Learn Data Analytics
From Zero Experience to Analyst-Ready.
Master the full analytics lifecycle — Excel, SQL, Statistics, Python & Pandas, Data Cleaning & EDA, Visualization, Power BI/Tableau dashboards, and foundational predictive analytics. Trained exactly the way a working analyst builds insights, ending in a portfolio-ready capstone project.
What you will know and do after this course
Practical, measurable capabilities verified through live, hands-on tool work — not theoretical knowledge you'll forget.
Built for people entering data & business analytics careers
Data Analytics is a highly accessible entry point into tech — no prior coding is required, since Python is taught from the ground up. Whether you come from commerce, marketing, operations, or engineering, this is your structured route into one of the fastest-growing analytics specialisations globally.
14 modules — analytics fundamentals to capstone project
Each module: concept overview, hands-on assignments on the GIM Retail case study, and real interview guidance built in from the start.
Freshers often confuse analytics tools with analytics thinking. This module ensures learners understand the "why" behind analytics before touching any tool.
- Definition and scope of Data Analytics
- Types of analytics: Descriptive, Diagnostic, Predictive, Prescriptive
- Data Analytics vs Data Science vs Business Intelligence
- The data analytics lifecycle: collect → clean → analyze → visualize → interpret → act
- Common industry tools: Excel, SQL, Python/R, Power BI/Tableau
- Career roles: Data Analyst, Business Analyst, BI Analyst, Data Scientist
Most entry-level analyst roles still expect strong Excel skills for quick, ad-hoc analysis before moving to specialized tools.
- Data types and formatting in Excel
- Logical, lookup, and text functions: IF, VLOOKUP/XLOOKUP, INDEX-MATCH
- Sorting, filtering, and conditional formatting
- PivotTables and PivotCharts
- Data validation and basic error-checking
- Goal Seek and What-If analysis
SQL is consistently ranked as the most in-demand technical skill for data analyst roles — extracting data directly from databases is non-negotiable.
- Relational database concepts: tables, rows, columns, keys
- SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Joins: INNER, LEFT, RIGHT, FULL
- Aggregate functions: SUM, COUNT, AVG, MIN, MAX
- Subqueries and Common Table Expressions (CTEs)
- Window functions: ROW_NUMBER, RANK, LAG/LEAD
- Basic database design and normalization concepts
Many analysts can run a query or build a chart but cannot judge whether the result is statistically meaningful. This module builds that judgment.
- Descriptive statistics: mean, median, mode, variance, standard deviation
- Measures of distribution: skewness, kurtosis
- Probability fundamentals
- Normal distribution and the Central Limit Theorem
- Correlation vs causation
- Sampling methods and sampling bias
Business stakeholders frequently ask, "Is this difference real, or just random noise?" This module equips analysts to answer that with statistical confidence.
- Population vs sample
- Null and alternative hypotheses
- p-values and significance levels
- t-tests and chi-square tests
- Confidence intervals
- Type I and Type II errors
Python proficiency is increasingly expected even for entry-level analyst roles, allowing automation and handling of large datasets beyond Excel's limits.
- Python syntax, variables, and data types
- Control flow: loops and conditionals
- Functions and basic error handling
- Data structures: lists, dictionaries, tuples, sets
- Working with files (CSV, Excel) in Python
Pandas is the most essential Python library for analysts, used daily for cleaning, transforming, and exploring data far beyond Excel's capacity.
- DataFrames and Series
- Data import: CSV, Excel, SQL
- Filtering, sorting, and indexing
- GroupBy operations and aggregation
- Merging and joining DataFrames
- Handling missing data: dropna, fillna
- Data type conversions
Industry experience shows analysts spend 60–80% of their time cleaning and exploring data before any visualization or modeling occurs.
- Identifying and handling missing values, duplicates, outliers
- Data type consistency and standardization
- Univariate, bivariate, and multivariate analysis
- Correlation matrices and pairplots
- Summary statistics as an exploration tool
A technically correct analysis with a poor visualization fails to communicate value. Visualization skill often separates a good analyst from a great one.
- Principles of effective data visualization
- Choosing the right chart type for the message
- Matplotlib fundamentals: line, bar, scatter, histogram
- Seaborn for statistical visualization: boxplots, heatmaps, pairplots
- Avoiding misleading visualizations
Most organizations rely on BI dashboards for ongoing decision-making rather than one-off reports. BI tool proficiency is a top requirement in nearly every analyst job posting.
- BI tool interface and data connection (Excel, SQL, cloud sources)
- Data modeling: relationships between tables
- Calculated fields and measures (DAX basics)
- Building interactive dashboards: filters, slicers, drill-downs
- Dashboard design best practices
Many mid-sized organizations rely entirely on Excel for reporting. Advanced Excel skills differentiate candidates in interviews and practical assessments.
