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Data Analytics Course in Noida

Curriculum

61 Sections
359 Lessons
20 Weeks

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Module 1: Excel Basics for Beginners
Tools Covered: Microsoft Excel (All Versions)
8
- 1.1
  Understanding Excel Interface and Ribbon
- 1.2
  Working with Workbooks, Worksheets, and Cells
- 1.3
  Entering and Formatting Data
- 1.4
  Basic Excel Formulas and Functions (SUM, AVERAGE, MIN, MAX)
- 1.5
  Using AutoFill and Flash Fill
- 1.6
  Basic Formatting (Fonts, Colors, Borders, Cell Styles)
- 1.7
  Adjusting Rows, Columns, and Cell Sizes
- 1.8
  Basic Excel Shortcuts for Efficiency
Module 2: Data Entry, Cleaning, and Formatting
Tools Covered: Excel Formatting, Data Cleaning Tools
6
- 2.1
  Data Validation (Drop-down Lists, Restrictions)
- 2.2
  Conditional Formatting for Data Highlighting
- 2.3
  Text Functions (LEFT, RIGHT, MID, LEN, TRIM, CONCATENATE)
- 2.4
  Handling Duplicates and Blank Cells
- 2.5
  Find and Replace, Go To Special
- 2.6
  Sorting and Filtering Data for Better Analysis
Module 3: Working with Formulas and Functions
Tools Covered: Excel Formulas, Logical and Lookup Functions
5
- 3.1
  Understanding Relative, Absolute, and Mixed References
- 3.2
  Logical Functions (IF, AND, OR, IFERROR, IFS)
- 3.3
  Lookup and Reference Functions (VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH)
- 3.4
  Date and Time Functions (TODAY, NOW, DATEDIF, NETWORKDAYS)
- 3.5
  Text Manipulation with String Functions (TEXT, UPPER, LOWER, PROPER)
Module 4: Data Visualization with Charts and Graphs
Tools Covered: Excel Charts and Graphs
5
- 4.1
  Creating and Customizing Charts (Column, Bar, Line, Pie, Area)
- 4.2
  Advanced Charting (Combo Charts, Waterfall, Funnel, Histogram, Pareto)
- 4.3
  Data Labels, Titles, and Formatting Techniques
- 4.4
  Using Sparklines for Miniature Charts in Cells
- 4.5
  Dynamic Charts using Named Ranges
Module 5: Data Analysis with Pivot Tables and Pivot Charts
Tools Covered: Excel Pivot Tables and Pivot Charts
6
- 5.1
  Introduction to Pivot Tables and Pivot Charts
- 5.2
  Creating Pivot Tables for Data Analysis
- 5.3
  Using Slicers and Filters in Pivot Tables
- 5.4
  Summarizing Data with Pivot Tables
- 5.5
  Creating Pivot Charts for Better Insights
- 5.6
  Grouping and Custom Calculations in Pivot Tables
Module 6: Advanced Excel Functions for Data Analysis
Tools Covered: Excel Advanced Formulas and Functions
5
- 6.1
  Advanced Lookup Functions (XLOOKUP, OFFSET, INDIRECT)
- 6.2
  Array Formulas and Dynamic Arrays (SORT, FILTER, UNIQUE)
- 6.3
  Advanced Conditional Formatting for Dynamic Data Highlighting
- 6.4
  Statistical Functions (COUNTIF, AVERAGEIF, SUMIF, RANK, PERCENTILE, QUARTILE)
- 6.5
  Data Forecasting and Trend Analysis
Module 7: Power Query and Power Pivot for Data Modeling
Tools Covered: Power Query, Power Pivot
5
- 7.1
  Introduction to Power Query for Data Cleaning and Transformation
- 7.2
  Importing and Transforming Data using Power Query
- 7.3
  Introduction to Power Pivot for Data Modeling
- 7.4
  Creating Relationships and Measures in Power Pivot
- 7.5
  Using DAX Functions for Data Analysis in Power Pivot
Module 8: Working with Large Datasets and Advanced Excel Tools
Tools Covered: Excel Data Management
5
- 8.1
  Working with Large Datasets (1M+ Rows) Efficiently
- 8.2
  Data Consolidation from Multiple Worksheets/Workbooks
