program)

TEACHING PLAN (3 Month Program)

Week 1

Session 1: Introduction to Business Analytics

- Definition and importance of business analytics

- Key concepts and terminology in business analytics

Session 2: Data Collection and Data Types

- Overview of data collection methods and sources

- Understanding different types of data (structured, unstructured, and semi-structured)

Session 3: Data Cleaning and Preparation

- Techniques for cleaning and preprocessing data

- Handling missing values, outliers, and data inconsistencies

Week 1

Session 1: Introduction to Business Analytics

- Definition and importance of business analytics

- Key concepts and terminology in business analytics

Session 2: Data Collection and Data Types

- Overview of data collection methods and sources

- Understanding different types of data (structured, unstructured, and semi-structured)

Session 3: Data Cleaning and Preparation

- Techniques for cleaning and preprocessing data

- Handling missing values, outliers, and data inconsistencies

Week 1

Session 1: Introduction to Business Analytics

- Definition and importance of business analytics

- Key concepts and terminology in business analytics

Session 2: Data Collection and Data Types

- Overview of data collection methods and sources

- Understanding different types of data (structured, unstructured, and semi-structured)

Session 3: Data Cleaning and Preparation

- Techniques for cleaning and preprocessing data

- Handling missing values, outliers, and data inconsistencies

Week 2

Session 4: Exploratory Data Analysis (EDA)

- Exploring and summarizing data using descriptive statistics)

- Visualizing data through charts, graphs, and plots

Session 5: Statistical Analysis for Business Analytics)

- Statistical techniques for analyzing business data

- Hypothesis testing, correlation, and regression analysis

Session 6: Predictive Analytics and Regression Models

- Introduction to predictive analytics and its applications in business

- Building and interpreting regression models for predictive analysis

Week 2

Session 4: Exploratory Data Analysis (EDA)

- Exploring and summarizing data using descriptive statistics)

- Visualizing data through charts, graphs, and plots

Session 5: Statistical Analysis for Business Analytics)

- Statistical techniques for analyzing business data

- Hypothesis testing, correlation, and regression analysis

Session 6: Predictive Analytics and Regression Models

- Introduction to predictive analytics and its applications in business

- Building and interpreting regression models for predictive analysis

Week 2

Session 4: Exploratory Data Analysis (EDA)

- Exploring and summarizing data using descriptive statistics)

- Visualizing data through charts, graphs, and plots

Session 5: Statistical Analysis for Business Analytics)

- Statistical techniques for analyzing business data

- Hypothesis testing, correlation, and regression analysis

Session 6: Predictive Analytics and Regression Models

- Introduction to predictive analytics and its applications in business

- Building and interpreting regression models for predictive analysis

Week 3

Session 7: Classification and Decision Trees

- Overview of classification algorithms for categorical data analysis

- Building decision tree models for classification tasks

Session 8: Cluster Analysis and Market Segmentation

- Understanding clustering techniques for customer segmentation

- Analyzing customer behavior and preferences through clustering

Session 9: Time Series Analysis and Forecasting

- Basics of time series data analysis

- Forecasting future trends and patterns using time series models

Week 3

Session 7: Classification and Decision Trees

- Overview of classification algorithms for categorical data analysis

- Building decision tree models for classification tasks

Session 8: Cluster Analysis and Market Segmentation

- Understanding clustering techniques for customer segmentation

- Analyzing customer behavior and preferences through clustering

Session 9: Time Series Analysis and Forecasting

- Basics of time series data analysis

- Forecasting future trends and patterns using time series models

Week 3

Session 7: Classification and Decision Trees

- Overview of classification algorithms for categorical data analysis

- Building decision tree models for classification tasks

Session 8: Cluster Analysis and Market Segmentation

- Understanding clustering techniques for customer segmentation

- Analyzing customer behavior and preferences through clustering

Session 9: Time Series Analysis and Forecasting

- Basics of time series data analysis

- Forecasting future trends and patterns using time series models

Week 4:

Session 10: Data Mining and Association Rules

-Introduction to data mining concepts and techniques

-Extracting actionable insights through association rule mining

Session 11: Text Mining and Sentiment Analysis

-Analyzing textual data for sentiment analysis

-Techniques for extracting meaning and sentiment from unstructured text

Session 12: Optimization and Linear Programming

-Overview of optimization models and linear programming

-Applying optimization techniques to business decision-making

Week 4:

Session 10: Data Mining and Association Rules

-Introduction to data mining concepts and techniques

-Extracting actionable insights through association rule mining

Session 11: Text Mining and Sentiment Analysis

-Analyzing textual data for sentiment analysis

-Techniques for extracting meaning and sentiment from unstructured text

Session 12: Optimization and Linear Programming

-Overview of optimization models and linear programming

-Applying optimization techniques to business decision-making

Week 4:

