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Wraystech Academy
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Basic

Data Analytics.

Core data analytics covering data cleaning, analysis, visualization, reporting, and business decision-making.

Learner rating

4.8/5

Course duration

4 Months

Learning modules

5 modules

Learning mode

Offline

1,460+ learners enrolledIndustry-recognized certificate
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Program overview

Data Analytics program overview

Master SQL, Advanced Excel, and Power BI to build a successful career in Data Analytics. Gain practical skills in data extraction, transformation, analysis, and dashboard creation to turn raw data into actionable business insights.

SQL queries, joins, and database fundamentals

Advanced Excel — pivot tables, VLOOKUP, macros

Power BI dashboards with DAX formulas

Real business case studies and capstone project

A structured learning journey

Data Analytics Curriculum

Explore the curriculum, build your understanding one module at a time, and put each new skill into practice.

01Business Intelligence & Excel FundamentalsModule 1 · 7 topics
  • What is Business Intelligence? — Data vs Information vs Insight vs Decision; what BI is and why businesses need it; Data Analyst vs Business Analyst vs BI Analyst roles; the BI ecosystem from data sources through ETL and data warehouses to reports/dashboards; descriptive, diagnostic, predictive, and prescriptive analytics; MIS reports vs dashboards
  • KPIs & Business Metrics Fundamentals — What makes a good KPI; Revenue, Growth %, Churn, Conversion Rate, and AOV; departmental KPIs for Sales, Finance, HR, Operations, and Marketing; reading a business dashboard like a manager
  • Excel Essentials — Excel UI, ribbons, workbook and sheet management; data types, cell formats, sorting and filtering; named ranges; data validation and drop-down lists; conditional formatting and custom rules
  • Analytical Formulas — SUM, COUNT, AVERAGE, MIN, MAX, IFS; VLOOKUP, HLOOKUP, INDEX-MATCH, XLOOKUP; IFERROR, ISNUMBER, TEXT, DATE; array formulas and dynamic arrays including FILTER, UNIQUE, and SORT
  • Data Cleaning in Excel — Remove duplicates; find and replace; TRIM/CLEAN; Text-to-Columns; Flash Fill; handling nulls, blanks, and inconsistencies; data auditing tools including trace precedents and dependents
  • PivotTables, Charts & Power Query — PivotTable rows, columns, values, and filters; calculated fields and items; slicers, timelines, and drill-down; bar, line, combo, waterfall, funnel, and sparkline charts; CSV, Excel, and SQL connections; applied steps; merge and append; M basics
  • MIS Dashboard Design — Dashboard layout principles and UX for business users; interactive dashboards with slicers; Record Macro automation basics; BI storytelling for management; protecting sheets and workbooks; print and sharing settings
02Module - 02 | SQL & Database Fundamentals for BIModule 2 · 5 topics
  • Database & BI Data Modelling Fundamentals — RDBMS tables, keys, and relationships; how BI systems store data using fact and dimension tables; SQL Server vs MySQL vs PostgreSQL; SSMS and Object Explorer; creating databases, schemas, and tables; SQL data types; CREATE, ALTER, DROP, and TRUNCATE
  • Core SQL for Business Reporting — SELECT, FROM, WHERE, ORDER BY, and DISTINCT; filtering with BETWEEN, IN, LIKE, and IS NULL; CASE WHEN expressions for business logic; INSERT, UPDATE, and DELETE statements
  • Aggregation & Business Metrics — GROUP BY and HAVING; SUM, COUNT, AVG, MIN, and MAX for KPIs; COUNT(DISTINCT); COUNTIF-style logic; ROLLUP and CUBE grouping sets
  • Joins & Relationships — INNER, LEFT, RIGHT, and FULL JOIN; CROSS JOIN and SELF JOIN; multi-table joins with aliases for business reports; NULL behaviour in joins
  • Subqueries, CTEs & Advanced SQL — Scalar, correlated, and table-valued subqueries; IN, EXISTS, and NOT EXISTS; WITH/Common Table Expressions; UNION, UNION ALL, INTERSECT, and EXCEPT; ROW_NUMBER, RANK, DENSE_RANK, LAG, and LEAD; stored procedures and views for reusable BI reporting
03Power BI & Tableau for Business IntelligenceModule 3 · 9 topics
  • Power BI Foundations — Power BI Desktop, Service, Mobile, and Gateway; Import vs DirectQuery vs Composite; connecting Excel, SQL Server, CSV, Web, and JSON; data source settings and privacy levels
  • Power Query — Query Editor interface and applied steps; data profiling with column quality and distribution; nulls, errors, replacements, and data types; pivot, unpivot, transpose, merge, and append
  • Data Modelling for BI — Star and snowflake design; fact and dimension tables; grain and keys; relationships, cardinality, and cross-filter direction; role-playing dimensions and date table creation
  • DAX for Business Metrics — Measures vs Calculated Columns; row vs filter evaluation context; SUM, COUNT, CALCULATE, FILTER, and ALL; YTD, MTD, QTD, and YoY; SUMX, RANKX, TOPN, and VAR/RETURN variables
  • Visualization, Storytelling & Publishing — Visual selection, formatting, themes, slicers, and sync slicers; drill-down, drill-through, bookmarks, and tooltips; Key Influencers, Anomaly Detection, and Forecasting; workspaces, apps, publishing, scheduled refresh, RLS; PL-300 exam strategy and mock test
  • Tableau Public Foundations — Tableau Public vs Tableau Desktop vs Tableau Server; Data Source pane, Sheets, Dashboards, and Stories; Excel, CSV, and Google Sheets connections; live connections vs extracts; dimensions vs measures; data types; joins and unions
  • Tableau Visual Analytics — Bar, line, map, scatter, and dual-axis charts; calculated fields and table calculations; filters, parameters, sets, and groups; formatting business-ready visuals for non-technical audiences
  • Tableau Dashboards & Storytelling — Dashboard layout, actions, and interactivity; stories for presenting insights to stakeholders; publishing and sharing visualizations on Tableau Public
  • Tableau vs Power BI — Choosing the right tool for the job
04Python for Data Analysis & BIModule 4 · 5 topics
  • Python Foundations — Python installation with VS Code and Jupyter Notebook; variables, data types, operators, and conditions; for and while loops; list and dictionary comprehensions; functions, lambda, *args, **kwargs, and error handling
  • NumPy for Numerical Computing — Arrays, ndarray, shape, reshape, and dtype; indexing, slicing, and broadcasting; mean, median, standard deviation, and percentile calculations
  • Pandas for Business Data Analysis — Series and DataFrame creation and import/export; reading CSV, Excel, SQL, and JSON; filtering, sorting, groupby, and aggregation as the Python equivalent of PivotTables; merging, joining, and concatenating; missing values, duplicates, and outliers
  • Data Cleaning, EDA & Visualization — Exploratory Data Analysis workflow; IQR and Z-score outlier detection; correlation matrices and heatmaps; feature engineering basics; Matplotlib, Seaborn, and Plotly for business-ready charts
  • Python + BI Integration — Python scripts inside Power BI Desktop; report automation with Python; requests library and REST API consumption; exporting insights to Excel and Power BI
05Capstone & Placement Prep1 week · 4 topics
  • End-to-end analytics capstone project
  • SQL and Power BI interview questions
  • Portfolio presentation
  • Job referrals and career guidance

