Login
Data Analyst

Advanced Diploma in Data Analytics

Enhance your career with our Advanced Diploma in Data Analytics. Learn essential skills and tools in data analysis over a comprehensive 148-hour course.

View Batches
Signature Learning Experience
Advanced Diploma in Data Analytics
Share this course

Earn an industry-recognized credential and get access to mentor-led guidance throughout your learning journey.

4.5/5 Program Rating
5,000 Learners Enrolled
Advance Level
148 hrs Total Duration
English,Hindi Language
Globally trusted accreditations Recognitions that back every SkillsBiz graduate
Nasscom Certification Six Sigma Certification ISO Certification MSME Certification ISO 9001 Certification Startup India Certification EU Certification MCA Certification Future Skills Certification Nasscom Logo
Program Overview

Experience a modern, outcome-driven curriculum

Built with industry mentors, this program blends immersive live sessions, simulation projects, and on-demand resources so you can master Mandarin Chinese with confidence.

The Advanced Diploma in Data Analytics is designed for professionals seeking to deepen their understanding of data analytics and its application in various fields.

This course covers key concepts including statistical analysis, data visualization, predictive modeling, and the use of analytics tools. Participants will engage in real-world projects that enable them to apply their learning in practical scenarios.

Why learners love this program

Each element is crafted to accelerate fluency, cultural context, and career readiness.

Lectures
74
Guided Hours
148 hrs.
Featured in the media Leading publications covering SkillsBiz Education
The Influencers of India The Indian Publisher The City Carnival Business Up2Date Bharat Bulletin Live The Hindustan Time India 24x7 News Nation Times Featuring Daily Dailyhunt
Personalised guidance

Book a discovery call with our program specialists

Get tailored advice on learning paths, certification journeys, and industry opportunities before you enroll.

In a focused 20-minute consultation we map your current proficiency, clarify where you want to get to, and suggest the quickest route to fluency with milestones we can help you hit.

  • Understand the skill gaps holding you back and the modules that can close them in the next cohort.
  • Get a personalised cohort recommendation based on availability, trainer profile, and your weekly bandwidth.
  • Discover global project work and certification add-ons that enhance Mandarin credentials on your CV.

Reserve your slot

Share your details and we will connect within 24 hours to set up a personalised walkthrough.

Curriculum Architecture

Structured modules that build fluency session after session

Explore the complete roadmap—from foundational vocabulary to advanced professional expressions—crafted with practical immersion in mind.

What is Data Analytics?
Define data analytics and identify its core components and applications across industries.
2.00 hrs
Types of Data and Analytical Approaches
Distinguish between structured and unstructured data and compare descriptive, diagnostic, predictive, and prescriptive analytics.
2.00 hrs
The Analytics Value Chain
Describe the stages from data collection to decision-making and explain how analytics creates value.
2.00 hrs
Key Roles and Stakeholders
Identify common roles in analytics projects and outline stakeholder responsibilities and collaboration practices.
2.00 hrs

Problem Framing and Hypothesis
Formulate business questions into analytical problems and develop testable hypotheses.
2.00 hrs
Data Sourcing and Governance
Explain methods for sourcing relevant data and the basics of data governance and stewardship.
2.00 hrs
Project Planning and Agile Analytics
Apply project planning techniques and agile principles for iterative analytics delivery.
2.00 hrs
Measuring Impact and ROI
Define metrics to evaluate analytics outcomes and calculate basic return on investment for projects.
2.00 hrs

Summarizing Data
Compute and interpret measures of central tendency and dispersion for different data types.
2.00 hrs
Data Distributions and Visualization
Recognize common distributions and use visual summaries to communicate data characteristics.
2.00 hrs
Correlation and Association
Measure and interpret relationships between variables using correlation and cross-tabulation.
2.00 hrs
Probability Basics
Understand fundamental probability concepts that underpin statistical reasoning.
2.00 hrs