Session 10: Data Mining and Association Rules

-Introduction to data mining concepts and techniques

-Extracting actionable insights through association rule mining

Session 11: Text Mining and Sentiment Analysis

-Analyzing textual data for sentiment analysis

-Techniques for extracting meaning and sentiment from unstructured text

Session 12: Optimization and Linear Programming

-Overview of optimization models and linear programming

-Applying optimization techniques to business decision-making

Week 5

Session 13: Simulation Modeling and Analysis

-Basics of simulation modeling for business scenarios

-Analyzing complex systems and making informed decisions through simulations

Session 14: Data Visualization and Storytelling

-Principles of effective data visualization for business analytics

-Creating compelling visualizations to communicate insights and tell data-driven stories

Session 15: Dashboard Design and Interactive Reporting

-Designing interactive dashboards for data exploration and reporting

-Creating visually appealing and user-friendly dashboards using visualization tools

Week 5

Session 13: Simulation Modeling and Analysis

-Basics of simulation modeling for business scenarios

-Analyzing complex systems and making informed decisions through simulations

Session 14: Data Visualization and Storytelling

-Principles of effective data visualization for business analytics

-Creating compelling visualizations to communicate insights and tell data-driven stories

Session 15: Dashboard Design and Interactive Reporting

-Designing interactive dashboards for data exploration and reporting

-Creating visually appealing and user-friendly dashboards using visualization tools

Week 5

Session 13: Simulation Modeling and Analysis

-Basics of simulation modeling for business scenarios

-Analyzing complex systems and making informed decisions through simulations

Session 14: Data Visualization and Storytelling

-Principles of effective data visualization for business analytics

-Creating compelling visualizations to communicate insights and tell data-driven stories

Session 15: Dashboard Design and Interactive Reporting

-Designing interactive dashboards for data exploration and reporting

-Creating visually appealing and user-friendly dashboards using visualization tools

Week 6

Session 16: Business Intelligence and Data Warehousing

- Understanding the role of business intelligence in analytics

- Basics of data warehousing for storing and accessing business data

Session 17: Data Governance and Privacy

- Ensuring data governance and compliance with privacy regulations

- Ethical considerations in handling and analyzing business data

Session 18: Project Management in Analytics

- Project management principles for analytics projects

- Planning, executing, and delivering successful analytics projects

Week 6

Session 16: Business Intelligence and Data Warehousing

- Understanding the role of business intelligence in analytics

- Basics of data warehousing for storing and accessing business data

Session 17: Data Governance and Privacy

- Ensuring data governance and compliance with privacy regulations

- Ethical considerations in handling and analyzing business data

Session 18: Project Management in Analytics

- Project management principles for analytics projects

- Planning, executing, and delivering successful analytics projects

Week 6

Session 16: Business Intelligence and Data Warehousing

- Understanding the role of business intelligence in analytics

- Basics of data warehousing for storing and accessing business data

Session 17: Data Governance and Privacy

- Ensuring data governance and compliance with privacy regulations

- Ethical considerations in handling and analyzing business data

Session 18: Project Management in Analytics

- Project management principles for analytics projects

- Planning, executing, and delivering successful analytics projects

Download Complete 3 Months Plan

Download Complete 3 Months Plan

CAREER DEVELOPMENT TRACK

CAREER DEVELOPMENT TRACK

  1. Pregrad Career Assist Access

  • Mentoring

  • career-specific resume tailoring

  1. Personal Branding

  • Build and showcase your skills in public

  • Strategic LinkedIn profiling

  1. Community Session

  • Strengthen Communication

  • Improve presentation skills

  1. Interview Preparation

  • Mock community sessions & GD

  • Art of negotiation

  1. Domain workshops/Masterclasses

  • Masterclasses from professionals

  • HR Session

  1. Career Kick-start

  • Internship/Freelance/ Applications & Interview

  • Placement assistance in final year

Total Fee of the Program

₹ 20060/- Including tax

(Non-refundable)

0% cost EMI Option Available*

EMI options for admission will not be available on discounted Fee or admission through scholarship

APPLY NOW

Live Learning delivered by Industry veteran

Sessions Backup

Hands-On Projects & Challenges

Global Certifications

Access to Career Assist cell*

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Payment gateway Razorpay

FAQ

Is this course for Beginner, Intermediate, and Advanced level?

What is the duration of this Program?

Is it possible to shift my batch?

Is this course for Beginner, Intermediate, and Advanced level?

What is the class schedule for the Program?

Will I be provided with recordings of classes and how long will we have access to it?

What is the role of the mentor?

What are the profiles of the mentors?

How often does the new Batch start?

When will we be getting internship opportunities?

How is placement at Pregrad?

What is the success rate of the Pregrad's Pre-graduation Program?

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G Block Sector 6, Noida, Uttar Pradesh - 201301

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Pregrad Logo

G Block Sector 6, Noida, Uttar Pradesh - 201301

hello@pregrad.in

Pregrad Logo

G Block Sector 6, Noida, Uttar Pradesh - 201301

hello@pregrad.in