Data Analytics Course in Noida with 100% Placement Assistance
Unlock your career potential with our Data Analytics Course in Noida — designed to turn beginners into industry-ready professionals! Learn the most in-demand tools like Python, SQL, Power BI, and Tableau through hands-on projects and real-world case studies. Our expert mentors guide you step-by-step to master data visualization, statistical analysis, and predictive modeling. With 100% placement assistance, we help you land your dream job in top MNCs and startups. Whether you’re a student or a working professional, this program is your gateway to a rewarding career in data analytics. Join today and transform your future!
Certification Partners
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
- TypecastingD
- 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 PPWARS
- 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
- SortList
- Copy List
- Join List
- List built-in Method
- n append()
- n count()
- n extend()
- n reverse()
- 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 item
- 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
Object Oriented Programming
- Class
- Object
- Inheritance
- Polymorphism
- Overloading
- Overriding
- Abstraction
- Encapsulation
- Web scraping
PYTHON AND DATA ANALYTICS LIBRARY
1. NumPУ
- Creating arrays
- Array indexing
- Array slicing
- Numpy Data Types
- Copy vs View
- Array Shape
- Array reshape
- Iterating
- Join
- Search
- Filter
- Split
- Sort
2. Pandas
- Pandas Series
- Pandas Data Frame
- Read CSV
- Read JSON
- Cleaning Data
- Missing Value Handling
- Optimizing Data Format
- Redundancy Minimization
- The corr) function
- Plotting Graphs in pandas
3. Matplotlib
- Welcome to the Data Visualization Section
- Introduction to Matplotlib
- Matplotlib Part 1
- Matplotlib Part 2
- Matplotlib Part
4. Seaborn
- Introduction to Seaborn
- Distribution Plots
- Categorical Plots
- Matrix Plots
- Grids
- Regression Plots
- Style and Color
5. WEB SCRAPING
- Scraping Webpages
- Beautifulsoup package
- Real time project APP
Introduction to EXCEL
- MS office Versions(similarities and differences)
- Interface(latest available version)
- Row and Columns
- Keyboard shortcuts for easy navigation
- Data Entry(Fill series)
- Find and Select
- Clear Options
- Ctrl+Enter
- Formatting options(Font,Alignment,Clipboard(copy, paste special))
Referencing, Named ranges,Uses, Arithemetic Functions
- Mathematical calculations with Cell
- referencing(Absolute,Relative,Mixed)
- Functions with Name Range
- Arithmetic functions
- (SUM,SUMIF,SUMIFS,COUNT,COUNTA,COUNTIFS, AVERAGE,AVER
- AGEIFS,MAX,MAXIFS,MIN,MINIFS)
Logical functions
- Logical functions:IF,AND,OR,NESTED IFS,NOT,IFERROR
- Usage of Mathematical and Logical functions nested
- together
Referring data from different tables: Various types of Lookup, Nested IF
- LOOKUP
- VLOOKUP
- NESTED VLOOKUP
- HLOOKUP
- INDEX
- INDEX WITH MATCH FUNCTION
- INDIRECТ
- OFFSET
Advanced functions
- Combination of Arithmatic
- Logical
- Lookup functions
- Data Validation(with Dependent drop down)
Date and Text Functions
- Date Functions:DATE,DAY,MONTH, YEAR,YEARFRAC,DATEDIFF, EOMONTH
- b.text Functions:TEXT,UPPER,LOWER,PROPER,LEFT,RIGHT,SEARCH,FIND, MID,TT
- C, Flash Fill
Data Handling::Data cleaning, Data type identification, Remove Duplicates, Formatting and Filtering
- Number Formatting(with shortcuts)
- CTRL+T(Converting into an Excel Table)
- Formatting Table
- Remove Duplicate
- SORT
- Advanced Sort
- FILTER
- Advanced Filter
Data Visualization: Conditional Formatting, Charts
- Conditional formatting(icon sets/Highlighted colour sets/Data bars/custom formatting)
- Charts:Bar,Column,Lines, Scatter,Combo,Gantt, Waterfall,pie
what if Analysis?
- Scenario Manager
- Goal Seek
- Data Tables
Data Summarization: Pivot Report and Charts
- Pivot Reports:Insert,Interface,Crosstable Reports;Filter, Pivot Charts,
- Slicers:Add,Connect to multiple reports and charts
- Calculated field, Calculated item
Data Summarization: Dashboard Creation, Tips and Tricks
- Dashboard:Types,Getting reports and charts together, Use of Slicers.
