Data Analytics & SQL
Ananya Krishnan
Over eight years of analytics experience across fintech and e-commerce. She has trained more than 500 learners and focuses on practical SQL and business intelligence.
Classroom & live online · Singapore
Data Analytics Course in Singapore by Uncodemy
Launch a career built on data with Uncodemy's Data Analytics Course in Singapore. Learn from working professionals, apply your skills through real projects, and move forward with structured placement guidance built to get you hired.
Considering a move into data-driven roles? Uncodemy's Data Analytics Course in Singapore is designed for both freshers and working professionals seeking practical, hands-on training rather than passive lectures. Through structured modules, guided assignments, and consistent mentor support, you'll build the technical skill and self-assurance needed to succeed in this competitive, fast-moving field.
- 18 learners per batch
- 12 graded projects
- 14 tools
- 180 hiring partners
- 6 mock interviews
Why Learn Data Analytics in Singapore?
Data analytics involves examining raw data to surface patterns, trends, and insights that support smarter, evidence-based decisions.
It plays a central role in helping organizations solve operational problems and identify new opportunities rather than relying on guesswork. Here's a simplified breakdown of how the process typically unfolds: set clear goals, understand the business context and available data, get a preliminary look at the dataset, build focused hypotheses, test those hypotheses, and confirm and validate results.
There are four core types of Data Analytics: Descriptive (what happened?), Diagnostic (why did it happen?), Predictive (what's likely to happen next?), and Prescriptive (what action should follow?). Uncodemy's Data Analytics Training Course in Singapore is structured to help you build each of these competencies, giving you a genuine edge in the job market.
Course Fee & Duration
The course fee is ₹17,500 + GST as a limited-seat offer, down from the listed fee of ₹50,000 + GST, and the course runs 5–6 months.
An EMI option starts at ₹2,999/month for learners who prefer to pay in parts. The offer applies to a fixed number of seats per batch, not to every batch of the year. Duration depends on the track you pick: weekday batches finish nearer 5 months, and weekend batches run closer to 6 months. Both tracks cover the same syllabus, the same 12 projects, and the same placement support. Nothing is trimmed from the weekend option.
| Item | Amount |
|---|---|
| Listed fee | ₹50,000 + GST |
| Special offer fee (limited seats) | ₹17,500 + GST |
| EMI option | Starting ₹2,999/month |
| Course duration | 5–6 months |
Who Should Join This Course?
This course is ideal for freshers, working professionals, and career-switchers who want to break into the data analytics field.
To build genuine competence in this field, you'll need skills across data management, data visualization, business understanding, statistical analysis, data cleaning, programming (Python, SQL), machine learning basics, Excel, critical thinking, and clear communication. Uncodemy's Data Analytics Training Course in Singapore is structured to help you build each of these competencies, whether you're starting fresh or building on prior exposure.
Curriculums for Data Analytics Training Courses in Singapore
Data Analytics Curriculum
The curriculum has been designed by faculty from IITs, and Expert Industry Professionals.
120+
Hours of Content
75+
Live Sessions
10+
Tools and Software
Set the Basics Right
At Uncodemy, we offer a comprehensive Data Analytics training course in Singapore designed to equip students with the skills and knowledge required to excel in the field of data analysis. Our curriculum is meticulously crafted to cover all essential aspects of data analytics, ensuring that our students gain a deep understanding and practical experience.