- 8.3
  Advanced Filtering and Sorting Techniques
- 8.4
  Using What-If Analysis (Goal Seek, Data Tables, Solver)
- 8.5
  Scenario Manager for Business Forecasting
Module 9: Excel for Business Intelligence and Reporting
Tools Covered: Power BI, Excel Dashboards
4
- 9.1
  Connecting Excel Data with Power BI
- 9.2
  Creating Interactive Dashboards in Excel
- 9.3
  Using Excel as a Data Source for Power BI
- 9.4
  Exporting Data from Excel to Power BI
Module 10: Projects and Interview Preparation
Tools Covered: Excel, Power Query, Power Pivot
4
- 10.1
  Real-World Data Analysis Projects using Excel
- 10.2
  Business Case Studies (Sales Analysis, Financial Reporting, HR Data Insights)
- 10.3
  Resume Preparation for Data Analyst Roles
- 10.4
  Common Interview Questions for Data Analyst Jobs
Module 11: Introduction to Databases and SQL
Tools Covered: MySQL, Microsoft SQL Server, PostgreSQL
5
- 11.1
  Understanding Databases and Relational Database Management Systems (RDBMS)
- 11.2
  Difference Between SQL and NoSQL Databases
- 11.3
  Database Architecture and ACID Properties
- 11.4
  Setting Up MySQL / SQL Server / PostgreSQL Environment
- 11.5
  Introduction to SQL and Its Role in Data Analysis
Module 12: SQL Basics – Writing Queries
Tools Covered: SQL Query Editor, MySQL Workbench, SSMS
6
- 12.1
  Introduction to SQL Syntax and Data Types
- 12.2
  Creating and Managing Databases
- 12.3
  Creating, Modifying, and Deleting Tables
- 12.4
  Understanding Primary Keys, Foreign Keys, and Constraints
- 12.5
  Inserting, Updating, and Deleting Records (CRUD Operations)
- 12.6
  Querying Data using SELECT Statements
Module 13: Advanced Data Retrieval and Filtering
Tools Covered: SQL Querying Tools
5
- 13.1
  Using WHERE, ORDER BY, and LIMIT Clauses
- 13.2
  Applying Logical Operators (AND, OR, NOT)
- 13.3
  Working with NULL Values and Handling Missing Data
- 13.4
  Using CASE Statements for Conditional Logic
- 13.5
  Aliasing Columns and Tables for Better Readability
Module 14: Aggregations and Grouping Data
Tools Covered: SQL Functions and Queries
4
- 14.1
  Introduction to Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
- 14.2
  Grouping Data using GROUP BY
- 14.3
  Filtering Grouped Data with HAVING Clause
- 14.4
  Using Window Functions (ROW_NUMBER, RANK, DENSE_RANK)
Module 15: Working with Joins and Relationships
Tools Covered: SQL Joins and Relationships
5
- 15.1
  Understanding Different Types of Joins (INNER, LEFT, RIGHT, FULL)
- 15.2
  Self Joins and Cross Joins
- 15.3
  Using UNION, UNION ALL, INTERSECT, and EXCEPT
- 15.4
  Subqueries and Nested Queries for Complex Data Retrieval
- 15.5
  Common Table Expressions (CTEs)
Module 16: Advanced SQL for Data Analysis
Tools Covered: SQL Advanced Queries
5
- 16.1
  Using String Functions (CONCAT, SUBSTRING, REPLACE, CHARINDEX)
- 16.2
  Date and Time Functions (NOW, DATEADD, DATEDIFF)
- 16.3
  Handling Complex Queries using CTEs and Recursive Queries
- 16.4
  Pivoting Data for Business Intelligence Reports
- 16.5
  Ranking and Analytical Functions
Module 17: SQL Performance Optimization and Indexing
Tools Covered: SQL Server / MySQL Performance Tools
5
- 17.1
  Understanding Indexes and Their Importance
- 17.2
  Optimizing Queries using Indexing
- 17.3
  Analyzing Query Execution Plans
- 17.4
  Using Stored Procedures and Triggers
- 17.5
  Transactions and Error Handling in SQL
Module 18: SQL for Data Analysis and Reporting