Sampling and Estimation
Explain sampling methods and compute point estimates and confidence intervals for population parameters.
2.00 hrs
Hypothesis Testing
Perform hypothesis tests and interpret p-values and Type I/II errors in decision contexts.
2.00 hrs
ANOVA and Categorical Tests
Apply ANOVA and chi-square tests to compare group differences and categorical associations.
2.00 hrs
Regression Foundations
Introduce simple linear regression and interpret coefficients, goodness-of-fit, and assumptions.
2.00 hrs
Statistical Power and Sample Size
Assess statistical power and determine sample size considerations for study design.
2.00 hrs

Visualization Design Fundamentals
Apply principles of effective visual encoding, color use, and chart selection for clear communication.
2.00 hrs
Telling Stories with Data
Structure narratives around data to support insights and persuasive communication.
2.00 hrs
Visualizing Uncertainty
Represent uncertainty and variability in visualizations to avoid misleading interpretations.
2.00 hrs
Accessibility and Ethical Visualization
Design visuals that are accessible and ethically represent data without distortion.
2.00 hrs

Introduction to Visualization Tools
Compare common tools (e.g., Tableau, Power BI, Python libraries) and their typical use cases.
2.00 hrs
Dashboard Design and KPI Tracking
Design dashboards that surface key performance indicators and support operational decision-making.
2.00 hrs
Interactive Visualizations
Implement interactivity concepts to enable exploration and drill-down analysis.
2.00 hrs
Performance and Data Refresh Strategies
Plan for dashboard performance, data refresh approaches, and data source management.
2.00 hrs

Data Ingestion Techniques
Describe methods to ingest data from varied sources and handle schema differences.
2.00 hrs
Parsing and Transformations
Apply parsing and transformation techniques to normalize and structure raw data for analysis.
2.00 hrs
Merging and Joining Datasets
Perform joins and merges while managing keys, duplicates, and mismatches.
2.00 hrs
Data Provenance and Lineage
Track data lineage and document transformations to ensure reproducibility and trust.
2.00 hrs

Handling Missing Data
Diagnose patterns of missingness and apply appropriate imputation or exclusion strategies.
2.00 hrs
Outliers and Anomaly Treatment
Detect outliers and decide on methods for treatment based on analytical impact.
2.00 hrs
Feature Engineering Basics
Create new features through aggregation, encoding, and transformation to improve model performance.
2.00 hrs
Data Quality Assessment
Establish quality checks and validation routines to ensure dataset reliability.
2.00 hrs

Linear and Logistic Regression
Build and interpret linear and logistic regression models for prediction tasks.
2.00 hrs
Tree-based Methods
Explain decision trees and ensemble methods such as random forests and their strengths.
2.00 hrs
Model Tuning and Hyperparameters
Perform hyperparameter tuning using grid search and cross-validation to optimize models.
2.00 hrs
Imbalanced Classes and Sampling
Address class imbalance with resampling and algorithmic approaches to improve classifier performance.
2.00 hrs
Time Series and Sequential Prediction
Apply basic time series forecasting methods and recognize when sequence-aware models are required.
2.00 hrs

Evaluation Metrics for Regression and Classification
Select and compute appropriate metrics such as RMSE, MAE, accuracy, precision, recall, and AUC.
2.00 hrs
Cross-Validation Strategies
Implement cross-validation techniques and understand their role in assessing generalization.
2.00 hrs
Bias-Variance Tradeoff
Explain overfitting and underfitting and apply strategies to balance bias and variance.
2.00 hrs
Model Interpretability
Use interpretability tools and techniques to explain model predictions to stakeholders.
2.00 hrs

Supervised Learning Workflow
Outline the supervised learning process from data splitting to model deployment.
2.00 hrs
Feature Selection and Regularization
Apply feature selection methods and regularization to improve model robustness.
2.00 hrs
Evaluation and Error Analysis
Conduct error analysis to identify model weaknesses and guide improvements.
2.00 hrs
Model Deployment Basics
Describe common approaches for deploying predictive models into production environments.
2.00 hrs