Here’s a detailed overview of what you can expect from our Data Analytics course curriculum:
1. Python for Data Analytics
- Need for Programming
- Advantages of Programming
- Overview of Python
- Organizations using Python
- Python Applications in Various Domains
- Python Installation
- Variables
- Operands and Expressions
- Conditional Statements
- Loops
- Command Line Arguments
- Method of Accepting User Input and eval Function
- Python - Files Input/Output Functions
- Lists and Related Operations
- Tuples and Related Operations
- Strings and Related Operations
- Sets and Related Operations
- Dictionaries and Related Operations
- User-Defined Functions
- Concept of Return Statement
- Concept of name=” main ”
- Function Parameters
- Different Types of Arguments
- Global Variables
- Global Keyword
- Variable Scope and Returning Values
- Lambda Functions
- Various Built-In Functions
- Introduction to Object-Oriented Concepts
- Built-In Class Attributes
- Public, Protected and Private Attributes, and Methods
- Class Variable and Instance Variable
- Constructor and Destructor
- Decorator in Python
- Core Object-Oriented Principles
- Inheritance and Its Types
- Method Resolution Order
- Overloading
- Overriding
- Getter and Setter Methods
- Inheritance-In-Class Case Study
- Standard Libraries
- Packages and Import Statements
- Topics : Working with Modules and Handling Exceptions
- Reload Function
- Important Modules in Python
- Sys Module
- Os Module
- Math Module
- Date-Time Module
- Random Module
- JSON Module
- Regular Expression
- Exception Handling
- Basics of Data Analysis
- NumPy - Arrays
- Operations on Arrays
- Indexing Slicing and Iterating
- NumPy ArrayAttributes
- Matrix Product
- NumPy Functions
- Functions
- Array Manipulation
- File Handling Using NumPy
- Array Creation and Logic Functions
- File Handling Using Numpy
- Introduction to pandas
- Data structures in pandas
- Series
- Data Frames
- Importing and Exporting Files in Python
- Basic Functionalities of a Data Object
- Merging of Data Objects
- Concatenation of Data Objects
- Types of Joins on Data Objects
- Data Cleaning using pandas
- Exploring Datasets
2. Data Science Primer and Statistics
- What is Data Science?
- What does Data Science involve?
- Era of Data Science
- Business Intelligence vs Data Science
- Life cycle of Data Science
- Tools of Data Science
- Application of Data Science
- Introduction
- Stages of Analytics
- CRISP DM Data Life Cycle
- Data Types
- Introduction to EDA
- First Business Moment Decision
- Second Business Moment Decision
- Third Business Moment Decision
- Fourth Business Moment Decision
- Correlation
- What is Feature
- Feature Engineering
- Feature Engineering Process
- Benefit
- Feature Engineering Techniques
- Basics Of Probability
- Discrete Probability Distributions
- Continuous Probability Distributions
- Central Limit Theorem
- Concepts Of Hypothesis Testing - I: Null And Alternate Hypothesis, Making A Decision, And Critical Value Method
- Concepts Of Hypothesis Testing - II: P-Value Method And Types Of Errors
- Industry Demonstration Of Hypothesis Testing: Two-Sample Mean And Proportion Test, A/B Testing
3. Machine Learning
- Simple Linear Regression
- Simple Linear Regression In Python
- Multiple Linear Regression
- Multiple Linear Regression In Python
- Industry Relevance Of Linear Regression
- Univariate Logistic Regression
- Multivariate Logistic Regression: Model Building And Evaluation
- Logistic Regression: Industry Applications
- Data mining classifier technique
- Application of KNN classifier
- Lazy learner classifier
- Altering hyperparameter(k) for better accuracy
- Black box
- SVM hyperplane
- Max margin hyperplane
- Kernel tricks for non linear spaces
- Rule based classification method
- Different nodes for develop decision trees
- Discretization
- Entropy
- Greedy approach
- Information gain
4. SQL
- Introduction to Databases
- How to create a Database instance on Cloud?
- Provision a Cloud hosted Database instance.
- What is SQL?
- Thinking About Your Data
- Relational vs. Transactional Models ER Diagram
- CREATE Table Statement and DROP tables
- UPDATE and DELETE Statements
- Retrieving Data with a SELECT Statement
- Creating Temporary Tables
- Adding Comments to SQL
- Basics of Filtering with SQL
- Advanced Filtering: IN, OR, and NOT
- Using Wildcards in SQL
- Sorting with ORDER BY
- Math Operations
- Aggregate Functions
- Grouping Data with SQL
- Using Subqueries
- Subquery Best Practices and Considerations
- Joining Tables
- Cartesian (Cross) Joins
- Inner Joins
- Aliases and Self Joins
- Advanced Joins: Left, Right, and Full Outer Joins
- Unions
- Working with Text Strings
- Working with Date and Time Strings
- Date and Time Strings Examples
- Case Statements
- Views
- Data Governance and Profiling
- Using SQL for Data Science
- How to access databases using Python?
