Professional Skill Course

Data Analytics Course in Nagpur

Go from raw spreadsheets to Power BI dashboards — master Excel, SQL, Python analytics, and data storytelling to land data analyst roles in Nagpur and beyond.

8 weeks (64 hours of instruction)

Offline Nagpur · Online · Hybrid

Course Completion Certificate — Skilleze Technologies

1-on-1 Mentorship Included

Key Facts SummaryExtractable Entity Specifications

Institute EntitySkilleze Technologies

Location & CampusNagpur, Maharashtra (Ramdaspeth / Central Nagpur)

Training & Delivery ModeOffline — Flat no. 8A, 2nd Floor, Kanchana Kiran Apartment, Ramdaspeth, Nagpur - 440010 · Online live (Zoom) · Hybrid

Program Duration8 weeks (64 hours of instruction)

Credential AwardedCourse Completion Certificate — Skilleze Technologies

Quick Answer

The Data Analytics course in Nagpur teaches advanced Excel, statistics, SQL querying, Python with pandas/numpy/matplotlib, data cleaning, exploratory data analysis, Power BI dashboard building, and business storytelling. It is designed for students, working professionals, and career-switchers who want to analyse real datasets, generate business insights, and move into data analyst or business intelligence roles.

Course Specifications at a Glance

Data Analytics course specifications
Duration	8 weeks (64 hours of instruction)
Weekly Hours	8 hours (weekday or weekend batch)
Mode	Offline — Flat no. 8A, 2nd Floor, Kanchana Kiran Apartment, Ramdaspeth, Nagpur - 440010 · Online live (Zoom) · Hybrid
Batch Timings	Weekday Morning: Mon–Fri 10:00 AM – 12:00 PM Weekday Evening: Mon–Fri 6:30 PM – 8:30 PM Weekend Batch: Sat–Sun 9:00 AM – 1:00 PM
Prerequisites	Basic computer literacy. Familiarity with Excel spreadsheets is helpful but not required. No coding experience needed.
Certificate	Course Completion Certificate — Skilleze Technologies
Fees	Fees on enquiry — speak to an advisor

Who Is This Course For?

Students

B.E., BCA, B.Sc. Statistics/Maths, B.Com, or MBA students interested in data careers
Students who want an analytics skill before placements to differentiate themselves
MCA or M.Sc. students pivoting toward data analytics from software development
Students in Nagpur at RTMNU, YCCE, VNIT, or other colleges with a quantitative interest
Final-year students building a data portfolio before campus or off-campus placements

Working Professionals

Working professionals in operations, finance, HR, or sales who use data in their job
MIS analysts or report writers who want to move from Excel to Python/Power BI
Business analysts wanting to add SQL and Python to their analytical toolkit
MBA graduates who want technical data skills to complement their business knowledge
Weekend-batch learners upskilling without leaving their current role

Week-by-Week Syllabus — Data Analytics

8 modules · 8 weeks (64 hours of instruction)

Module 1Advanced Excel for Data Analytics

Week 1

Structured tables, named ranges, and data validation
Advanced formulas: XLOOKUP, INDEX-MATCH, SUMIFS, COUNTIFS, IFERROR
Pivot Tables: grouping, filtering, calculated fields, and slicers
Pivot Charts: bar, line, pie — when to use each and formatting
Power Query: importing data from CSV/web, basic transformations, append/merge
Conditional formatting: data bars, colour scales, icon sets for dashboards
Excel dashboards: combining charts, slicers, and KPI cards

Microsoft Excel 365Google Sheets

Outcome: Build an Excel dashboard with Pivot Tables, Power Query-cleaned data, dynamic charts, and slicers — without writing a single macro.

Module 2Statistics for Data Analytics

Week 2

Descriptive statistics: mean, median, mode, variance, standard deviation
Distributions: normal, binomial, Poisson — reading and interpreting them
Correlation vs causation: Pearson correlation, Spearman rank correlation
Hypothesis testing: null/alternate hypothesis, p-values, significance levels
T-tests, chi-square tests, and when to use them
Sampling methods and sampling bias — why it matters for business data
Practical examples: A/B testing interpretation, survey data analysis

Pythonscipy.statsExcel (Stats Add-in)

Outcome: Apply descriptive statistics, correlation analysis, and basic hypothesis tests to real datasets and interpret results correctly for business decision-making.

Module 3SQL for Data Analytics

Week 3

SQL fundamentals: SELECT, WHERE, ORDER BY, LIMIT, DISTINCT
Aggregation: GROUP BY, HAVING, COUNT, SUM, AVG, MIN, MAX
Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN with real datasets
Subqueries and Common Table Expressions (CTEs) for readable complex queries
Window functions: ROW_NUMBER, RANK, LAG, LEAD, PARTITION BY
Date and string manipulation functions for time-series and text data
Query optimisation basics: EXPLAIN, indexes, and avoiding full table scans

PostgreSQLpgAdminDBeaverSQLite (practice)

Outcome: Write complex analytical SQL queries using CTEs, window functions, and multi-table joins to extract business metrics from a relational database.

