|
Certificate Course on Data Analysis |
|
|
Duration: 2 Months Training + 2 Months Internship |
Certification Validity: 5 years |
Course Overview
Unlock the Power of Data Analysis
Learn how to analyze, visualize, and share insights from data using two of the most powerful tools in the industry: Power BI and Tableau. This comprehensive course is designed to give you practical skills in data analysis, data visualization, and reporting. Whether you’re a beginner or looking to advance your skills, this course will help you become proficient in using both PowerBI and Tableau to turn data into actionable insights.
Why Take This Course?
-
In-Demand Skills: Master two of the most widely used tools in the data analytics industry.
-
Hands-On Experience: Practical, project-based learning with real-world data.
-
Expert-Led: Learn from experienced instructors with industry expertise.
-
Certificate: Receive a recognized certification upon successful completion.
-
Career Opportunities: Improve your data analysis skills for better job prospects and career growth.
Who Should Enroll?
-
Aspiring Data Analysts: Looking to gain hands-on experience in PowerBI and Tableau.
-
Business Professionals: Seeking to enhance data-driven decision-making in their organization.
-
Students: Wanting to add data analysis tools to their skill set for future job opportunities.
-
Anyone Interested in Data Visualization: Those looking to break into the data science or analytics field.
Course Learning Objectives
By the end of this course, you will be able to:
-
Understand the fundamentals of data analysis and visualization.
-
Connect PowerBI and Tableau to multiple data sources (Excel, databases, cloud, etc.).
-
Clean and transform raw data into structured formats for analysis.
-
Create a wide range of interactive and insightful visualizations.
-
Use advanced features in PowerBI and Tableau for data modeling and analysis.
-
Build interactive dashboards for data exploration and presentation.
-
Effectively share your reports and dashboards with stakeholders.
-
Gain hands-on experience with real-world case studies and projects.
Course Contents
Part-I PowerBI
Introduction to Business Intelligence and Power BI
1. Introduction to Business Intelligence (BI)
-
What is Business Intelligence?
-
Definition: BI refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information to help in decision-making.
-
The need for BI: Data-driven decisions improve operational efficiency, competitive advantages, and overall business performance.
-
Key Components of BI: Data collection, data storage, data analysis, reporting, and visualization.
-
BI Tools Overview: SQL-based tools, Excel, Power BI, Tableau, SAS, etc.
-
-
Overview of Power BI
-
What is Power BI?
-
Components of Power BI:
-
Power BI Desktop: The primary authoring tool to create reports.
-
Power BI Service: The online platform for publishing, sharing, and collaborating on reports and dashboards.
-
Power BI Mobile: Access and interact with reports and dashboards on mobile devices.
-
-
Benefits of using Power BI: User-friendly, scalable, rich visualizations, integration with multiple data sources, and strong community support.
-
Power BI vs other BI Tools: A comparison of Power BI with tools like Tableau, QlikView, etc.
-
2. Power BI Interface Overview
-
Walkthrough of the Power BI Desktop interface.
-
Exploring key sections: Ribbon, Fields Pane, Visualizations Pane, Report View, Data View, and Model View.
Connecting to Data Sources and Data Transformation
1. Connecting to Data Sources
-
Types of Data Sources in Power BI:
-
File-based sources: Excel, CSV, Text files.
-
Database sources: SQL Server, Oracle, MySQL, PostgreSQL, etc.
-
Cloud-based sources: Azure SQL, Google Analytics, Salesforce, etc.
-
Web-based sources: JSON, XML, Web data (via APIs).
-
-
Connecting to Data in Power BI:
-
Step-by-step process to connect to Excel, SQL Server, and Web-based data sources.
-
Using Get Data wizard in Power BI Desktop to import data from different sources.
-
Preview data before loading into Power BI.
-
2. Data Transformation and Cleaning
-
Introduction to Power Query Editor:
-
What is Power Query Editor?
-
Using Power Query to clean, filter, and transform data.
-
Hands-on: Opening Power Query Editor and understanding its interface.
-
-
Data Transformation Tasks:
-
Filtering Rows: Remove rows based on specific conditions or filters.
-
Removing Columns: Delete unnecessary columns from your data.
-
Renaming Columns: Change column names to make them more user-friendly.
-
-
Hands-on Examples:
-
Cleaning a sales dataset: Removing invalid rows, renaming product categories, filtering rows with no values, etc.
-
Data Modeling and DAX
1. Data Modeling in Power BI
-
Understanding the Power BI Data Model:
-
Power BI uses a tabular data model, which is a columnar database optimized for analytical queries.
-
Exploring relationships between tables: Why they matter in BI solutions.