- Design and placement: Formatting of Tables, Charts,Sheets, Proper use of Colours and Shapes
Connecting to Data: Power Query, Pivot, Power Pivot within Excel
- Power Query: Interface, Tabs
- Connecting to data from other excel files, text files, other sources
- Data Cleaning
- Transforming
- Loading Data into Excel Query
Introduction to SQL
- Introduction to Databases
- Introduction to RDBMS
- Explain RDBMS through normalization
- Different types of RDBMS
- Software Installation(MySQL Workbench)
SQL Commands and Data Types
- Types of SQL Commands (DDL,DML,DQL,DCL,TCL) and their applications
- Data Types in SQL (Numeric, Char, Datetime)
DQL & Operators
- SELECT
- LIMIT
- DISTINСТ
- WHERE AND
- OR
- IN
- NOT IN
- BETWEEN
- EXIST
- ISNULL
- IS NOT NULL
- Wild Cards
- ORDER BY
Case When Then and Handling NULL Values
- Usage of Case When then to solve logical problems and
handling NULL Values (IFNULL, COALESCE)
Group Operations & Aggregate Functions
- Group By
- Having Clause
- COUNT
- SUM
- AVG
- MIN
- MAX
- COUNT String Functions
- Date & Time Function
Constraints
- NOT NULL
- UNIQUE
- o CHECСК
- DEFAULT
- Primary key
- Foreign Key (Both at column level and table level)
Joins
- Inner
- Left
- Right
- Cross
- Self Joins
- Full outer join
DDL
- Create
- Drop
- Alter
- Rename
- Truncate
- Modify
- Commentnt
DML & TCL Commands
- DML
o Insert
o Update & Delete
- TCL
o Commit
o Rollback
o Savepoint
o Data Partitioning
Indexes and Views
- Indexes (Different Type of Indexes)
- Views in SQL
Stored Procedures
- Procedure with IN Parameter
- Procedure with OUT parameter
- Procedure with INOUT parameter
Function, Constructs
- User Define Function
- Window Functions
- Rank
- Dense Rank
- Lead
- Lag
- Row_number
Union, Intersect, Sub-query
- Union, Union all
- Intersect
- Sub Queries, Multiple Query
Exception Handling
- Handling Exceptions in a query
- CONTINUE Handler
- EXIT handler
Triggers
- Triggers – Before | After DML Statement
Introduction Power BI
- Unerstanding Power BI Background
- Installation of Power Bl and check list for perfect installation
- Formatting and Setting prerequisits
- Understanding the difference between Power BI desktop & Power Query
The Power BI user interface, including types of data sources and visualizations
- Getting familiar with the interface BI Query & Desktop
- Understanding type of Visualisation
- Loading data from multiple sources
- Data type and the type of default chart on drag drop.
- Geo location Map integration
Sample dashboard with Animation Visual
- Fnanical sample data in Power BI
- Preparing sample dashboard as get started
- Map visual Types and usages in different variation
- Understanding scatter Plot chart with Play axis and the parameters
Power BI Visualization
- Understanding Column Chart
- Understanding Line Chart
- Implementation of Conditional formating
- Implementation of Formating techniques
Power Query Editor
- Loading data from folder
- Understanding Power Query in detail
- Promote header, Split to limiter, Add columns, append, merge queries etc
Modelling with Power BI
- Loading multiple data from different format
- Understanding modelling (How to create relationship)
- Connection type, Data cardinality, Filter direction
- Making dashboard using new loaded data
Power Query Editor Filter Data
- Power Query Custom Column & Conditional Column
- Manage Parameter
- Introduction to Filter and types of filter
- Trend analysis, Future forecast
Customize the data in Power BІ
- Understanding Tool tip with information
- Use and understanding of Drill Down
- Visual interaction and customisation of visual interaction
- Drill through function and usage
- Button triggers
- Bookmark and different use and implementation
- Navigation buttons
Dax Expressions
- Introduction to DAX
- Table Dax, Calculated column, DAX measure and difference
- Eg:- Calendar, Calendar auto, Summarize, Group by etc
- Calculated Column
- Related, Lookup value, switch, Datedif, Rankx, Date functions
- Dax Measure and Quick Measure
- Remove filters, Keep filters, All, Allselected, Time Intelligence
- Functions,Rolling average,YoY, Running total
Custom Visual
- Custom visual and understanding the use of custom
- Loading custom visual, Pinning visual
- Loading to template for future use
- Publishinhg Power Bi
Power BI Service
