Data Analytics Course in Noida with 100% Placement Assistance
Our data analytics course in Noida, in Sector 2, teaches Python, SQL, Power BI, and Tableau on real datasets, not slide decks. You’ll clean messy data, build dashboards, and run the same analysis a working analyst runs on day one.
Beginners and working professionals both join this program. Either way, you leave with a project portfolio, a certificate, and 100% placement assistance that runs until you’re placed, not until the course ends.
Certification Partners
Why Choose Our Data Analytics Course in Noida?
Our Students Placed In These Companies
Data Analytics with AI Course Syllabus
ADVANCE PYTHON PROGRAMMING
1. Introduction to Python
- Simple
- Open Source
- High Level Programming
- Portable
- Object and Procedure Oriented
- Easy to Maintain
2. Input and Output Functions
- The Input() function
- The print() function
n use of % percent operator
n use of format()
3. Variable and Data Types
- What is a Variable
- Assign Values to variable
- Typecasting
- Data Types in Python
n Numeric
n String
n Boolean
n Componup
n List
n Tuple
n Set
n FrozenSet
n Dictionary
4. Operators in Python
- Types of Operators
n Arithmetic Operators
n Comparison Operators
n Assignment Operators
n Logical Operators
n Bitwise Operators
n Identity Operators
n Membership Operators
n Operators Associativity
n Operator Precedence
n BEDMAS
5. Conditional Statements in Python
- The if Statement
- if- else statement
- The elif Statement
- Nested if-else ladder
6. Loops in Python
- Use of while loop
- use of for loop
- range) function
- arange) function
- The break Statement
- The continue Statement
- The pass statement
7. User defined functions in python
- Define a function
- calling a function
- Types of function
- UDF
- Function Arguments
- Functions Parameters
- Anonymous Function
- Global and Local Variable
- lambda
- map
- reduce
- Filter
- Mathematical Function
- Trigonometric Function
- Random Function
8. Strings in python
- creating strings
- difference between “” &”
- creating multiline comment
- basic string operations
- creating slices in strings
- String Built-in Functions
- n capitalize()
- n upper()
- n lower()
- n isalnum()
- n isalpha()
- n isnumeric()
- n isdecimal()
- n islower()
- n isupper()
9. List
- Access List items
- Change List items
- Add List Items
- Remove List Items
- Loop List
- List Comprehension
- Copy List
- Join List
- List built-in Method
- n append()
- n count()
- n extend()
- nreverse()
- n sort()
10. Tuple
- Access Tuples
- Update Tuples
- UnpackTuples
- LoopTuple
- JoinTuples
- Tuplebuilt-in Methods
- n index()
- n count()
11. Sets
- Create Sets
- Access Set items
- Add Set items
- Remove Set items
- Loop Sets
- Join Sets
12. Dictionaries
- Create Dictionaries
- Access Dictionary items
- Change Dictionary items
- Add Dictionary items
- Remove Dictionary items
- Loop Dictionaries
- Copy Dictionaries
- Nested Dictionaries
- Dictionary built-in Methods
- n keys()
- n values()
- n items()
- n get()
13. Python Modules
- Date Module
- Time Module
- os Module
- The import statement
- The from…import Statement
ITERATORS- GENERATORS
PYTHON AND DATA ANALYTICS LIBRARY
Introduction to Advance EXCEL
Referencing, Named ranges,Uses, Arithemetic Functions
Logical functions
Referring data from different tables: Various types of Lookup, Nested IF
Advanced functions
Date and Text Functions
Data Handling::Data cleaning, Data type identification, Remove Duplicates, Formatting and Filtering
Data Visualization: Conditional Formatting, Charts
what if Analysis?