- Writing code using DB-API
- Connecting to a database using DB API
- Create Database Credentials
- Connecting to a database instance
- Creating tables, loading, inserting, data and querying data
- Analysing data with Python
5. Excel
- Input data & handling large spreadsheets
- Tricks to get your work done faster
- Automating data analysis (Excel VLOOKUP, IF Function, ROUND and more)
- Transforming messy data into shape
- Cleaning, Processing and Organizing large data
- Spreadsheet design principles
- Drop-down lists in Excel and adding data validation to the cells.
- Creating Charts & Interactive reports with Excel Pivot Tables, PivotCharts, Slicers and Timelines
- Functions like: - COUNTIFS, COUNT, SUMIFS, AVERAGE and many more.
- Excel features: - Sort, Filter, Search & Replace Go to Special etc...
- Importing and Transforming data (with Power Query)
- Customize the Microsoft Excel interface
- Formatting correctly for professional reports.
- Commenting on cells.
- Automate data entry with Autofill and Flash-fill.
- Writing Excel formulas & referencing to other workbooks / worksheets.
- Printing options
- Charts beyond column and bar charts: - Pareto chart, Histogram, Treemap, Sunburst charts & more
- Introduction to Excel for Data Analytics
- Data Cleaning in Excel
- Formulas and Functions
- Lookup Functions
- Conditional Functions
- Sorting and Filtering
- Pivot Tables
- Pivot Charts
- Data Visualization
- Excel Dashboards
- Advanced Excel Analytics
6. Tableau
- Introduction to Data Visualization
- Tableau Introduction and Tableau Architecture
- Exploring Data using Tableau
- Working with Data using Tableau including Data Extraction and Blending
- Various Charts in Tableau(Basics to Advanced)
- Sorting-Quick Sort, Sort from Axis, Legends, Axis, Sort by Fields
- Filtering- Dimension Filters, Measure Filters, Date Filters, Tableau Context Filters
- Groups , Sets and Combined Sets
- Reference Lines, Bands and Distribution
- Parameters, Dynamic Parameters and Actions
- Forecasting-Exponential Smoothening Techniques
- Clustering
- Calculated Fields in Tableau, Quick Tables
- Tableau Mapping Features
- Tableau Dashboards, Dashboards Action and Stories
- Introduction to Tableau
- Tableau Interface and Navigation
- Connecting Data Sources
- Data Preparation in Tableau
- Dimensions and Measures
- Charts and Visualizations
- Filters and Sorting
- Calculated Fields
- Dashboards
- Interactive Dashboards
- Tableau Stories
- Publishing and Sharing Dashboards
7. Power BI
- Introduction to Power BI – Need, Importance
- Power BI – Advantages and Scalable Options
- Power BI Data Source Library and DW Files
- Business Analyst Tools, MS Cloud Tools
- Power BI Installation
- Power BI Desktop – Instalation, Usage
- Sample Reports and Visualization Controls
- Understanding Desktop & Mobile Editions
- Report Rendering Options and End User Access
- Report Design with Databse Tables
- Report Visuals, Fields and UI Options
- Reports with Multiple Pages and Advantages
- Pages with Multiple Visualizations. Data Access
- “GET DATA” Options and Report Fields, Filters
- Report View Options: Full, Fit Page, Width Scale
- Report Design using Databases & Queries
8. Artificial Intelligence (AI) & Generative AI
- What is Artificial Intelligence?
- History and Evolution of AI
- Types of AI: Narrow AI, General AI, Super AI
- AI vs Machine Learning vs Deep Learning
- Real-world Applications of AI
- AI in Business, Healthcare, Finance, and Retail
- Ethical Considerations in AI
- What is Generative AI?