Tools Covered: SQL Reporting and Visualization
4
- 18.1
  Creating Views for Simplified Querying
- 18.2
  Extracting Insights from Large Datasets
- 18.3
  Connecting SQL Databases to Power BI for Reporting
- 18.4
  Automating SQL Queries for Business Intelligence
Module 19: Real-World Projects and Interview Preparation
Tools Covered: SQL, Power BI, Excel
4
- 19.1
  Real-World Data Analysis Projects using SQL
- 19.2
  Business Case Studies (Sales Analysis, Customer Segmentation, Financial Reports)
- 19.3
  Resume Preparation for Data Analyst Roles
- 19.4
  Common SQL Interview Questions and Mock Interviews
Module 20: Introduction to Python
16
- 20.1
  Overview of Python
- 20.2
  History and Versions of Python
- 20.3
  Features of Python:
- 20.4
  Simple and Open Source
- 20.5
  High-Level Programming Language
- 20.6
  Portable and Interpreted
- 20.7
  Object-Oriented & Procedural
- 20.8
  Easy to Maintain
- 20.9
  Comparison of Python with Other Languages
- 20.10
  Java vs. Python
- 20.11
  C++ vs. Python
- 20.12
  JavaScript vs. Python
- 20.13
  Perl vs. Python
- 20.14
  Executing Python Programs
- 20.15
  Python Interactive Mode vs. Script Mode
- 20.16
  Comments in Python
Module 21: Python Variables & Data Types
11
- 21.1
  Understanding Variables in Python
- 21.2
  Assigning and Declaring Variables
- 21.3
  Data Types in Python
- 21.4
  Numeric Data Types (int, float, complex)
- 21.5
  Boolean Data Type
- 21.6
  Compound Data Types
- 21.7
  Lists
- 21.8
  Tuples
- 21.9
  Dictionaries
- 21.10
  Sets
- 21.11
  Arrays
Module 22: Operators in Python
10
- 22.1
  Types of Operators
- 22.2
  Arithmetic Operators
- 22.3
  Relational (Comparison) Operators
- 22.4
  Assignment Operators
- 22.5
  Logical (Boolean) Operators
- 22.6
  Identity Operators
- 22.7
  Membership Operators
- 22.8
  Bitwise Operators
- 22.9
  Operator Precedence and Associativity
- 22.10
  Understanding Order of Execution in Python
Module 23: Conditional Statements in Python
3
- 23.1
  Control Flow Statements
- 23.2
  if Statement
- 23.3
  if-else Statement
Module 24: Looping in Python
7
- 24.1
  Loops in Python
- 24.2
  for Loop
- 24.3
  while Loop
- 24.4
  Loop Control Statements
- 24.5
  break Statement
- 24.6
  continue Statement
- 24.7
  pass Statement
Module 25 Working with Numbers in Python
3
- 25.1
  Mathematical Functions
- 25.2
  Random Functions
- 25.3
  Trigonometric Functions
Module 26: Working with Strings in Python
4
- 26.1
  Accessing Strings
- 26.2
  String Operations
- 26.3
  String Slicing
- 26.4
  Built-in String Functions
Module 27: Working with Lists in Python
7
- 27.1
  Introduction to Lists
- 27.2
  Accessing and Modifying Lists
- 27.3
  List Slicing and Operations
- 27.4
  Aliasing and Cloning Lists
- 27.5
  List Comprehensions
- 27.6
  Deleting Lists
- 27.7
  Built-in List Functions
Module 28: Working with Tuples in Python
4
- 28.1
  Introduction to Tuples
- 28.2
  Creating, Accessing, and Modifying Tuples
- 28.3
  Deleting Tuples
- 28.4
  Built-in Tuple Functions
Module 29: Working with Dictionaries in Python
4
- 29.1
  Introduction to Dictionaries
- 29.2
  Creating, Accessing, and Modifying Dictionaries
- 29.3
  Deleting Dictionary Elements
- 29.4
  Built-in Dictionary Functions
Module 30: Working with Sets in Python
4
- 30.1
  Introduction to Sets
- 30.2
  Creating and Accessing Sets
- 30.3
  Set Operations (Union, Intersection, Difference, etc.)