Data Summarization: Pivot Report and Charts
Data Summarization: Dashboard Creation, Tips and Tricks
Connecting to Data: Power Query, Pivot, Power Pivot within Excel
Connecting to Data: Power Query, Pivot, Power Pivot within Excel
Introduction to SQL
SQL Commands and Data Types
DQL & Operators
Case When Then and Handling NULL Values
Group Operations & Aggregate Functions
Constraints
Joins
DDL
DML & TCL Commands
Indexes and Views
Stored Procedures
Function, Constructs
Union, Intersect, Sub-query
Exception Handling
Triggers
Power BI Introduction and Installation
The Power BI user interface, including types of data sources and visualizations
Sample dashboard with Animation Visual
Power BI Visualization
Power Query Editor
Modelling with Power BI
Power Query Editor Filter Data
Customize the data in Power BІ
Dax Expressions
Custom Visual
Power BI Service
Descriptive Statistics
Probability and Normal Distribution
Inferential Statistics
Data cleaning and Insights
Al Tools for Data Analytics & Visualization
DATA ANALYTICS PROJECTS:
ADVANCE PYTHON PROGRAMMING
1. Introduction to Python
- Simple
- Open Source
- High Level Programming
- Portable
- Object and Procedure Oriented
- Easy to Maintain
2. Input and Output Functions
- The Input() function
- The print() function
n use of % percent operator
n use of format()
3. Variable and Data Types
- What is a Variable
- Assign Values to variable
- Typecasting
- Data Types in Python
n Numeric
n String
n Boolean
n Componup
n List
n Tuple
n Set
n FrozenSet
n Dictionary
4. Operators in Python
- Types of Operators
n Arithmetic Operators
n Comparison Operators
n Assignment Operators
n Logical Operators
n Bitwise Operators
n Identity Operators
n Membership Operators
n Operators Associativity
n Operator Precedence
n BEDMAS
5. Conditional Statements in Python
- The if Statement
- if- else statement
- The elif Statement
- Nested if-else ladder
6. Loops in Python
- Use of while loop
- use of for loop
- range) function
- arange) function
- The break Statement
- The continue Statement
- The pass statement
7. User defined functions in python
- Define a function
- calling a function
- Types of function
- UDF
- Function Arguments
- Functions Parameters
- Anonymous Function
- Global and Local Variable
- lambda
- map
- reduce
- Filter
- Mathematical Function
- Trigonometric Function
- Random Function
8. Strings in python
- creating strings
- difference between “” &”
- creating multiline comment
- basic string operations
- creating slices in strings
- String Built-in Functions
- n capitalize()
- n upper()
- n lower()
- n isalnum()
- n isalpha()
- n isnumeric()
- n isdecimal()
- n islower()
- n isupper()
9. List
- Access List items
- Change List items
- Add List Items
- Remove List Items
- Loop List
- List Comprehension
- Copy List
- Join List
- List built-in Method
- n append()
- n count()
- n extend()
- nreverse()
- n sort()
10. Tuple
- Access Tuples
- Update Tuples
- UnpackTuples
- LoopTuple
- JoinTuples
- Tuplebuilt-in Methods
- n index()
- n count()
11. Sets
- Create Sets
- Access Set items
- Add Set items
- Remove Set items
- Loop Sets
- Join Sets
12. Dictionaries
- Create Dictionaries
- Access Dictionary items
- Change Dictionary items
- Add Dictionary items
- Remove Dictionary items
- Loop Dictionaries
- Copy Dictionaries
- Nested Dictionaries
- Dictionary built-in Methods
- n keys()
- n values()
- n items()
- n get()
13. Python Modules
- Date Module
- Time Module
- os Module
- The import statement
- The from…import Statement
ITERATORS- GENERATORS
PYTHON AND DATA ANALYTICS LIBRARY
Introduction to Advance EXCEL
Referencing, Named ranges,Uses, Arithemetic Functions
Logical functions
Referring data from different tables: Various types of Lookup, Nested IF
Advanced functions
Date and Text Functions
Data Handling::Data cleaning, Data type identification, Remove Duplicates, Formatting and Filtering
Data Visualization: Conditional Formatting, Charts
what if Analysis?
Data Summarization: Pivot Report and Charts
Data Summarization: Dashboard Creation, Tips and Tricks
Connecting to Data: Power Query, Pivot, Power Pivot within Excel
Introduction to SQL
SQL Commands and Data Types
DQL & Operators
Case When Then and Handling NULL Values
Group Operations & Aggregate Functions
Constraints
Joins
DDL
DML & TCL Commands
Indexes and Views
Stored Procedures
Function, Constructs
Union, Intersect, Sub-query
Exception Handling
Triggers
Power BI Introduction and Installation
The Power BI user interface, including types of data sources and visualizations
Sample dashboard with Animation Visual
Power BI Visualization
Power Query Editor
Modelling with Power BI
Power Query Editor Filter Data
Customize the data in Power BІ
Dax Expressions
Custom Visual
Power BI Service
Introduction to Tableau
Basic Charts & Formatting
Filters
Calculations
Data Combining Techniques
Custom Charts
Groups, Bins, Hierarchies, Sets, Parameters
Analytics & Dashboard
Dashboard Actions & Tableau Public
Descriptive Statistics
Probability and Normal Distribution
Inferential Statistics
Data cleaning and Insights
Al Tools for Data Analytics & Visualization
DATA ANALYTICS PROJECTS:
Our Placed Students
Khushi Shaho
Web Developer
Placed at
Jay Prakash
Cyber Security Consultant
Placed at
Shristi Kumari
Software Developer
Placed at
Mukul Chauhan
Java Developer
Placed at
Tools That You will Learn
Excel
SQL
Power BI
Numpy
Pandas
Matplotlib
Seaborn
Jupyter
Julius
Colab
Claude
ChatGPT
Tableau
Copilot
25+ Data Analytics Skills You Will Learn
- Data Cleaning
- Data Collection
- Data Wrangling
- Data Analysis
- Data Visualization
- Statistical Analysis
- Exploratory Data Analysis (EDA)
- Report Generation
- Business Intelligence (BI)
- Dashboard Creation
- Data Interpretation
- Data Validation
- Predictive Analytics
- Data Storytelling
- KPI Analysis
- Problem Solving
- Critical Thinking
- Business Analysis
- Data-Driven Decision Making
- Requirement Analysis
- Trend Analysis
- Customer Analytics
- Financial Analytics
- Sales Analytics
- Marketing Analytics
Our Process
What data analytics actually means
If you’re comparing data analytics courses in Noida right now, start with the basics. Data analytics is the process of cleaning, structuring, and interpreting data so a business can make a decision instead of a guess. A retail chain uses it to find out which store is losing money. A hospital uses it to predict bed occupancy next week. A bank uses it to catch fraud before a transaction clears.