- How Generative AI Works: Overview of GANs, VAEs, and Transformers
- Popular Generative AI Models: GPT, DALL-E, Stable Diffusion
- Prompt Engineering: Basics and Best Practices
- Use Cases of Generative AI: Content Creation, Code Generation, Image Synthesis
- Ethical and Bias Issues in Generative AI
- Introduction to Generative AI and Large Language Models (LLMs)
- Prompt Engineering fundamentals for data tasks
- Using ChatGPT/Claude for Exploratory Data Analysis (EDA)
- AI-assisted data cleaning and preprocessing
- Writing and debugging SQL queries using AI tools
- AI-assisted Python scripting for data analysis
- Using Copilot in Microsoft Excel for automation
- Power BI Copilot for report and dashboard generation
- AI-generated data visualizations and insights summaries
- Automating repetitive analytics tasks with AI
- Ethical use of AI in data analytics (bias, accuracy, data privacy)
- Case Study: Using AI to speed up a real-world analytics project
- Introduction to NLP
- Tokenization, Stemming, Lemmatization
- Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe)
- Transformers and Attention Mechanism
- Large Language Models (LLMs): GPT, BERT, LLaMA
- Fine-tuning LLMs for Specific Tasks
- Building Chatbots and Conversational AI
- Neural Networks: Perceptron, Activation Functions
- Feedforward Neural Networks (FNN)
- Convolutional Neural Networks (CNN) for Image Data
- Recurrent Neural Networks (RNN) and LSTMs for Sequential Data
- Autoencoders and Variational Autoencoders (VAEs)
- Introduction to GANs (Generative Adversarial Networks)
- Using TensorFlow / Keras for Deep Learning
- Building AI Models using Python (Scikit-learn, TensorFlow, PyTorch)
- Model Evaluation and Hyperparameter Tuning
- Deploying AI Models using Flask / FastAPI
- Introduction to MLOps
- AI Model Monitoring and Maintenance
- Using Cloud AI Services: AWS SageMaker, Azure AI, Google AI
- Building End-to-End AI Applications
- AI Fairness, Accountability, and Transparency
- Bias Detection and Mitigation in AI Models
- Explainable AI (XAI)
- Data Privacy and Security in AI
- Emerging Trends: Multimodal AI, Self-supervised Learning
- AI and the Future of Work
- Career Opportunities in AI and Gen AI
9. R Programming for Data Analytics
- Introduction to R
- R Programming Basics
- Variables and Data Types
- Operators and Expressions
- Conditional Statements
- Loops and Functions
- Data Structures in R
- Vectors, Lists and Matrices
- Data Frames and Factors
- Data Import and Export
- Data Manipulation using R
- Data Cleaning using R
10. Data Visualization with ggplot2
- Introduction to Data Visualization
- Introduction to ggplot2
- Basic Plots using ggplot2
- Bar Charts
- Histograms
- Box Plots
- Scatter Plots
- Line Charts
- Customizing ggplot2 Visualizations
- Themes, Labels and Annotations
- Multiple Plots and Facets
11. Business Intelligence with Looker
- Introduction to Business Intelligence
- Introduction to Looker
- Looker Interface and Navigation
- Connecting Data Sources
- Data Exploration in Looker
- Creating Reports and Dashboards
- Filters and Dimensions
- Measures and Visualizations
- Creating Interactive Dashboards
- LookML Basics
- Sharing and Scheduling Reports
12. MATLAB for Data Analysis
- Introduction to MATLAB
- MATLAB Environment and Interface
- Variables and Data Types
- Arrays and Matrices
- Operators and Expressions
- Conditional Statements and Loops
- Functions in MATLAB
- Data Import and Export
- Data Manipulation using MATLAB
- Data Visualization using MATLAB
- Statistical Analysis using MATLAB
Uncodemy's Data Analytics training course in Singapore not only covers these fundamental topics but also includes hands-on experience with real-world data, ensuring that students are well-prepared for the demands of the industry. Our curriculum is designed to provide a balanced mix of theoretical knowledge and practical skills, making it one of the best data analytics courses available.
By choosing Uncodemy, you are opting for a program that is tailored to meet the needs of aspiring data analysts, offering a robust education in a field that is both dynamic and in high demand. Whether you are looking for offline or online data analytics courses in Singapore, our training programs are structured to provide the best learning experience possible.
Tools & Technologies Covered
You work hands-on with 14 tools across the course:
- Languages: Python (pandas, NumPy, Matplotlib, Seaborn), SQL, R
- Databases: MySQL, PostgreSQL
- Reporting: Advanced Excel, Power Query, Tableau, Power BI with DAX, Looker
- Gen AI: ChatGPT, GitHub Copilot, Power BI Copilot, Claude for document analysis
- Workflow: Jupyter Notebook, Google Colab, Git and GitHub for version control
Every tool appears in at least one graded project, so nothing stays theory-only.