- Introduction to app.powerbi.com
- Schedule refresh
- Data flow and use power bi from online
- Download data as live in power point and more
Descriptive Statistics
- Data Types, Measure Of central tendency, Measures of Dispersion
- Graphical Techniques, Skewness & Kurtosis, Box Plot
Probability and Normal Distribution
- Random Variable, Probability, Probility Distribution, Normal Distribution, SND, Expected Value
Inferential Statistics
- Sampling Funnel, Sampling Variation, Central Limit Theorem, Confidence interval
- Introduction to Hypothesis Testing
- Hypothesis Testing (2 proportion test, 2 t sample t test)
- Anova and Chisquare
Data cleaning and Insights
- Data Cleaning (Invalid cells,Blanks, Outliers,Null values)
- Imputation Techniques(Mean and Median)
- Scatter Diagram
- Correlation Analysis
Al Tools for Data Analytics & Visualization
- Julius Al – Al-powered data analysis and visualization assistant
- Quadratic Al – Spreadsheet + Al tool for analytics and calculations
- ChatGPT – Al assistant for DAX, SQL, and analytics learning
- Claude – Al assistant for documentation and analysis support
DATA ANALYTICS PROJECTS:
Sales Dashboard
HR Dashboard
Finance Dashboard
E-commerce Dashboard
Supply Chain Dashboard
Employee Database Analysis
Retail sales Analysis
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
- TypecastingD
- 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 PPWARS
- 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
- SortList
- Copy List
- Join List
- List built-in Method
- n append()
- n count()
- n extend()
- n reverse()
- 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 item
- 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
Object Oriented Programming
- Class
- Object
- Inheritance
- Polymorphism
- Overloading
- Overriding
- Abstraction
- Encapsulation
- Web scraping
PYTHON AND DATA ANALYTICS LIBRARY
1. NumPУ
- Creating arrays
- Array indexing
- Array slicing
- Numpy Data Types
- Copy vs View
- Array Shape
- Array reshape
- Iterating
- Join
- Search
- Filter
- Split
- Sort
2. Pandas
- Pandas Series
- Pandas Data Frame
- Read CSV
- Read JSON
- Cleaning Data
- Missing Value Handling
- Optimizing Data Format
- Redundancy Minimization
- The corr) function
- Plotting Graphs in pandas
3. Matplotlib
- Welcome to the Data Visualization Section
- Introduction to Matplotlib
- Matplotlib Part 1
- Matplotlib Part 2
- Matplotlib Part
4. Seaborn
- Introduction to Seaborn
- Distribution Plots
- Categorical Plots
- Matrix Plots
- Grids
- Regression Plots
- Style and Color
5. WEB SCRAPING
- Scraping Webpages
- Beautifulsoup package
- Real time project APP
Introduction to EXCEL
- MS office Versions(similarities and differences)
- Interface(latest available version)
- Row and Columns
- Keyboard shortcuts for easy navigation
- Data Entry(Fill series)
- Find and Select
- Clear Options
- Ctrl+Enter
- Formatting options(Font,Alignment,Clipboard(copy, paste special))
Referencing, Named ranges,Uses, Arithemetic Functions
- Mathematical calculations with Cell
- referencing(Absolute,Relative,Mixed)
- Functions with Name Range
- Arithmetic functions
- (SUM,SUMIF,SUMIFS,COUNT,COUNTA,COUNTIFS, AVERAGE,AVER
- AGEIFS,MAX,MAXIFS,MIN,MINIFS)
Logical functions
- Logical functions:IF,AND,OR,NESTED IFS,NOT,IFERROR
- Usage of Mathematical and Logical functions nested
- together
Referring data from different tables: Various types of Lookup, Nested IF
- LOOKUP
- VLOOKUP
- NESTED VLOOKUP
- HLOOKUP
- INDEX
- INDEX WITH MATCH FUNCTION
- INDIRECТ
- OFFSET
Advanced functions
- Combination of Arithmatic
- Logical
- Lookup functions
- Data Validation(with Dependent drop down)
Date and Text Functions
- Date Functions:DATE,DAY,MONTH, YEAR,YEARFRAC,DATEDIFF, EOMONTH
- b.text Functions:TEXT,UPPER,LOWER,PROPER,LEFT,RIGHT,SEARCH,FIND, MID,TT
- C, Flash Fill
Data Handling::Data cleaning, Data type identification, Remove Duplicates, Formatting and Filtering
- Number Formatting(with shortcuts)
- CTRL+T(Converting into an Excel Table)
- Formatting Table
- Remove Duplicate
- SORT
- Advanced Sort
- FILTER
- Advanced Filter
Data Visualization: Conditional Formatting, Charts
- Conditional formatting(icon sets/Highlighted colour sets/Data bars/custom formatting)
- Charts:Bar,Column,Lines, Scatter,Combo,Gantt, Waterfall,pie
what if Analysis?