Module 4Python for Data — pandas & numpy

Weeks 4–5

Python setup: Jupyter Notebooks, virtual environments, pip
numpy: arrays, broadcasting, vectorised operations, random number generation
pandas Series and DataFrame: creation, indexing, slicing, boolean masking
Reading data: CSV, Excel, JSON, SQL with pandas
DataFrame operations: merge, groupby, pivot_table, apply, lambda functions
Time series analysis: pd.to_datetime, resampling, rolling windows
Handling large datasets: chunking, memory optimisation with dtypes

Python 3.12Jupyter NotebookpandasnumpyVS Code

Outcome: Load, transform, and aggregate a multi-column real-world dataset using pandas — producing summary statistics and derived metrics without using Excel.

Module 5Data Cleaning & Exploratory Data Analysis (EDA)

Week 6

Identifying data quality issues: missing values, outliers, duplicates, type errors
Imputation strategies: mean/median fill, forward fill, drop — when to use each
Outlier detection: IQR method, Z-score, visualisation approaches
EDA workflow: univariate → bivariate → multivariate analysis
Visualisations for EDA: histograms, box plots, scatter plots, correlation heatmaps
Feature engineering basics: binning, encoding categoricals, creating derived columns
EDA report: documenting findings and anomalies for stakeholder communication

pandasmatplotlibseabornmissingno

Outcome: Perform a complete EDA on a raw dataset — cleaning it, detecting outliers, and producing a structured report of key distributions and relationships.

Module 6Data Visualisation with matplotlib & seaborn

Week 6 (continued)

matplotlib figure anatomy: Figure, Axes, subplots, twin axes
Line charts, bar charts, scatter plots, pie charts, histograms in matplotlib
seaborn high-level API: heatmap, pairplot, violin plot, boxplot, FacetGrid
Chart design principles: chart junk, data-ink ratio, colour accessibility
Plotly: interactive charts for web embedding and executive dashboards
Saving high-resolution charts for reports and presentations

matplotlibseabornPlotly (intro)

Outcome: Create publication-quality charts in matplotlib and seaborn following data visualisation best practices, and one interactive Plotly chart for web use.

Module 7Power BI — Business Intelligence Dashboards

Week 7

Power BI Desktop interface: data view, model view, report view
Importing data: CSV, Excel, SQL Server, web connectors
Power Query Editor: transforming raw data before loading
Data modelling: star schema, relationships, cardinality in Power BI
DAX basics: calculated columns, measures, CALCULATE, FILTER, time intelligence
Visualisations: bar, line, donut, map, card, slicer, and custom visuals
Publishing to Power BI Service, sharing dashboards, and scheduling refreshes

Power BI DesktopPower Query EditorDAX

Outcome: Build and publish an interactive Power BI dashboard with cross-filtering slicers, DAX-calculated KPIs, and a data model connected to a real data source.

Module 8Data Storytelling & Capstone BI Project

Week 8

Data storytelling framework: situation → complication → resolution
Choosing the right chart for the insight you are communicating
Executive summary design: KPIs, trend lines, and narrative annotations
Capstone project: full pipeline from raw CSV to Power BI published dashboard
Presenting data findings: structure, pacing, anticipating questions
Resume and LinkedIn profile update for data analyst roles
What hiring managers look for in a data analyst portfolio

Power BIpandasCanva / PowerPoint

Outcome: Complete and present a full-pipeline data analysis capstone: data cleaning in Python, SQL analysis, and a Power BI dashboard published and shared with mentor.

Tools & Technologies You Will Master

Excel / Power Query

Python 3.12

pandas

numpy

matplotlib

seaborn

Plotly

SQL / PostgreSQL

Power BI Desktop

DAX

Jupyter Notebook

scipy.stats

Language Framework Database DevOps AI Platform

Capstone Projects — What You Will Ship

Retail Sales Analytics Dashboard

Project 1

Problem: Analyse 2 years of raw retail transaction data: clean it in Python, query revenue and return metrics in SQL, and build an executive-ready Power BI dashboard.

Python / pandasPostgreSQLPower BIDAXPower Query

You ship: A published Power BI dashboard with 5+ interactive KPI visuals, Python data cleaning notebook on GitHub, and a one-page executive summary PDF.

HR Attrition Analysis

Project 2

Problem: Use the IBM HR Analytics dataset to identify the strongest predictors of employee attrition through EDA, SQL grouping, and visualisation — then present findings to a mock executive audience.