Find out how this curriculum fits your career goals.

Tools & technologies

Explore industry-relevant tools through hands-on learning to master practical, in-demand skills.

Excel
SQL
Power BI
Tableau
Python
NumPy
Matplotlib
Pandas
SciPy

Build practical skills with industry-relevant learning, expert guidance, and hands-on projects designed for modern teams.

Get the complete curriculum, tools, projects, and career support details in one brochure.

Build something that matters

6 Industry Projects

Every student completes 6 real-world projects — one per month — building a professional portfolio demonstrated during placement interviews.

Project 01

Customer Churn & Retention Analytics

Purpose

Analyse customer transactions, engagement, usage patterns, complaints, and customer characteristics to identify factors influencing churn. Use analytics and predictive techniques to identify customers at risk of leaving.

Business Output

Identify high-risk and high-value customers before they leave, enabling targeted retention campaigns, personalised offers, and proactive engagement to reduce churn and improve customer lifetime value.

Project 02

Employee Attrition & Workforce Analytics

Purpose

Analyse employee information including department, tenure, salary, performance, job role, and workforce factors to understand attrition patterns and identify the major factors associated with employee turnover.

Business Output

Help HR teams identify attrition-prone departments, employee segments, and retention risks to reduce hiring costs and improve workforce stability.

Project 03

HR Analytics & Attrition Report

Purpose

Analyse revenue, expenses, costs, margins, budgets, and profitability across products, departments, regions, or business units to understand financial performance and profit-and-loss drivers.

Business Output

Reveal profitability drivers, cost leakage, budget variances, and margin improvement opportunities for better cost control, financial planning, and strategic decisions.

Project 04

Healthcare Operations & Patient Analytics

Purpose

Analyse patient visits, appointments, waiting times, hospital departments, bed occupancy, resource utilisation, and operational performance to identify bottlenecks and improve healthcare-service efficiency.

Business Output

Identify high-demand departments, resource-utilisation gaps, waiting-time problems, and operational inefficiencies to improve patient experience and support better planning.

Portfolio tip: document the problem, your approach, and the result so you can walk an interviewer through your decisions.

Learning process from career advice and enrolment through practical sessions, projects, interview preparation, and placement

Who should enroll

  • Graduates targeting data analyst roles
  • Finance, operations, and HR professionals
  • MBA students adding analytics skills
  • Anyone starting a career in data

Learning modes

  • Classroom training at Thane center
  • Live online interactive sessions
  • Recorded sessions for missed classes
  • Free demo class before enrollment

Prerequisites

  • Basic computer and Excel familiarity
  • No SQL or programming experience required
  • Analytical mindset and attention to detail

95% of our students successfully get placed after completing our programs.