- Scenario Manager
- Goal Seek
- Data Tables
Data Summarization: Pivot Report and Charts
- Pivot Reports:Insert,Interface,Crosstable Reports;Filter, Pivot Charts,
- Slicers:Add,Connect to multiple reports and charts
- Calculated field, Calculated item
Data Summarization: Dashboard Creation, Tips and Tricks
- Dashboard:Types,Getting reports and charts together, Use of Slicers.
- Design and placement: Formatting of Tables, Charts,Sheets, Proper use of Colours and Shapes
Connecting to Data: Power Query, Pivot, Power Pivot within Excel
- Power Query: Interface, Tabs
- Connecting to data from other excel files, text files, other sources
- Data Cleaning
- Transforming
- Loading Data into Excel Query
Introduction to SQL
- Introduction to Databases
- Introduction to RDBMS
- Explain RDBMS through normalization
- Different types of RDBMS
- Software Installation(MySQL Workbench)
SQL Commands and Data Types
- Types of SQL Commands (DDL,DML,DQL,DCL,TCL) and their applications
- Data Types in SQL (Numeric, Char, Datetime)
DQL & Operators
- SELECT
- LIMIT
- DISTINСТ
- WHERE AND
- OR
- IN
- NOT IN
- BETWEEN
- EXIST
- ISNULL
- IS NOT NULL
- Wild Cards
- ORDER BY
Case When Then and Handling NULL Values
- Usage of Case When then to solve logical problems and
handling NULL Values (IFNULL, COALESCE)
Group Operations & Aggregate Functions
- Group By
- Having Clause
- COUNT
- SUM
- AVG
- MIN
- MAX
- COUNT String Functions
- Date & Time Function
Constraints
- NOT NULL
- UNIQUE
- o CHECСК
- DEFAULT
- Primary key
- Foreign Key (Both at column level and table level)
Joins
- Inner
- Left
- Right
- Cross
- Self Joins
- Full outer join
DDL
- Create
- Drop
- Alter
- Rename
- Truncate
- Modify
- Commentnt
DML & TCL Commands
- DML
o Insert
o Update & Delete
- TCL
o Commit
o Rollback
o Savepoint
o Data Partitioning
Indexes and Views
- Indexes (Different Type of Indexes)
- Views in SQL
Stored Procedures
- Procedure with IN Parameter
- Procedure with OUT parameter
- Procedure with INOUT parameter
Function, Constructs
- User Define Function
- Window Functions
- Rank
- Dense Rank
- Lead
- Lag
- Row_number
Union, Intersect, Sub-query
- Union, Union all
- Intersect
- Sub Queries, Multiple Query
Exception Handling
- Handling Exceptions in a query
- CONTINUE Handler
- EXIT handler
Triggers
- Triggers – Before | After DML Statement
Introduction Power BI
- Unerstanding Power BI Background
- Installation of Power Bl and check list for perfect installation
- Formatting and Setting prerequisits
- Understanding the difference between Power BI desktop & Power Query
The Power BI user interface, including types of data sources and visualizations
- Getting familiar with the interface BI Query & Desktop
- Understanding type of Visualisation
- Loading data from multiple sources
- Data type and the type of default chart on drag drop.