-
-
Relationships in Power BI:
-
One-to-One: Each record in one table corresponds to one record in another.
-
One-to-Many: A record in one table corresponds to multiple records in another (e.g., one customer can have many orders).
-
Many-to-Many: Multiple records in one table correspond to multiple records in another (e.g., many students enrolled in many courses).
-
-
Creating Relationships Between Tables:
-
Creating relationships manually in Power BI.
-
Understanding relationship direction and cross-filtering.
-
Cardinality: One-to-One, One-to-Many, and Many-to-Many relationships.
-
2. DAX (Data Analysis Expressions)
-
Introduction to DAX: DAX is a formula language used in Power BI to define calculations and business logic.
-
Basic DAX Functions:
-
SUM: Calculates the sum of a column.
-
AVERAGE: Returns the average of a column.
-
COUNT: Returns the number of rows in a column.
-
FILTER: Filters a table or column based on a condition.
-
3. Creating Calculated Columns and Measures:
-
Calculated Columns:
-
A new column created by applying DAX formulas to existing data.
-
Example: Adding a “Profit” column by subtracting the “Cost” column from the “Revenue” column.
-
-
Measures:
-
Measures are dynamic calculations based on user interaction with visualizations.
-
Example: Creating a measure for “Total Sales” or “Average Order Value.”
-
Power BI Service, Dashboards, and Sharing
1. Power BI Service Overview
-
What is Power BI Service?:
-
Introduction to Power BI’s cloud-based service.
-
How it differs from Power BI Desktop: publishing, sharing, and collaboration features.
-
-
Publishing Reports to Power BI Service:
-
Step-by-step process to publish reports from Power BI Desktop to the Service.
-
Data refresh and security considerations.
-
-
Creating Dashboards:
-
Dashboards in Power BI: A collection of visualizations from different reports.
-
Creating a new dashboard from Power BI Service.
-
2. Best Practices for Dashboard Design
-
Effective Design Principles:
-
Simplicity: Focus on what matters.
-
Consistency: Use similar visual styles for related data.
-
Clear Titles & Labels: Ensure that the users can understand data at a glance.
-
-
Designing Visualizations:
-
Use appropriate charts for data visualization (Bar charts, Pie charts, Line charts, etc.).
-
Utilizing slicers and filters for interactive dashboards.
-
Tips for making dashboards user-friendly.
-
3. Hands-on Exercise: Build a Dashboard:
-
Build a dashboard by pinning visuals from reports in the Power BI Service.
-
Add interactivity using slicers and filters.
Sharing, Collaboration, and Capstone Project
1. Sharing and Collaboration in Power BI
-
Sharing Reports and Dashboards:
-
How to share your reports and dashboards with stakeholders.
-
Sharing options in Power BI: Share with individuals, groups, or publish to web.
-
Permissions and access control.
-
-
Collaboration Features:
-
Using Power BI Workspaces for collaboration.
-
How to create and manage workspaces.
-
Annotating and commenting on reports.
-
2. Power BI Mobile App
-
Accessing Reports on Mobile: Viewing and interacting with reports and dashboards on the go.
-
Optimizing Reports for Mobile Devices: Best practices for creating mobile-friendly reports.
3. Capstone Project: Building a Complete Power BI Solution
-
Overview of the Capstone Project:
-
Participants will work on a real-world data set to build a complete Power BI solution.
-
Key steps: Data connection, data transformation, modeling, and visualization.
-
Participants will publish their solution to Power BI Service and share it.
-
-
Hands-on: Build Your Solution:
-
Work on the project individually or in groups, apply DAX, create relationships, build interactive reports, and publish to Power BI Service.
-
-
Final Presentation:
-
Present your Power BI solution, explain the workflow, and discuss the insights provided by the report/dashboard.
-
Part-II Tableau
1. Tableau Introduction and Products
-
-
-
-
What is Tableau?
-
Key Features of Tableau:
-
Tableau Products:
-
-
-
2. Connecting to Data Sources
Types of Data Connections in Tableau:
-
File-based Data Connections:
-
Excel: Connect to Excel files for analysis and create visualizations with the imported data.
-
Text Files (CSV): Easily import CSV files into Tableau.
-
JSON: Connect Tableau to JSON files to work with structured data.
-
Access Database: Connect to Microsoft Access databases.
-
3. Data Preparation and Cleaning
Using Tableau Prep for Data Cleaning:
Tableau Prep is a tool that facilitates data cleaning, transformation, and shaping.
-
Connect to Data: Import data into Tableau Prep from multiple sources.
-
Data Transformation: Modify and clean data by filtering rows, renaming columns, changing data types, etc.