Industry Expert Trainers
Learn from professionals with genuine, hands-on analytics experience from leading companies.
Swipe to see all four trainers →
Python & Statistics
Rahul Mehta
Worked as a data scientist at a leading IT services firm. He brings real-world project experience into the classroom and helps learners build strong statistical foundations.
Power BI & Tableau
Priya Deshmukh
Led dashboard development teams at two product companies. Her sessions focus on storytelling with data and creating interactive dashboards that drive business decisions.
AI & Gen AI
Vikram Singh
Specializes in AI and machine learning applications in analytics. He has helped multiple teams integrate Gen AI tools into their daily workflow.
Placement Assistance & Hiring Partners
Uncodemy backs every learner with complete placement assistance, from resume building to mock interviews.
Our placement support includes structured interview assistance, placement drives run in partnership with leading companies, and career focus on roles like Data Analyst, BI Analyst, AI Analyst, and Data Visualization Specialist.
Resume Building
One page, project-led, matched to the exact keywords in analyst job posts.
Mock Interview with Industry Experts
Live mock interviews conducted by industry professionals to prepare you for real hiring rounds.
MNC Interview Calls
Get interview calls from top MNCs through our extensive hiring partner network.
Live Project Work
Work on real-world projects that become the centerpiece of your portfolio.
Grooming Sessions
Professional grooming sessions to help you present yourself confidently in interviews.
Career Transition Support
Guidance and support for a smooth career transition into the analytics field, with 12 months of continued assistance.
Salary Packages for Data Analytics Professionals
Compensation in data analytics varies by experience level, specialization, and hiring organization, but the field generally offers strong earning potential.
As more organizations lean on data to guide decisions, roles like Data Analyst, Data Scientist, BI Analyst, and Data Engineer continue to see steady hiring activity across IT, finance, healthcare, and retail sectors.
| Role | Salary Range |
|---|---|
| Entry-Level (0–2 yrs) | ₹3–6 LPA |
| Mid-Level (3–5 yrs) | ₹6–10 LPA |
| Senior (5+ yrs) | ₹10–15 LPA |
| BI Analyst | ₹7–12 LPA |
| Data Engineer | ₹8–18 LPA |
| Data Scientist | ₹8–20 LPA |
These figures can climb further for professionals at top-tier organizations or those with specialized expertise in advanced analytics and machine learning.
Get Renowned Data Analytics Certification in Singapore
Earning a widely recognized Data Analytics Certification can genuinely strengthen your professional standing.
Uncodemy's Singapore-based program pairs certification with hands-on project work, experienced trainers, and a curriculum shaped by what employers actively look for. You'll gain applicable skills, tap into a supportive alumni network, and receive career guidance built to help you turn learning into a genuine job outcome.
Certification partners: ISO, NASSCOM, Skill India. Get certified by IBM & Microsoft — Uncodemy's Data Analytics Certification is backed by globally recognized names.
Trends in Data Analytics Training
The analytics landscape continues to shift quickly, and staying current with emerging trends is essential for anyone building a career in this space.
Real-time and Big Data Processing
Organizations increasingly need instant insights rather than delayed reporting, pushing demand for real-time analytics capability.
AI/ML Integration
Machine learning is becoming a standard part of the analyst's toolkit, not just a specialist add-on.
Advanced Visualization
Tools that turn complex datasets into clear, interactive stories are now considered essential rather than optional.
Cloud-Based Analytics
Platforms like AWS, Azure, and Google Cloud are becoming the default environment for scalable data work.
Industry-Specific Applications
Sector-focused analytics — in healthcare, finance, retail — is growing as businesses seek more tailored insight.
Project-Based Learning
Employers increasingly value hands-on project experience over theoretical knowledge alone.
Why Choose Uncodemy for Data Analytics Training in Singapore?
Uncodemy blends expert-led instruction with genuinely hands-on learning, making it a strong choice for anyone serious about mastering Data Analytics.
What genuinely sets Uncodemy apart is our emphasis on applied practice. You'll work through live projects that simulate real business scenarios, helping you internalize complex concepts while building genuine confidence in tackling analytics tasks independently. Flexible learning formats — both online and offline — let you progress at a pace that suits your schedule.