- Advanced formulas: SUMIFS, COUNTIFS, array formulas
- Power Query for data transformation
- Power Pivot and the Data Model
- Building Excel dashboards with slicers and PivotCharts
- Macros and basic VBA automation
Many analyst roles are evolving toward "analytics + light ML" responsibilities. A conceptual grounding here prepares learners for advanced specialization later.
- Supervised vs unsupervised learning
- Linear regression for prediction
- Logistic regression for classification
- Train-test split and model evaluation: accuracy, RMSE
- Overfitting vs underfitting
A brilliant analysis that is poorly communicated rarely leads to action. Hiring managers consistently rank communication as a top differentiator among candidates.
- Structuring an analytics narrative: context → insight → recommendation
- Tailoring communication to technical vs non-technical audiences
- Designing effective presentation slides for data findings
- Avoiding data overload in stakeholder communication
Capstone module — the most important module for portfolio building. Capstone projects are frequently discussed in interviews and serve as proof of applied capability.
- End-to-end application of the full analytics lifecycle
- Independent problem definition and analytical approach design
- Integration of SQL, Python/Excel, statistics, and visualization
- Final stakeholder-ready deliverable creation
What learners say after finishing this course
This rating is specific to the Data Analytics program above — not an average across Upskeeling's other courses. We show the breakdown as collected rather than just the headline number, because the detail tells you more than the average does.
The 5-star reviews consistently mention the same thing: writing SQL joins and Pandas code against real, messy datasets made the concepts click in a way slides never had. The 4-star group was happy overall but wanted more repetition time on the harder modules — Hypothesis Testing and Predictive Analytics/ML Concepts came up most. The 3-star reviews were the most useful to us internally: the recurring note was that the live batch pace is demanding alongside a full-time job, not that the content itself was weak. We've since added extra practice sessions before the Statistics and SQL modules to address exactly that.
Where this programme takes you
- Data Analyst Trainee
- Junior Business Analyst
- Reporting Analyst
- BI Analyst (Associate)
- Senior Data Analyst
- Business Intelligence Consultant
- Analytics Consultant
- Data Science / Data Engineering transition path
Your trajectory over time
Entry Level
- Data Analyst Trainee
- Junior Business Analyst
- Reporting Analyst
- BI Analyst (Associate)
Mid Level
- Senior Data Analyst
- Business Intelligence Consultant
- Analytics Consultant
- Data Science / Data Engineering (transition)
Advanced
- Analytics Manager / Lead
- Head of Business Intelligence
- Freelance Analytics Consultant
- Data Science Transition Specialist
What data analysts earn globally
A completed Data Analytics certification with a portfolio project delivers a meaningful salary premium over identical academic records without hands-on proof of skill.
We stay with you throughout your journey
During Training
- Live sessions on current industry tools
- Session recordings after every class
- Live Excel, SQL, Python & BI practice
- Detailed notes and study documents
- Curated reference e-books and cheat sheets
- Real-time business case simulations
- Interview guidance at each module
Post-Training
- 1 month additional practice environment access
- 1 year study material availability
- Job references provided
- Interview scheduling assistance
- Capstone project refinement support
- Post-course community knowledge base
Career Support
- Professional CV and resume review
- Mock interview sessions
- Career consultancy and role guidance
- Community access with peers
- Freelance and contract guidance
- Ongoing mentor availability
Already trained, or learning elsewhere? Rent a practice environment on its own.
For analysts who already know the tools and just need a live SQL database, Python workspace, and BI sandbox to practice on, or learners following another course who want real hands-on access — without enrolling in the full program.
- Dedicated SQL database and Python/Jupyter workspace — not shared or queued
- Mentor support included for troubleshooting and code review
- Practice Excel, SQL, Python, and Power BI/Tableau at your own pace
Corporate & Team Training
Programmes
Training your entire analytics or business team at once? We deliver bespoke corporate batches for IT firms, retail and e-commerce teams, and enterprises building internal analytics capability — with custom schedules, dedicated trainers, and progress reporting.
- Custom batch scheduling — weekday, weekend, or blended
- Dedicated trainer assigned to your organisation
- Team progress dashboards and completion certificates
- Module customisation for your specific tool stack
- Volume pricing — significant discounts for teams of 5+
- Post-training support for capstone and reporting projects
What you need before starting
- No prior coding background required — Python is taught from the fundamentals in Module 6
- Basic familiarity with Excel helpful but not mandatory
- A laptop or desktop with a stable internet connection
- Motivation to practise on real, messy datasets between sessions
- Any graduate can join — B.Com, BBA, MBA, B.Sc, B.Tech, or equivalent
Questions before you enrol
Three ways to get your question answered
The analysts hired next quarter
are enrolling right now.
Every week you wait is a week someone with the same background — but a portfolio-ready analytics project — pulls ahead. One message. Zero commitment.