Clustering Techniques
Apply clustering algorithms and evaluate cluster quality for segmentation tasks.
2.00 hrs
Dimensionality Reduction
Use PCA and other techniques to reduce dimensionality while preserving signal.
2.00 hrs
Advanced Feature Engineering
Develop domain-specific features and pipelines to enhance model input quality.
2.00 hrs
Unsupervised Evaluation and Use Cases
Assess unsupervised models and identify practical applications in analytics workflows.
2.00 hrs

Big Data Principles
Explain the characteristics of big data and when specialized technologies are required.
2.00 hrs
Storage and Data Lakes
Differentiate storage architectures, including data lakes and warehouses, and their trade-offs.
2.00 hrs
Processing Paradigms
Compare batch and stream processing approaches and their typical use cases.
2.00 hrs
Cloud Platforms for Analytics
Identify key cloud services and architectures that enable scalable analytics solutions.
2.00 hrs

Defining Project Scope
Translate a business problem into a scoped capstone project with clear objectives and deliverables.
2.00 hrs
Data Requirements and Acquisition
Identify required data sources and plan acquisition, ensuring ethical and legal compliance.
2.00 hrs
Project Timeline and Milestones
Develop a project timeline with milestones and risk mitigation strategies.
2.00 hrs
Team Roles and Collaboration
Assign roles and set up collaboration processes for effective project execution.
2.00 hrs

Data Preparation for the Capstone
Execute data cleaning and transformation steps tailored to the project dataset.
2.00 hrs
Feature Engineering and Selection
Develop and select features that directly support the project modeling goals.
2.00 hrs
Model Building and Validation
Build predictive models and validate them using appropriate evaluation frameworks.
2.00 hrs
Iteration and Refinement
Iteratively refine models and data pipelines based on evaluation and stakeholder feedback.
2.00 hrs

Communicating Results
Prepare clear presentations and visualizations that convey project findings and recommendations.
2.00 hrs
Deployment Considerations
Plan practical steps for deploying project outcomes, including monitoring and maintenance.
2.00 hrs
Ethical and Legal Review
Conduct a review of ethical, privacy, and compliance considerations relevant to the project.
2.00 hrs
Reflection and Lessons Learned
Document lessons learned and identify opportunities for future improvement and scaling.
2.00 hrs

Privacy and Data Protection
Understand privacy principles and apply data protection best practices in analytics projects.
2.00 hrs
Bias, Fairness and Accountability
Identify sources of bias, evaluate fairness, and design accountable analytics processes.
2.00 hrs
Transparent and Responsible Reporting
Adopt transparency practices in reporting methods and limitations to stakeholders.
2.00 hrs
Regulatory and Ethical Frameworks
Apply relevant regulations and ethical frameworks to guide responsible analytics work.
2.00 hrs

Landscape of Roles and Skills
Map common career paths and the skills required for roles such as analyst, scientist, and engineer.
2.00 hrs
Building a Professional Portfolio
Create a portfolio of projects and artifacts that demonstrate practical analytics capabilities.
2.00 hrs
Interview Preparation and Networking
Prepare for technical and behavioral interviews and use networking strategies to find opportunities.
2.00 hrs
Continuous Learning and Certification Paths
Plan continued learning pathways and evaluate certifications and advanced study options.
2.00 hrs
Upcoming Batches

Pick a schedule that fits your routine

We run multiple live cohorts so you can line up a batch with your goals, weekly bandwidth, and preferred mode of learning.

New cohorts are being planned

Share your interest and we will notify you as soon as the next batch opens.

Learner Voices

Stories from professionals who accelerated with SkillsBiz

Hear how learners leveraged mentor feedback, immersive projects, and certification support to reach their Mandarin goals.