Python / seaborn / matplotlibpandasSQLPower BI

You ship: A complete EDA report with annotated visualisations, a Power BI attrition dashboard, and a 5-slide data story deck used in a simulated stakeholder presentation.

Career Outcomes — Where This Course Takes You

Target Role Titles

Data Analyst

Business Intelligence Analyst

MIS Analyst

Reporting Analyst

Junior Data Scientist (pathway)

Business Analyst (Data-focused)

Skills You Will Reach

Skills matrix for Data Analytics
Skill	Level	Indicator
Excel & Power Query	Advanced
SQL (analytical queries)	Advanced
Python pandas & numpy	Proficient
Data Visualisation (matplotlib/seaborn)	Proficient
Power BI & DAX	Proficient
Statistics & Hypothesis Testing	Foundational
Data Cleaning & EDA	Proficient
Data Storytelling	Proficient

Sample Interview Questions You Will Be Ready For

“How do you handle missing values in a dataset? Walk me through your decision process.”
“What is the difference between INNER JOIN and LEFT JOIN? Give a business example of when you would use each.”
“Explain how a window function works in SQL. Give an example using RANK or LAG.”
“What is the difference between a calculated column and a measure in Power BI / DAX?”
“How would you communicate a counterintuitive finding from your analysis to a non-technical manager?”

Salary benchmarks: <<PLACEHOLDER: Average salary for Data Analysts in Nagpur — source: AmbitionBox / Glassdoor India, verified [month year]>>

Why Skilleze Technologies — Not Just Another Institute

Skilleze Technologies vs generic institutes comparison
What You Get	Skilleze Technologies	Generic Institutes
Mentorship	1-on-1 mentor sessions + PR code reviews	Recorded videos only
Projects	2–3 deployed capstone projects with live URL	Toy exercises or no projects
Syllabus currency	Updated to 2025–26 industry tooling	Often 2–3 years behind market
Batch size	Small cohorts — mentor access is real	Large batches, no individual feedback
Certificate verifiability	Verifiable reference number issued	No verification mechanism
Location	Offline Nagpur + online + hybrid	Online only or fixed city only
Career layer	ATS resume + LinkedIn + mock interviews	Not included

Comparisons are factual statements about our own offering. We do not name or endorse any specific competitor.

Frequently Asked Questions — Data Analytics

Is data analytics a good career in Nagpur in 2026?

Data analytics is among the fastest-growing roles in Indian IT services, banking, healthcare, and e-commerce. Nagpur-based companies and MNCs with Nagpur offices actively hire analysts with SQL, Python, and Power BI skills. The role bridges business and technology, making it one of the most accessible high-paying careers for graduates without a pure CS background.

What is the fee for the Data Analytics course in Nagpur?

Fees are shared during a personal advisor call. EMI options and batch-specific pricing are available. Connect via WhatsApp or the enquiry form — an advisor will share the complete fee structure within one working day along with upcoming batch schedules.

Can a non-IT graduate learn Data Analytics?

Absolutely. Data analytics is one of the most non-IT-friendly tech careers. B.Com, B.Sc. Maths, MBA, and economics graduates often have a statistical intuition advantage over pure CS students. The course starts with Excel and builds up to Python and SQL — all from scratch, no programming experience needed.

Does this course cover Python for data analytics?

Yes. Weeks 4–6 cover Python extensively: pandas for data manipulation, numpy for numerical computing, matplotlib and seaborn for visualisation, and scipy.stats for statistical testing. All Python work is done in Jupyter Notebooks — the standard environment for data analytics in industry.

Is Power BI covered in this data analytics course?

Yes. Week 7 is dedicated entirely to Power BI Desktop — importing data, Power Query transformation, building a star schema data model, writing DAX measures, and publishing dashboards to Power BI Service. The capstone project includes a Power BI dashboard as a mandatory deliverable.

Is this data analytics course available offline in Nagpur?

Yes. Offline classes at our Wardha Road centre in Nagpur, with online live Zoom and hybrid options. Weekend batches run Saturday–Sunday for working professionals and students with weekday commitments.

What certificate do I get after the Data Analytics course?

You receive a Course Completion Certificate from Skilleze Technologies after completing all modules and submitting the capstone project. The certificate includes your name, course title, completion date, and a verifiable reference number — suitable for LinkedIn and resume.

Do I need statistics knowledge to start this data analytics course?

No. Week 2 covers statistics from scratch: descriptive statistics, distributions, correlation, and hypothesis testing are all taught with practical examples. You do not need prior statistics study — a willingness to engage with numbers and interpret results is enough to begin.

How long does the Data Analytics course take?

The course runs for 8 weeks with approximately 8 hours of instruction per week — a total of 64 hours. Weekday batches run two hours per session; the weekend batch covers four hours each on Saturday and Sunday. Capstone project work adds an additional 10–15 hours outside class.