- 30.4
  Built-in Set Functions
Module 31: Date & Time Handling in Python
2
- 31.1
  Working with time Module
- 31.2
  Working with calendar Module
Module 32: Functions in Python
5
- 32.1
  Defining and Calling Functions
- 32.2
  Types of Functions
- 32.3
  Function Arguments (Positional, Keyword, Default, Variable-Length)
- 32.4
  Lambda (Anonymous) Functions
- 32.5
  Scope of Variables (Global & Local Variables)
Module 33: Working with Modules in Python
4
- 33.1
  Introduction to Modules
- 33.2
  Using import Statement
- 33.3
  Using from … import Statement
- 33.4
  Using from … import * Statement
Module 34: File Handling in Python (I/O Operations)
4
- 34.1
  Opening and Closing Files
- 34.2
  Reading and Writing Files
- 34.3
  Renaming and Removing Files
- 34.4
  Working with Directories
Module 35: Exception Handling in Python
6
- 35.1
  Understanding Errors in Python
- 35.2
  Syntax Errors
- 35.3
  Exceptions
- 35.4
  Handling Exceptions in Python
- 35.5
  Raising Exceptions
- 35.6
  User-Defined Exceptions
Module 36: Object-Oriented Programming (OOP) in Python
5
- 36.1
  Understanding Classes and Objects
- 36.2
  Attributes and Methods
- 36.3
  Inheritance (Single, Multiple, Multilevel)
- 36.4
  Method Overloading and Overriding
- 36.5
  Encapsulation and Abstraction
Module 37: Introduction to Anaconda Distribution
Python Modules Curriculum Pandas, Numpy, Matplotlib and Seaborn
7
- 37.1
  🛠 Tools Covered: Anaconda, Python, Jupyter Notebook, PyCharm
- 37.2
  What is Anaconda Distribution?
- 37.3
  Difference between Anaconda and Python Distribution
- 37.4
  How to install Anaconda?
- 37.5
  Anaconda Repository: Understanding conda packages and environments
- 37.6
  Anaconda Navigator: Managing libraries and environments
- 37.7
  Integrating Anaconda with PyCharm for seamless coding
Module 38: Using Git and GitHub
🛠 Tools Covered: Git, GitHub
5
- 38.1
  Setting up GitHub Account
- 38.2
  Configuring Your First Git Repository
- 38.3
  Making First Git Commit
- 38.4
  Pushing First Commit to GitHub
- 38.5
  Understanding Git and GitHub Workflow (Step-by-Step)
Module 39: Introduction to NumPy & Statistical Analysis
Tools Covered: NumPy, Python
7
- 39.1
  What is NumPy?
- 39.2
  Performance Testing of NumPy vs Lists
- 39.3
  NumPy Arrays and Matrices
- 39.4
  Indexing and Selection in NumPy
- 39.5
  NumPy Operations: Array with Array, Scalars, Universal Functions
- 39.6
  Working with Flat Files using NumPy
- 39.7
  Mathematical and Statistical Functions in NumPy
Module 40: Introduction to Pandas & Data Analysis
Tools Covered: Pandas, Python
8
- 40.1
  What is Pandas?
- 40.2
  Creating Pandas Series and DataFrames
- 40.3
  Grouping, Sorting, and Filtering Data
- 40.4
  Merging, Joining, and Concatenating DataFrames
- 40.5
  Handling Missing Data (Imputation Techniques)
- 40.6
  Pandas Operations for Data Analysis
- 40.7
  Data Input and Output (CSV, Excel, JSON, Databases)
- 40.8
  Hands-on Practical Use Cases using Pandas
Module 41: Statistics and Probability
Concepts Covered: Statistical Analysis, Probability Theory
9
- 41.1
  Types of Datasets: Numerical, Categorical, Ordinal
- 41.2
  Descriptive Statistics: Mean, Median, Mode
- 41.3
  Variance & Standard Deviation
- 41.4
  Probability Functions:
- 41.5
  Probability Density Function (PDF)
- 41.6
  Probability Mass Function (PMF)
- 41.7
  Percentiles and Moments
- 41.8
  Covariance vs Correlation
- 41.9
  Conditional Probability & Bayes’ Theorem
Module 42: Data Visualization using Matplotlib
Tools Covered: Matplotlib, Python
7
- 42.1
  Understanding Exploratory Data Analysis (EDA)
- 42.2
  Plotting Line Graphs on Time-Series Data
- 42.3
  Pie Charts, Bar Charts, and Horizontal Bar Graphs
- 42.4
  Introduction to the IRIS Dataset
- 42.5
  2D Scatter Plots & Pair Plots
- 42.6
  Histograms & Probability Density Function (PDF)
- 42.7
  Cumulative Distribution Function (CDF)
Module 43: Data Visualization using Seaborn