Once you complete the training, you'll walk away with a certification genuinely recognized by employers, alongside strong placement support designed to connect you with roles matching your skills and career goals. With continuous mentorship and access to an active professional network, Uncodemy ensures you're not just trained — you're truly ready for a lasting career in Data Analytics.
Uncodemy's Placed Students
Over 5,500+ learners have gone on to build careers in analytics after completing Uncodemy's Data Analytics Course in Singapore.
“Our training model is built around measurable outcomes — pairing strong technical grounding with focused interview preparation — so students leave genuinely equipped to compete for real roles, not just holding a piece of paper.”
“I joined as a fresher with no coding background. The structured modules and mentor support helped me build confidence. I'm now working as a BI Analyst at a product startup.”
“The live projects were the game-changer. I could show real work in interviews, not just talk about what I learned. The placement team was with me every step of the way.”
Frequently Asked Questions
Which Institute is Best for Data Analytics Course in Singapore?
Uncodemy is widely regarded as a strong choice for Data Analytics training in Singapore. The institute offers structured learning led by experienced trainers, real project work, and consistent placement support, making it a solid option for anyone entering the data field.
What are the Data Analytics job trends in Singapore?
Demand for analytics professionals in Singapore continues to grow as businesses increasingly rely on data-driven decisions. Roles like data analyst and BI analyst are seeing consistent hiring, especially among candidates skilled in visualization tools and statistical analysis.
How Long Does It Take For You to Complete Data Analytics Training Course?
Uncodemy's Data Analytics training course generally spans 5–6 months, giving learners enough time to build both theoretical understanding and applied project experience.
What career opportunities can I explore after completing this Data Analytics course?
Graduates can pursue roles such as Data Analyst, Data Scientist, Business Intelligence Analyst, Data Engineer, Analytics Consultant, and Market Research Analyst, among others, across multiple industries.
What skills will I gain from Uncodemy's Data Analytics training?
You'll build proficiency in data interpretation, visualization, statistical methods, data cleaning, and foundational machine learning, alongside tools like Excel, Python, SQL, and Tableau.
What is Data Analytics Certification, and is it good for your career?
A Data Analytics Certification validates your practical skills to employers. It strengthens your resume, improves job prospects, and can positively influence salary negotiations by showcasing verified expertise.
Is Data Analytics in demand in 2026?
Yes, demand remains strong in 2026 as organizations across sectors continue prioritizing data-backed decision-making, keeping the job market favorable for trained analytics professionals.
Who is eligible for Data Analytics Course?
Anyone with basic familiarity with spreadsheets or numbers can start. A background in math, statistics, or computer science helps but isn't mandatory — a genuine willingness to learn matters most.
How do I start Data Analytics Course in Singapore?
Simply research the program, complete the application form with required documents, pay the course fee, and begin attending sessions in your preferred format — online or offline.
What is the syllabus of Data Analytics Course?
The syllabus covers Python, statistics, machine learning, SQL, Excel, Tableau, Power BI, AI & Generative AI, R Programming, data visualization, business intelligence, and live project work.
What is the best course on Data Analytics?
The right course depends on your goals, but generally, one offering comprehensive content, practical projects, and strong mentor support — like Uncodemy's — tends to deliver the best outcomes.
Is coding required to enroll in this Data Analytics online training?
Coding isn't mandatory at the outset, but foundational knowledge of Python or SQL becomes helpful as the course moves into more technical modules.
Is Data Analytics tough to learn?
It can be challenging initially, but with structured guidance and consistent practice, most learners find it manageable — especially with mentor support available to clarify difficult concepts.
Is Data Analytics high paying job?
Yes, data analytics roles generally offer competitive compensation, with salaries scaling meaningfully as experience and specialization increase.
Is Data Analytics good for job?
Absolutely. With consistently growing demand across industries, data analytics offers strong long-term career stability and multiple pathways for advancement.
Sign Up for Our Data Analytics Training in Singapore ASAP!
Your move into data analytics starts with a single decision. Enroll in Uncodemy's Data Analytics Training Course in Singapore and take the first confident step toward certified, in-demand expertise.
Seats are limited, so reach out today and let our counseling team help you map out the right path forward. Book a free demo class, sit through one live session, and check the trainers and batch size yourself before you pay anything.
