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IBM
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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.
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Limited Seats in the Cohort
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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
Pregrad Career Assist Access
Mentoring
career-specific resume tailoring
Personal Branding
Build and showcase your skills in public
Strategic LinkedIn profiling
Community Session
Strengthen Communication
Improve presentation skills
Interview Preparation
Mock community sessions & GD
Art of negotiation
Domain workshops/Masterclasses
Masterclasses from professionals
HR Session
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*
Payment gateway Razorpay
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G Block Sector 6, Noida, Uttar Pradesh - 201301
hello@pregrad.in
+91 9971300194
G Block Sector 6, Noida, Uttar Pradesh - 201301
hello@pregrad.in
+91 9971300194