Tools Covered: Seaborn, Python
5
- 43.1
  Introduction to Seaborn
- 43.2
  Scatter Plots & Figure Customization
- 43.3
  Line Plots for Time-Series Data
- 43.4
  Advanced Seaborn Visualizations
- 43.5
  Hands-on Data Visualization with Real Datasets
Module 44: Project & Interview Preparation
Projects, Career Guidance & Resume Building
7
- 44.1
  Hands-on Real-World Projects
- 44.2
  E-commerce Data Analysis
- 44.3
  Retail Sales Forecasting
- 44.4
  Customer Segmentation using SQL & Pandas
- 44.5
  Soft Skills & PD (Personality Development) Classes
- 44.6
  Resume Preparation & Optimization for Data Analyst Roles
- 44.7
  Common Interview Questions & Mock Interview Sessions
Module 45: Quick Start with Power BI Service
Power BI Course Curriculum (Beginner to Advanced) | Tools Covered: Power BI Service, Power BI Desktop
6
- 45.1
  Getting Power BI Tools: Installing and Setting Up
- 45.2
  Introduction to Power BI Components and Terminology
- 45.3
  Creating a Dashboard in Minutes (Hands-on Exercise)
- 45.4
  Refreshing Data in Power BI Service
- 45.5
  Interacting with Dashboards and Reports
- 45.6
  Sharing Dashboards and Reports
Module 46: Getting and Transforming Data with Power BI Desktop
Tools Covered: Power BI Desktop, Power Query Editor
6
- 46.1
  Introduction to Power BI Desktop
- 46.2
  Getting Data: Excel vs Power BI Desktop and Service
- 46.3
  Understanding Data Structure for Q&A
- 46.4
  Direct Query vs Import Data
- 46.5
  Connecting to Multiple Data Sources
- 46.6
  Data Cleaning and Transformation using Power Query
Module 47: Data Modeling in Power BI
Tools Covered: Power BI Desktop
7
- 47.1
  Introduction to Data Modeling
- 47.2
  Setting Up and Managing Relationships
- 47.3
  Understanding Cardinality and Cross-Filtering
- 47.4
  Default Summarization and Sorting Options
- 47.5
  Creating Calculated Columns
- 47.6
  Creating Measures and Quick Measures
- 47.7
  Optimizing Data Models for Performance
Module 48: Data Visualization in Power BI
Tools Covered: Power BI Desktop
20
- 48.1
  Creating Different Types of Visualizations
- 48.2
  Formatting and Customizing Visualizations
- 48.3
  Setting Sort Order for Visuals
- 48.4
  Using Scatter and Bubble Charts with Play Axis
- 48.5
  Tooltips and Interactive Reports
- 48.6
  Slicers, Timeline Slicers, and Sync Slicers
- 48.7
  Cross Filtering and Highlighting
- 48.8
  Applying Visual, Page, and Report Level Filters
- 48.9
  Drill Down and Drill Up Functionality
- 48.10
  Working with Hierarchies
- 48.11
  Adding Reference and Constant Lines
- 48.12
  Creating Tables and Matrices with Conditional Formatting
- 48.13
  Using KPI Indicators, Cards, and Gauges
- 48.14
  Map Visualizations
- 48.15
  Importing and Using Custom Visuals
- 48.16
  Managing and Arranging Visuals in Reports
- 48.17
  Implementing Drill-through for In-depth Analysis
- 48.18
  Using Custom Report Themes
- 48.19
  Grouping and Binning Data
- 48.20
  Working with Selection Pane, Bookmarks, and Buttons
Module 49: Power BI Service Visualization Tools
Tools Covered: Power BI Service
5
- 49.1
  Introduction to Power BI Service Visualization Tools
- 49.2
  Creating and Managing Standalone Tiles
- 49.3
  Setting Up Data-Driven Alerts
- 49.4
  Gaining Quick and Related Insights
- 49.5
  Customizing Q&A Questions
Module 50: Publishing and Sharing Reports
Tools Covered: Power BI Service, Power BI Desktop
13
- 50.1
  Introduction to Power BI Publishing and Sharing Options
- 50.2
  Overview of Different Sharing Methods
- 50.3
  Publishing Reports from Power BI Desktop
- 50.4
  Publishing Reports to the Web
- 50.5
  Sharing Dashboards with Power BI Service
- 50.6
  Creating and Managing Workspaces and Apps (Power BI Pro)
- 50.7
  Using Content Packs (Power BI Pro)
- 50.8
  Printing or Saving Reports as PDFs
- 50.9
  Implementing Row-Level Security (Power BI Pro)
- 50.10
  Exporting Data from Visualizations
- 50.11