- Geo location Map integration
Sample dashboard with Animation Visual
- Fnanical sample data in Power BI
- Preparing sample dashboard as get started
- Map visual Types and usages in different variation
- Understanding scatter Plot chart with Play axis and the parameters
Power BI Visualization
- Understanding Column Chart
- Understanding Line Chart
- Implementation of Conditional formating
- Implementation of Formating techniques
Power Query Editor
- Loading data from folder
- Understanding Power Query in detail
- Promote header, Split to limiter, Add columns, append, merge queries etc
Modelling with Power BI
- Loading multiple data from different format
- Understanding modelling (How to create relationship)
- Connection type, Data cardinality, Filter direction
- Making dashboard using new loaded data
Power Query Editor Filter Data
- Power Query Custom Column & Conditional Column
- Manage Parameter
- Introduction to Filter and types of filter
- Trend analysis, Future forecast
Customize the data in Power BІ
- Understanding Tool tip with information
- Use and understanding of Drill Down
- Visual interaction and customisation of visual interaction
- Drill through function and usage
- Button triggers
- Bookmark and different use and implementation
- Navigation buttons
Dax Expressions
- Introduction to DAX
- Table Dax, Calculated column, DAX measure and difference
- Eg:- Calendar, Calendar auto, Summarize, Group by etc
- Calculated Column
- Related, Lookup value, switch, Datedif, Rankx, Date functions
- Dax Measure and Quick Measure
- Remove filters, Keep filters, All, Allselected, Time Intelligence
- Functions,Rolling average,YoY, Running total
Custom Visual
- Custom visual and understanding the use of custom
- Loading custom visual, Pinning visual
- Loading to template for future use
- Publishinhg Power Bi
Power BI Service
- Introduction to app.powerbi.com
- Schedule refresh
- Data flow and use power bi from online
- Download data as live in power point and more
Introduction to Tableau
- What is Tableau?
- What is Data Visulaization?
- Tableau Products
- Tableau Desktop Variations
- Tableau File Extensions
- Data Types, Dimensions, Measures, Aggregation concept
- Tableau Desktop Installation
- Data Source Overview
- Live Vs Extract
Basic Charts & Formatting
- Overview of worksheet sections
- Shelves
- Bar Chart, Stacked Bar Chart
- Discrete & Continuous Line Charts
- Symbol Map & Filled Map
- Text Table, Highlight Table
- Formatting: Remove grid lines, hiding the axes, conversion of numbers to thousands, millions, Shading, Row divider, Column divider
- Marks Card
Filters
- What are Filters?
- Types of Filters
- Extract, Data Source, Context, Dimension, Measure, Quick Filters
- Order of operation of filters
- Cascading
- Apply to Worksheets
Calculations
- Need for calculations
- Types: Basic, LOD’s, Table
- Examples of Basic Calculations: Aggregate functions, Logical functions, String functions, Tablea calculation functions, numerical functions, Date functions
- LOD’s: Examples
- Table Calculations: Examples
Data Combining Techniques
- What is Data Combining Techniques?
- Types
- Joins, Relationships, Blending & Union
Custom Charts
- Dual Axis
- Combined Axis
- Donut Chart
- Lollipop Chart
- KPI Cards (Simple)
- KPI Cards (With Shape)
Groups, Bins, Hierarchies, Sets, Parameters
- What are Groups ? Purpose
- What are Bins ? Purpose
- What are Hierarchies ? Purpose
- What are Sets ? Purpose
- What are Parameters ? Purpose and examples
Analytics & Dashboard
- Reference Lines
- Trend Line
- Overview of Dashboard: Tiled Vs Floating
- All Objects overview, Layout overview
- Dashboard creation with formatting
Dashboard Actions & Tableau Public
- Actions: Filter, Highlight, URL, Sheet, Parameter, Set
- How to save the workbook to Tableau Public website?