Learn more about how we have been impacting thousands of careers.

  • Write complex SQL queries for business reporting
  • Build interactive Power BI dashboards with DAX
  • Analyze data in Excel for quick business insights
  • Present actionable recommendations to management

Recognition that travels with you

Globally recognized certifications

Data Analytics Certificate

Certification pathway

Stand out with proof of practical skills.

Build a credible portfolio of skills with structured preparation, practical projects, and certification guidance from experienced mentors.

Data Analytics Certificate certificate

Beyond the classroom

Career Assistance Program

Soft skill session

Resume building

Aptitude training

LinkedIn profile building

Mock interview

Job assistance

Placement Assistance

Dedicated placement support with industry connections and recruiter interactions.

Dedicated placement cell with job referrals
Resume & LinkedIn profile optimization
Mock interviews with industry experts
Interview call scheduling support
Career counseling until you get placed
Career and placement support for Data Analytics learners
Accenture logoCapgemini logoCognizant logoDeloitte logoHCLTech logoIBM logoInfosys logoWipro logoAccenture logoCapgemini logoCognizant logoDeloitte logoHCLTech logoIBM logoInfosys logoWipro logo

Access your complete learning experience through our all-in-one learning portal.

SkillUpAll-in-one
01Live Mentor Sessions
02AI-Powered Practice
03Progress Tracking
04Career support
One learning hub for your complete career journey

Your next opportunity starts here

Limited seats. High demand. Enroll today.

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Get industry-ready with Wraystech Academy

  • Enroll for one course and get access to the complete learning journey
  • In-depth curriculum with live mentor support
  • Integrated projects and industry-recognized certifications
  • Practice on real-world cases and career-ready assignments
  • Dedicated placement guidance and interview preparation
Career growth through practical learning

CAREER OUTCOME

Offer letter preview

Congratulations!

Your practical skills, projects, and career preparation can help you approach interviews with clarity and confidence.

Role-ready preparation

Portfolio, interview practice, and mentor feedback.

Career counsellor

READY

A little more clarity

Frequently asked questions

Answers to common questions about learning, projects, and getting started.

What is Data Analytics?

Data Analytics is the process of collecting, cleaning, processing, and analyzing data to identify patterns, generate insights, and support better business decisions.

Who can join a Data Analytics Course?

Students, fresh graduates, working professionals, job seekers, and individuals looking to switch careers can join the course. No advanced technical background is required to get started.

Do I need programming knowledge to learn Data Analytics?

No, prior programming experience is not mandatory. You can start with basic concepts and gradually learn SQL, Excel, and other essential analytical tools.

Which tools are covered in the Data Analytics Course?

The course covers industry-relevant tools such as Advanced Excel, SQL, Power BI, Tableau, and Python for data analysis and visualization.

Is Data Analytics a good career option for freshers?

Yes. Data Analytics offers opportunities across industries such as IT, finance, healthcare, retail, e-commerce, and banking. Freshers can explore entry-level roles after developing the required skills.

What are the eligibility criteria for a Data Analytics Course?

Students from any educational background can learn Data Analytics. Basic computer knowledge, logical thinking, and an interest in working with data are helpful.

What is the difference between Data Analytics and Data Science?

Data Analytics focuses on analyzing existing data to identify trends and generate business insights. Data Science involves broader concepts, including advanced statistics, machine learning, predictive modeling, and AI.

Will I learn SQL and Advanced Excel in this course?

Yes. You will learn SQL for database querying and data extraction, along with Advanced Excel for data cleaning, calculations, analysis, pivot tables, and reporting.

What will I learn in Power BI?

You will learn to connect data sources, transform data, create data models, write DAX formulas, and build interactive dashboards and reports using Power BI.

Are practical projects included in the Data Analytics Course?

Yes. The course includes practical exercises, assignments, case studies, and industry-oriented projects to help you apply analytical concepts to real-world business scenarios.

How long does it take to complete a Data Analytics Course?

The course duration depends on the training structure and learning schedule. Contact the institute for the latest batch duration and timetable.

What career opportunities are available after completing the course?

You can explore roles such as Data Analyst, Junior Data Analyst, Business Analyst, MIS Executive, Reporting Analyst, and Power BI Developer, depending on your skills and experience.

Can non-IT students pursue a career in Data Analytics?

Yes. Students from commerce, science, arts, management, and other non-IT backgrounds can learn Data Analytics with structured training and regular practice.

Does the Data Analytics Course include placement assistance?

Placement assistance may include resume preparation, mock interviews, career guidance, and support in identifying relevant job opportunities.

What are the benefits of learning Data Analytics at Raystech Academy?

Raystech Academy focuses on practical learning, industry-relevant tools, hands-on projects, expert guidance, and career preparation to help students develop job-oriented Data Analytics skills.

Your next chapter starts with a conversation.

Discuss your background, explore the course, and get help choosing a learning plan that fits your goals.

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