  Publishing Reports for Mobile Applications
- 50.12
  Exporting Reports to PowerPoint
- 50.13
  Summary of Sharing Options
Module 51: Data Refresh and Gateway Setup
Tools Covered: Power BI Service, On-Premises Gateway
5
- 51.1
  Understanding Data Refresh in Power BI
- 51.2
  Configuring Automatic Refresh
- 51.3
  Setting Up and Using the Personal Gateway (Power BI Pro and 64-bit Windows)
- 51.4
  Replacing Datasets in Power BI
- 51.5
  Troubleshooting Data Refresh Issues
Module 52: Power BI and Excel Integration
Tools Covered: Power BI Desktop, Power BI Service, Excel
5
- 52.1
  Different Options for Publishing Data from Excel to Power BI
- 52.2
  Pinning Excel Elements to Power BI Dashboards
- 52.3
  Connecting Excel Data using Power BI Publisher and Analyze in Excel
- 52.4
  Publishing Excel Dashboards to Power BI
- 52.5
  Uploading and Exporting Excel Data to Power BI
Module 53: Power BI Projects and Interview Preparation
Tools Covered: Power BI Desktop, Power BI Service
4
- 53.1
  Comprehensive Project Work Covering All Concepts
- 53.2
  Hands-on Real-World Scenarios and Case Studies
- 53.3
  Resume Preparation for Power BI Roles
- 53.4
  Power BI Interview Questions and Mock Interview Sessions
Module 54: Introduction to Tableau and BI Concepts
Tableau Mastery (Optional**): Data Visualization and Business Intelligence
6
- 54.1
  Overview of Data Warehousing and Business Intelligence
- 54.2
  Fundamentals of Data Analysis and Visualization
- 54.3
  Business Reporting and Dashboard Essentials
- 54.4
  Introduction to Tableau and Its Architecture
- 54.5
  Understanding Measures vs. Dimensions
- 54.6
  Continuous vs. Discrete Data, Value & Category Axes
Module 55: Connecting and Managing Data Sources
4
- 55.1
  Importing Data from Flat Files, Databases, and Cloud Sources
- 55.2
  Web Services and Clipboard Data Integration
- 55.3
  Understanding Extracts and Incremental Extracts
- 55.4
  Managing Tableau Server Data Sources
Module 56: Saving and Publishing Workbooks
3
- 56.1
  Working with Packaged Workbooks
- 56.2
  Publishing Reports to Tableau Server
- 56.3
  Understanding Different Tableau File Formats
Module 57: Core Data Visualization Techniques
5
- 57.1
  Creating Worksheets and Dashboards
- 57.2
  Applying Filters and Customizing Filter Actions
- 57.3
  Understanding Row and Column Shelves
- 57.4
  Using Marks Cards: Color, Size, Labels, Tooltips, and Paths
- 57.5
  Working with Sets, Groups, Parameters, and Calculated Columns
Module 58: Creating Effective Visualizations
Building Various Chart Types:
6
- 58.1
  Line, Bar, Stacked, and Dual-Axis Charts
- 58.2
  Heat Maps, Text Tables, and Highlight Tables
- 58.3
  Symbol and Filled Maps, Pie Charts, and Treemaps
- 58.4
  Circle, Area, and Combination Charts
- 58.5
  Scatter Plots, Histograms, and Box Plots
- 58.6
  Gantt, Bullet, and Packed Bubble Charts
Module 59: Advanced Features and Analytics
7
- 59.1
  Designing Interactive Dashboards
- 59.2
  Forecasting and Trend Analysis
- 59.3
  Adding Reference Lines, Bands, and Visual Highlights
- 59.4
  Handling Missing Values and Null Data
- 59.5
  Implementing Table Calculations and Totals
- 59.6
  Custom Formatting, Annotations, and Layout Adjustments
- 59.7
  Using Dashboard Actions: Filters and Highlights
Module 60: Tableau Server and Collaboration
4
- 60.1
  Authoring and Publishing Dashboards
- 60.2
  Managing Subscriptions and Report Distribution
- 60.3
  Embedding Dashboards into Web Pages
- 60.4
  Exporting Reports and Sharing Server Views
Module 61: Project Work and Interview Preparation
Note- Students can choose Power BI or Tableau. Opting for both may result in a revised fee structure. Contact us for details.
3
- 61.1
  Hands-on Data Analysis and Dashboard Creation
- 61.2
  Resume Building for Data Analyst Roles
- 61.3
  Common Tableau Interview Questions and Practical Exercises

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