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Online Course Advanced Certification in Data Analytics and Visualization
IT & Software Development

Advanced Certification in Data Analytics and Visualization

Build practical data analytics skills with Python, statistics, data visualization, exploratory analysis, predictive techniques and business intelligence. Work on real-world datasets, dashboards, case studies and a capstone project.
Course ModeOnline
Duration12 Weeks
StartsTo Be Announced
Course Fee
₹9,999 /-
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About The Course

Overview

The Advanced Certificate in Data Analytics and Visualization course is designed to develop practical data analytics skills from foundational concepts to advanced analytics and business intelligence.

The course covers the complete data analytics lifecycle, including data collection, cleaning, preprocessing, statistics, Python programming, NumPy, Pandas, data visualization, exploratory data analysis, predictive analytics, business intelligence, dashboards and reporting.

Learners will work with real-world datasets, apply statistical and analytical techniques, create meaningful visualizations and dashboards, and develop insights for data-driven decision-making. The program also includes hands-on practical sessions, case studies and a capstone project covering problem definition, data preparation, analysis, visualization, dashboard development and final presentation.

Learning Goals

Course Outcomes

By the end of the course, learners will be able to:

  1. Understand the fundamentals, types and applications of data analytics and the data analytics lifecycle.
  2. Collect, understand, clean, transform and preprocess data from different sources.
  3. Apply statistical concepts and techniques to analyze and interpret data.
  4. Use Python, NumPy and Pandas for data manipulation and analytics.
  5. Create effective data visualizations and perform exploratory data analysis using Matplotlib and Seaborn.
  6. Apply regression, classification, clustering and model evaluation techniques for predictive analytics.
  7. Develop business intelligence dashboards and reports using Power BI or Tableau.
  8. Apply data analytics techniques to a capstone project and communicate meaningful insights and business recommendations.
Curriculum

Course / Modules

Unit 1: Fundamentals of Data Analytics

Topics Covered:

  1. Introduction to Data Analytics
  2. Data Analytics Overview
  3. Importance and Applications of Data Analytics
  4. Types of Analytics
  5. Descriptive Analytics
  6. Diagnostic Analytics
  7. Predictive Analytics
  8. Prescriptive Analytics
  9. Data Analytics Life Cycle
  10. Problem Definition
  11. Data Collection
  12. Data Preparation
  13. Data Analysis
  14. Data Visualization
  15. Interpretation and Decision-Making
  16. Types and Sources of Data
  17. Structured, Semi-Structured and Unstructured Data
  18. Qualitative and Quantitative Data
  19. Primary and Secondary Data Sources
  20. Data Analytics Tools and Ecosystem
  21. Excel
  22. SQL
  23. Python
  24. Pandas
  25. NumPy
  26. Matplotlib
  27. Seaborn
  28. Power BI and Tableau
  29. Introduction to the Analytics Workflow

Unit 2: Data Collection, Cleaning and Preprocessing

Topics Covered:

  1. Data Collection and Import
  2. Data Acquisition
  3. CSV, Excel and JSON Data
  4. Database Data Sources
  5. APIs for Data Collection
  6. Importing Datasets into Analytics Environments
  7. Data Understanding and Profiling
  8. Dataset Structure
  9. Variables and Observations
  10. Data Types and Metadata
  11. Summary Statistics
  12. Data Profiling
  13. Data Quality Assessment
  14. Missing Values
  15. Duplicate Data
  16. Inconsistent Data
  17. Invalid Values
  18. Outliers
  19. Data Quality Issues
  20. Data Cleaning Techniques
  21. Handling Missing Data
  22. Duplicate Removal
  23. Correcting Inconsistent Values
  24. Data Validation
  25. Data Standardization
  26. Data Transformation
  27. Encoding Categorical Variables
  28. Scaling
  29. Normalization
  30. Aggregation
  31. Binning
  32. Feature Transformation
  33. Data Preprocessing Case Study

Unit 3: Statistics for Data Analytics

Topics Covered:

  1. Introduction to Statistics
  2. Population and Sample
  3. Variables
  4. Measures of Central Tendency
  5. Mean
  6. Median
  7. Mode
  8. Measures of Dispersion
  9. Range
  10. Variance
  11. Standard Deviation
  12. Quartiles
  13. Percentiles
  14. Coefficient of Variation
  15. Probability for Data Analytics
  16. Basic Probability Concepts
  17. Events
  18. Conditional Probability
  19. Independence
  20. Bayes' Theorem
  21. Probability Distributions
  22. Normal Distribution
  23. Binomial Distribution
  24. Poisson Distribution
  25. Applications of Probability Distributions
  26. Correlation and Statistical Relationships
  27. Covariance
  28. Pearson Correlation
  29. Correlation Matrix
  30. Interpretation of Statistical Relationships

Unit 4: Python for Data Analytics

Topics Covered:

  1. Python for Data Analytics
  2. Python Environment
  3. Variables and Data Types
  4. Operators
  5. Conditional Statements
  6. Loops
  7. Functions
  8. NumPy Fundamentals
  9. Arrays
  10. Array Creation
  11. Indexing
  12. Slicing
  13. Mathematical Operations
  14. Aggregation
  15. Vectorization
  16. Pandas Fundamentals
  17. Series
  18. DataFrames
  19. Indexing
  20. Filtering
  21. Sorting
  22. Data Selection
  23. Basic Data Operations
  24. Data Manipulation with Pandas
  25. GroupBy
  26. Aggregation
  27. Merging
  28. Joining
  29. Concatenation
  30. Pivot Tables
  31. Reshaping
  32. Working with Real-World Datasets
  33. Importing CSV and Excel Data
  34. Handling Dates
  35. Handling Categorical Data
  36. Handling Missing Values
  37. Practical Data Manipulation

Unit 5: Data Visualization and Exploratory Data Analysis

Topics Covered:

  1. Fundamentals of Data Visualization
  2. Purpose of Data Visualization
  3. Principles of Effective Visualization
  4. Visual Encoding
  5. Selecting Appropriate Charts
  6. Basic Charts and Graphs
  7. Bar Charts
  8. Line Charts
  9. Pie Charts
  10. Histograms
  11. Area Charts
  12. Comparison of Categorical and Numerical Data
  13. Advanced Statistical Visualizations
  14. Box Plots
  15. Violin Plots
  16. Scatter Plots
  17. Bubble Charts
  18. Density Plots
  19. Distribution Analysis
  20. Seaborn and Matplotlib
  21. Creating Professional Visualizations
  22. Customization
  23. Labels and Legends
  24. Annotations
  25. Multiple Chart Types
  26. Exploratory Data Analysis
  27. EDA Workflow
  28. Univariate Analysis
  29. Bivariate Analysis
  30. Multivariate Analysis
  31. Identifying Patterns and Anomalies
  32. Advanced EDA Case Study
  33. Real-World Dataset Analysis
  34. Statistical Interpretation
  35. Visual Interpretation

Unit 6: Advanced Analytics and Predictive Techniques

Topics Covered:

  1. Introduction to Predictive Analytics
  2. Predictive Analytics Concepts
  3. Prediction Problems
  4. Features
  5. Target Variables
  6. Training and Testing Datasets
  7. Regression Analysis
  8. Simple Linear Regression
  9. Multiple Linear Regression
  10. Regression Assumptions
  11. Model Fitting
  12. Model Interpretation
  13. Classification Techniques
  14. Classification Concepts
  15. Logistic Regression
  16. Decision Trees
  17. Classification Performance Evaluation
  18. Clustering and Segmentation
  19. Unsupervised Learning
  20. K-Means Clustering
  21. Cluster Interpretation
  22. Customer and Data Segmentation
  23. Model Evaluation and Interpretation
  24. Accuracy
  25. Precision
  26. Recall
  27. F1-Score
  28. Confusion Matrix
  29. MAE
  30. MSE
  31. RMSE
  32. Model Interpretation

Unit 7: Business Intelligence, Dashboards and Reporting

Topics Covered:

  1. Introduction to Business Intelligence
  2. BI Concepts
  3. Data-Driven Decision-Making
  4. KPIs
  5. Metrics
  6. Reporting
  7. Analytics Applications
  8. Power BI/Tableau Fundamentals
  9. Interface
  10. Data Loading
  11. Data Sources
  12. Data Modeling
  13. Basic Visualizations
  14. Interactive Dashboards
  15. Filters
  16. Slicers
  17. Drill-Down
  18. Cards
  19. Charts
  20. Interactive Reports
  21. Dashboard Design Principles
  22. Advanced Dashboard Design
  23. KPI Dashboards
  24. Calculated Fields and Measures
  25. Relationships
  26. Dashboard Storytelling
  27. User-Centric Design
  28. Data Storytelling and Reporting
  29. Communicating Insights
  30. Selecting Key Findings
  31. Narrative Visualization
  32. Executive Reports
  33. Presentation of Analytics

Unit 8: Capstone Project and Final Assessment

Topics Covered:

  1. Capstone Project – Problem Definition
  2. Project Selection
  3. Business Problem Identification
  4. Project Objectives
  5. Dataset Selection
  6. Analytical Questions
  7. Project Planning
  8. Data Collection
  9. Data Cleaning
  10. Data Preprocessing
  11. Exploratory Data Analysis
  12. Identification of Important Patterns
  13. Visualization Selection
  14. Dashboard Development
  15. Insight Generation
  16. Data Interpretation
  17. Project Documentation
  18. Final Project Presentation
  19. Project Demonstration
  20. Findings and Business Recommendations
  21. Viva
  22. Feedback
  23. Final Assessment

 

Need Help?

Frequently Asked Questions

The course duration is 60 hours and 12 Weeks.
The course is designed for learners ranging from beginner to advanced level.
Yes. The course covers Business Intelligence concepts, KPIs, Power BI/Tableau fundamentals, interactive dashboards, advanced dashboard design and data storytelling.
Yes. The final module includes problem definition, data preparation, analysis, visualization, dashboard development, project documentation, presentation and final assessment.
The course is suitable for beginners, students, graduates, working professionals and learners interested in developing practical data analytics and visualization skills.
Need More Information?

Contact

Name: Ms. Rupali Chaure

Mobile No: 9425674409

Email: [email protected]