Descriptive Statistics
- Data Types, Measure Of central tendency, Measures of Dispersion
- Graphical Techniques, Skewness & Kurtosis, Box Plot
Probability and Normal Distribution
- Random Variable, Probability, Probility Distribution, Normal Distribution, SND, Expected Value
Inferential Statistics
- Sampling Funnel, Sampling Variation, Central Limit Theorem, Confidence interval
- Introduction to Hypothesis Testing
- Hypothesis Testing (2 proportion test, 2 t sample t test)
- Anova and Chisquare
Data cleaning and Insights
- Data Cleaning (Invalid cells,Blanks, Outliers,Null values)
- Imputation Techniques(Mean and Median)
- Scatter Diagram
- Correlation Analysis
Al Tools for Data Analytics & Visualization
- Julius Al – Al-powered data analysis and visualization assistant
- Quadratic Al – Spreadsheet + Al tool for analytics and calculations
- ChatGPT – Al assistant for DAX, SQL, and analytics learning
- Claude – Al assistant for documentation and analysis support
DATA ANALYTICS PROJECTS:
F5F5F5Sales Dashboard
HR Dashboard
Finance Dashboard
E-commerce Dashboard
Supply Chain Dashboard
Employee Database Analysis
Retail sales Analysis
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
Why Appwars is the Best Data Analytics Institute in Noida?
Our Process
Introduction to Data Analytics
Data analytics refers to the systematic computational analysis of data, an essential practice that has gained immense significance in today’s business landscape. Organizations are progressively leaning towards data-driven decision-making, utilizing vast amounts of data to derive insights that can propel their performance, enhance customer satisfaction, and streamline operations. This approach allows businesses to understand their market better, predict trends, and personalize customer experiences, ultimately leading to a more competitive edge.
The surge in digital information has rendered data analytics a crucial tool in various sectors, including finance, healthcare, retail, and technology. Skilled data analysts are now viewed as pivotal assets, enabling companies to transform raw data into meaningful information. As organizations continue to prioritize data as a core component within their workflows, the demand for proficient data analysts is witnessing substantial growth. This trend highlights the importance of investing in quality data analytics Course in Noida, which empowers individuals with necessary skills and knowledge.
Moreover, as the volume of data generated across digital platforms skyrockets, the need for sophisticated analytical skills has never been more pressing. Data analytics courses in Noida offer structured learning paths that cover essential topics such as statistical analysis, data visualization, and predictive modeling. By engaging in these training programs, participants can remain relevant in the fast-paced digital economy and utilize data to make informed, strategic business decisions.
In essence, the modern business environment implicitly relies on data analytics to drive success. By understanding the significance of data and honing their analytical skills through comprehensive courses, aspiring data analysts can position themselves to capitalize on vast career opportunities that await in this ever-evolving field.
Why Choose Appwars Technologies for Data Analytics course?
When considering data analytics Course in Noida, Appwars Technologies stands out for several compelling reasons that cater to both aspiring data analysts and seasoned professionals looking to upgrade their skills. One of the primary advantages of choosing Appwars is its extensive expertise in the field of data analytics. With years of experience in delivering high-quality educational programs, the institute has established itself as a frontrunner in Noida’s training landscape.
An essential attribute of Appwars Technologies is its team of experienced instructors. These professionals possess not only advanced academic qualifications but also practical experience in data analytics across various industries. This combination of knowledge allows them to impart valuable insights and real-world applications through their teaching. Students benefit from their expertise, and guidance fosters not only technical competencies but also critical thinking skills that are vital in the analytics domain.
The modern infrastructure at Appwars Technologies further enhances the learning experience. Equipped with state-of-the-art facilities and the latest software tools, the training environment reflects industry standards. This setup allows participants to work on current platforms and gain hands-on experience, which is instrumental in mastering data analytics concepts. Practical exposure is crucial in a field like data analytics, where familiarity with tools such as R, Python, and SQL can be the game-changer in one’s career progression.
Moreover, Appwars Technologies emphasizes real-world project exposure as an integral part of its data analytics course in Noida. Participants engage in projects that simulate actual industry scenarios, enabling them to apply theoretical knowledge to practical situations. Such experience not only enriches the learning journey but also makes candidates more attractive to potential employers in a competitive job market.
Data Analytics Course Curriculum Overview
Obtaining a strong foundation in data analytics is imperative for professionals pursuing careers in this rapidly evolving field. The data analytics course in noida offered by Appwars Technologies provides an extensive curriculum designed to equip students with the essential skills required to excel in the industry. The training encompasses a comprehensive range of modules that cover both theoretical knowledge and practical applications.
One of the primary modules in this data analytics course in Noida focuses on data analysis tools. Students will be introduced to various software applications such as Excel, R, and Python, which are crucial for data manipulation and visualization. Through hands-on practice, learners will understand how to gather, clean, and analyze data effectively, providing them with a strong base to build upon.