P
Pooja Sethi
I joined HR Analytics with Skillsbiz to upgrade my skills in data-driven HR practices. The trainer explained concepts like workforce planning, attrition analysis, and performance metrics in a very structured way. The hands-on sessions using Excel and visualization tools were the highlight. Now I can present HR reports in a much more professional and insightful manner.
A
Abhishek Jain
I enrolled for PMP training with Skillsbiz and the experience was excellent. The trainer explained concepts like project lifecycle, risk management, and stakeholder handling in a very practical way. The mock tests and study material were very close to the actual exam. I cleared PMP in my first attempt and it has already added value to my career.
M
Manish Reddy
I did Lean Six Sigma Green Belt with Skillsbiz. The training material was clear, and the online sessions were interactive with live case studies. I was able to use Lean tools in my company and my process improvement project got recognized by management. This course has really boosted my profile.
S
Swati Chaturvedi
SAP HR module training was very detailed. I liked the practical approach and the way the trainer explained HR processes in SAP. The course gave me the clarity and confidence I was missing earlier. I strongly recommend Skillsbiz to anyone serious about SAP.
A
Amit Bansal
I took up the GST certification course at Skillsbiz and it was very helpful. The trainer explained GST concepts clearly and also taught filing procedures with practical demonstrations. This course has given me confidence to handle GST in my business as well as in client projects.
R
Ritika Singh
I joined Digital Marketing to upgrade my skills and it was the best decision. The trainers were actual industry experts, not just lecturers. They shared real-life examples of campaigns and strategies. I especially found the SEO and social media modules very powerful. I feel confident now to manage digital campaigns on my own.
N
Neha Kulkarni
I joined German B1 and I am very happy with the experience. The course structure was systematic, with grammar, vocabulary, and a lot of speaking practice. What I liked most was the personal attention given to every student. Now I am preparing for B2 with them because I trust their teaching quality.
A
Arjun Mehta
I completed French A1 and A2 levels with Skillsbiz. The trainer was very patient and made classes interactive with role-plays, videos, and practice conversations. I was initially hesitant to speak, but now I can confidently hold basic conversations in French. This has really helped me in my travel plans and career goals.
P
Priya Nair
I opted for Six Sigma Black Belt and the journey was very smooth. The curriculum was detailed, the trainer had deep industry knowledge, and the Skillsbiz support team helped me throughout. I cleared my certification in the first attempt, and this has boosted my professional profile.
R
Rohit Yadav
I enrolled for the SAP FICO course with Skillsbiz and the experience was really good. The trainer explained every concept step by step with real-time examples. The practical assignments gave me confidence to handle SAP modules, and within 2 months I was ready for interviews. Thanks to this course, I cracked my first SAP consultant job.
Frequently Asked Questions

Your questions, answered in one place

Everything you need to know before you commit—from certification timelines to support during the program.

The course is designed to be completed in 148 hours.
The course fee is INR 79999.
A basic understanding of statistics and data handling is recommended.
Students will gain skills in data analysis, visualization, predictive modeling, and more.
Yes, there will be practical assignments and projects throughout the course.
Yes, the course is offered entirely online.
Yes, a certificate will be awarded upon successful completion of the course.
You will learn to use tools like Excel, R, Python, and Tableau.
Yes, students can contact the instructor through the course platform.
Yes, there is a 14-day refund policy if you are not satisfied with the course.

Global recognitions that power your profile

We are trusted by leading accreditation bodies, ensuring your certificate is respected by employers worldwide.

Nasscom Certification Six Sigma Certification ISO Certification MSME Certification ISO 9001 Certification Startup India Certification EU Certification MCA Certification Future Skills Certification Nasscom Logo
Awards & Recognitions

Celebrated for excellence in outcome-driven education

Our pedagogy, learner outcomes, and mentor network have been acknowledged by industry councils and global education forums.

Ready to fast-track your Advanced Diploma in Data Analytics mastery?

Join a live cohort, collaborate with peers, and unlock premium resources designed for global careers.

Explore Schedules
Call WhatsApp