None of that needs a PhD. It needs SQL to pull the data, Excel or Python to clean it, and a BI tool like Power BI or Tableau to make it readable for someone who isn’t a data person. That’s the job, in practice.
Companies across Noida and the wider NCR, from IT services firms to fintech startups, hire for exactly this skill set. Every team that touches a spreadsheet eventually needs someone who can turn it into an answer, and that demand isn’t slowing down.
Why train with Appwars Technologies
We’ve run data analytics batches in Noida for years, and it shows in how the course is built. Every instructor has worked as an analyst or BI developer before teaching, so the syllabus reflects what a hiring manager tests for, not just what fits neatly into a slide.
Batches stay small: 15 students, offline and online, taught live by the same instructor, so nobody gets lost in a recorded video queue.
You’ll work in the tools a working analyst opens every day: Excel, SQL, Python, Power BI, and Tableau, on lab machines in our Sector 2 center. Every project mirrors a real business problem: a sales dashboard, an HR attrition report, or a supply chain bottleneck, not a toy dataset from a textbook.
How the course is structured
The program runs 5 or 6 months, in two stages.
Stage one covers the fundamentals: Python programming, Excel from formulas through Power Query, and SQL from SELECT statements through window functions and stored procedures.
Stage two moves into the tools that turn raw numbers into a dashboard someone can act on: Power BI, DAX, and Tableau, plus a statistics module covering hypothesis testing, probability, and the AI tools (ChatGPT, Claude, and Julius AI) analysts now use to speed up reporting.
The full module-by-module syllabus is above. Every stage ends with a project you can put on your résumé.
The projects you’ll actually build
You won’t just watch us build dashboards. You’ll build seven of your own: a sales dashboard, an HR dashboard, a finance dashboard, an e-commerce dashboard, a supply chain dashboard, an employee database analysis, and a retail sales analysis.
Each one uses a dataset close to what you’d see on the job: messy, incomplete, and in need of real cleaning before it’s useful. A tutorial dataset that’s already clean teaches you nothing about the first two hours of a real analyst’s day.
By the time you finish, you’ll have a portfolio you can walk an interviewer through, not just a certificate. For more project ideas to practice on your own, see our guide to the top data analytics projects for your résumé.
Career paths and salary after the course
Graduates move into roles like Data Analyst, Business Intelligence Analyst, Reporting Analyst, and, with more experience, Data Scientist or Data Engineer. IT services firms, fintech, e-commerce, and healthcare companies across Delhi NCR hire for all four.
Pay depends heavily on your tool stack, not just your job title. A fresher who knows only Excel starts near the bottom of the range. A fresher who can show SQL, Python, and 2 to 3 real dashboard projects starts meaningfully higher.
We break down the exact numbers, by experience level and city, in our data analyst salary guide. Use it to check any job offer against real market data instead of a course brochure’s promise.
Where our students land
Our placement team works with companies across IT services, product, and fintech to place data analytics graduates in the roles they trained for: Data Analyst, Business Intelligence Analyst, and reporting roles at MNCs and growing startups across the NCR.
Placement support runs until you’re placed, not until the course ends. That includes mock interviews, résumé reviews built around ATS scoring, and introductions to our hiring partners.
See our full placements page for the companies recent batches have joined.
How to enroll
No degree or coding background is required. Basic comfort with Excel helps, but the course starts from the fundamentals either way.
- Fill out the enquiry form on this page or call +91 9911169001.
- Our academic counsellor walks you through batch timing, fees, and the 5 or 6 month options, and answers any syllabus questions.
- Pick your batch and confirm your seat. Batches run small (15 students), so seats fill fast once a batch opens.
Check upcoming batch dates for the next start date, offline or online.
Support that continues after class
Every batch gets an internship certificate for the hands-on project work, on top of the course completion certificate.
You also get study material, recorded sessions for revision, and access to instructors after the batch ends if a concept needs a second look. Mentors have worked as analysts and BI developers, so questions get answered by someone who’s done the job, not just taught it.
Career support runs on the same timeline as placement: resume building, mock interviews, and referrals to hiring partners, for as long as it takes to land a role.
Who this course is for
- Graduates in any stream (B.E, B.Tech, MCA, BCA, B.Com) who want a career in data analytics or business intelligence
- IT professionals looking to move into an analytics or BI-focused role
- Professionals from non-IT backgrounds making a switch into tech
- Anyone restarting their career after a gap
- Working professionals who want to add Power BI, Tableau, or SQL to an existing skill set
Get Certified to boost your Professional Growth
Data Analytics Course with AI Course Fees
- 15 Students Per Batch
- Live offline & online Classes from Industry Expert
- Hands-on curriculum with Real-Life Projects
- 15 Students Per Batch
- Live offline & online Classes from Industry Expert
- Hands-on curriculum with Real-Life Projects
Got more questions?
Talk to our team directly
Contact us and our academic counsellor will get in touch with you shortly