Another significant aspect of the curriculum is statistical techniques. Understanding statistical analysis is fundamental for interpreting data accurately. The course will delve into hypothesis testing, regression analysis, and other statistical methods that enable learners to derive meaningful insights from large data sets. This knowledge is vital for making data-driven decisions in any enterprise.
The integration of machine learning concepts further enhances the curriculum. Students will explore various algorithms, model training, and evaluation methods, which are essential for predictive analytics. By the end of this module, participants will be proficient in developing machine learning models that can adapt and provide predictions based on new data.
Ultimately, the training curriculum of the data analytics course in Noida is meticulously structured to ensure that students acquire a balanced mix of analytical skills, practical experience, and theoretical understanding, preparing them for an array of career opportunities in analytics and data science.
Experience and Projects of Data Analytics Course
The importance of practical experience in education cannot be overstated, especially in the field of data analytics. Appwars Technologies emphasizes a hands-on learning approach in its data analytics course in Noida, ensuring that students not only gain theoretical knowledge but also apply this knowledge in real-world scenarios. This methodology enhances the learning experience and prepares them for the challenges they will face in their careers.
During the training program, students engage in various projects that cover a wide range of topics within data analytics. These projects allow participants to work with actual datasets, conduct analyses, and derive meaningful insights. For instance, they may work on projects involving data cleaning, data visualization, or predictive modeling. By undertaking such hands-on assignments, students become adept at using tools and technologies that are prevalent in the industry.
Furthermore, the projects designed by Appwars Technologies are tailored to simulate real-world challenges. This practical approach helps students develop problem-solving skills necessary for a successful career in data analytics. They learn to interpret data trends, make informed decisions based on data, and communicate their findings effectively. By the end of the training program, students emerge with a robust portfolio that showcases their ability to tackle real-life analytical problems, making them highly attractive to potential employers.
Additionally, the collaborative nature of the projects facilitates teamwork and enhances communication skills, essential traits for a career in data analytics. This experiential learning strategy ensures a thorough understanding of concepts, fostering confidence in students as they transition from the classroom to the workplace. Overall, the blend of theoretical and practical training further solidifies the value of the data analytics course in Noida offered by Appwars Technologies, equipping students for the demands of the industry.
Career Opportunities After Data Analytics Course
The completion of a data analytics training program in Noida can significantly open doors to a multitude of career opportunities. As businesses increasingly rely on data to drive decision-making, the demand for skilled data analysts is surging across various sectors, including finance, healthcare, retail, and technology. Graduates from a reputable data analytics course in Noida are well-prepared to meet this demand, armed with the essential skills required to interpret complex data sets and provide actionable insights.
Upon completing their training, individuals can seek numerous roles. Common job titles for data analytics graduates include Data Analyst, Business Intelligence Analyst, Data Scientist, and Data Engineer. Each of these positions requires a distinct set of skills, but they share a common foundation in data analysis. For instance, a Data Analyst typically focuses on examining datasets to identify trends and patterns, while a Data Scientist may delve deeper into predictive modeling and machine learning techniques.
Salary prospects for data analytics professionals also present an attractive proposition. In Noida, entry-level data analysts can expect salaries ranging from INR 4 to 7 lakhs per annum, depending on their proficiency and the hiring company. As analysts gain experience and deepen their expertise, they can ascend to higher roles, commanding salaries upwards of INR 12-20 lakhs annually in senior positions. Professional certifications earned alongside the data analytics course in Noida can further enhance earning potential and career advancement opportunities.
Industries actively seeking data analytics talent are diverse, spanning finance, retail, telecommunications, and e-commerce, among others. Companies in these sectors are keen to leverage data analytics to optimize operations, improve customer experiences, and make informed strategic decisions. Therefore, pursuing data analytics course in Noida not only equips individuals with vital skills but also positions them as competitive candidates in a thriving job market.
Success Stories of Past Students
The impact of quality education in the realm of data analytics is quite evident through the success stories of former students who have completed their data analytics course in Noida at Appwars Technologies. Many of these individuals have transformed their careers, landing prestigious roles in distinguished companies across various industries. These testimonials serve as a testament to the effectiveness of the training program in equipping students with the necessary skills to thrive in a competitive job market.