-
Join and Union: Combine data from multiple tables or files using joins and unions.
-
Filtering Data: Remove unnecessary rows based on conditions like null values or outliers.
-
Handling Missing Data: Use techniques such as data imputation or removing incomplete records .
Data Transformation Tasks:
-
Splitting and Merging Columns: Break down columns into multiple values or merge multiple columns into one.
-
Grouping Data: Create custom groupings for categorical data.
-
Pivoting Data: Convert columns into rows and vice versa.
4. Data Modeling
Understanding Data Structure:
-
Dimensional Data Model: Involves dimensions (descriptive attributes) and facts (quantitative metrics). This structure is crucial for BI tools like Tableau.
-
Star Schema: A type of schema in data warehousing where fact tables are connected to dimension tables, resembling a star.
Types of Relationships:
-
One-to-One (1:1): Each record in one table relates to a single record in another table.
-
One-to-Many (1:N): One record in a table relates to many records in another table (e.g., one customer has many orders).
-
Many-to-Many (N:M): Multiple records in one table relate to multiple records in another (e.g., a student can take many courses, and a course can have many students).
Building Data Models:
-
Creating Relationships Between Tables: Using primary and foreign keys to relate tables and build an integrated model.
-
Aggregating Data: Summarizing data into higher-level insights such as averages, counts, and sums.
5. Data Visualization
Creating Visualizations:
-
Basic Charts: Line charts, bar charts, pie charts, and area charts to visualize trends, categories, and distributions.
-
Advanced Charts: Heat maps, bullet charts, histograms, box plots, and waterfall charts.
-
Geospatial Visualization: Mapping geographic data with Tableau’s built-in map visualizations (e.g., latitude and longitude).
Types of Visualizations:
-
Trend Analysis: Display time series data to identify patterns and trends over time.
-
Distribution: Visualize data distribution using histograms and box plots.
-
Proportional: Pie charts and stacked bar charts to show the proportion of categories.
-
Comparisons: Side-by-side bar charts or line charts to compare different categories.
Interactivity:
-
Filters: Allow users to filter data in visualizations.
-
Parameters: Enable users to dynamically control filters and other inputs.
-
Highlight Actions: Highlight certain elements when a user interacts with the chart.
6. Calculated Fields and Functions
Basic Functions:
-
String Functions: CONCATENATE, UPPER, LOWER, TRIM.
-
Date Functions: DATEADD, DATEDIFF, TODAY, NOW.
-
Number Functions: ROUND, ABS, CEILING, FLOOR.
Conditional Statements:
-
IF-THEN-ELSE: Define logic to categorize or modify data based on conditions.
-
CASE Statements: A shorthand version of IF-THEN-ELSE for multiple conditions.
Aggregations:
-
SUM, AVG, COUNT: Common aggregate functions.
-
Window Functions: Create moving averages, rank data, or calculate cumulative sums.
7. Dashboards and Storytelling
Creating Dashboards:
-
Building Dashboards: Combine multiple visualizations into a single view. Arrange them in an interactive, easy-to-understand layout.
-
Use of Filters: Add global filters and slicers to provide interactivity within dashboards.
-
Actions: Add filter, highlight, and URL actions to create a more interactive user experience.
Storytelling:
-
Creating Stories: Organize visualizations into a narrative format where data points guide the audience through a sequence of insights.
-
Narrative Flow: Create a story that leads from insights to conclusions, highlighting key points and decisions.
8. Tableau Public and Sharing Insights
Using Tableau Public:
-
Creating Visualizations in Tableau Public: Build reports and share them publicly with the Tableau community.
-
Publishing Dashboards: Publish dashboards to Tableau Public for others to explore and comment.
-
Embedding Visualizations: Embed Tableau visualizations in websites, blogs, or social media.
Sharing Insights with Tableau:
-
Tableau Server & Tableau Online: Share and collaborate on dashboards within your organization or externally.
-
Interactive Sharing: Allow viewers to filter and interact with published visualizations.
9. Capstone Project: Building a Complete Tableau Solution
Capstone Project Overview:
-
Objective: Apply the concepts learned throughout the course to build a comprehensive Tableau solution, from data connection to final dashboard presentation.
-
Key Activities:
-
Data Connection: Connect to a real-world data source (e.g., sales data, financial data, etc.).
-
Data Preparation: Clean and transform the data using Tableau Prep and other tools.
-
Modeling: Design the data model and establish relationships.
-
Visualization: Create insightful visualizations and dashboards.
-
Storytelling: Create a narrative using Tableau’s storytelling feature to communicate key insights.
-