For instance, one notable success story involves a former student, Rahul Sharma, who embarked on his journey with limited knowledge of data analytics tools. After enrolling in the data analytics course in Noida provided by Appwars Technologies, he garnered substantial expertise in data visualization, statistical analysis, and machine learning. Today, Rahul works as a Data Analyst at a leading technology firm, where he plays a crucial role in driving data-driven decision-making processes. His ability to interpret complex datasets has not only earned him recognition within his company but has also positioned him for future promotions.
Another inspiring account is from Priya Desai, who initially worked in a non-technical role. After completing the data analytics course in Noida, she transitioned to a Business Intelligence position within her organization. Priya frequently highlights how the practical approach of the course, coupled with real-world projects, enabled her to apply theoretical concepts effectively. Currently, she is developing predictive analytics models that have significantly improved her company’s operational efficiencies.
These stories, among many others, reflect the positive influence of data analytics training on career trajectories. Former students not only obtain valuable technical skills but also gain the confidence to navigate their professional paths successfully. As they continue to excel in their careers, they stand as inspirational figures for prospective students considering the data analytics course in Noida at Appwars Technologies.
Enrollment Process and Requirements
Enrolling in a data analytics training program in Noida, particularly through Appwars Technologies, is a straightforward process designed to guide prospective students from inquiry to admission. To begin, candidates should familiarize themselves with the prerequisites. Typically, applicants should possess a basic understanding of statistics and a familiarity with Excel or similar tools. While prior experience in programming or data analysis is not mandatory, it can be beneficial for those looking to deepen their expertise during the course.
The application process consists of several steps. Interested candidates must first complete an online application form available on the Appwars Technologies website. This form captures essential details such as educational background, previous work experience, and specific interests within the data analytics field. After submission, applicants may be required to participate in a preliminary assessment. This assessment aims to gauge their analytical skills and understanding of fundamental concepts. Successful candidates will receive notification regarding their acceptance into the data analytics course in Noida.
It is essential to be aware of important dates related to enrollment. The course typically has defined start dates, and applicants are encouraged to submit their applications at least a month prior to the course commencement. Keeping track of deadlines is crucial, as late submissions may result in exclusion from the current batch. Regarding documentation, candidates usually need to provide identification, academic transcripts, and proof of any previous qualifications related to the field of study.
Overall, the enrollment process for data analytics course in Noida is designed with clarity to ensure that all candidates have a smooth transition into the program. Preparing the necessary documentation and adhering to deadlines will enhance the likelihood of securing a spot in this valuable training opportunity.
Additional Resources and Support
Appwars Technologies recognizes that the journey of mastering data analytics requires more than just theoretical knowledge. Committed to ensuring the success of its students, the organization provides a robust framework of additional resources and support services tailored for participants of its data analytics course in Noida.
One of the key features of this training program is the mentorship opportunities available to students. Experienced professionals from the industry offer guidance and insights, helping learners navigate the challenges of becoming adept in data analytics. Structured mentorship sessions are a vital element of the learning experience, empowering students to ask questions and receive personalized advice aimed at enhancing their skills and knowledge. This collaborative approach fosters a deeper understanding of complex concepts, ensuring that students can apply their learning effectively in real-world scenarios.
Additionally, students enrolled in the data analytics course in Noida benefit from access to a wealth of online materials. These resources include curated study materials, webinars, and interactive forums, all designed to enhance the learning experience. This extensive library of resources enables students to grasp fundamental principles of data analytics at their own pace, reinforcing classroom learning with flexible, on-demand content.
Further extending support, Appwars Technologies also offers career counseling services that facilitate job placements and internships. Career advisors assist students in refining their resumes and preparing for interviews, equipping them with the tools necessary for succeeding in the competitive job market. Networking opportunities, such as alumni events and industry meetups, are also organized to connect students with professionals and potential employers, fostering valuable relationships within the field of data analytics.
Overall, these resources and support services highlight Appwars Technologies’ dedication to not just providing a data analytics course in Noida but also ensuring that every student feels supported throughout their educational journey and beyond.
Who can apply for the course?
BE / BTech / MCA passed aspirants to make their careers as Web Developers / Data Scientists
IT-Professionals who want to get a career as a Programming Expert
Professionals from non-IT bkg, and want to establish in IT
Candidates who would like to restart their career after a gap
Web Designers for the next level of their career.
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
