Data Analytics & Visualization

About Course

Description

The Data Analytics & Visualization Course provides comprehensive training in Python with a focus on its integration with Power BI for analytics and visualization. Participants will develop expertise in Python programming techniques tailored for Power BI, enabling them to design intelligent analytics and interactive visualizations.

Python Fundamentals: Establish a solid foundation in Python programming by covering essential concepts such as syntax, data structures, and control flow, with an emphasis on analytical applications.
Data Science Libraries: Master essential Python libraries like NumPy, Pandas, and Matplotlib for efficient data manipulation, analysis, and preparation for analytical tasks.
Power BI: Learn to use Microsoft’s business analytics tool to connect to diverse data sources, transform data, and create interactive dashboards and reports. Explore its powerful visualizations, AI-driven insights, and seamless integration within the Microsoft ecosystem, utilizing components like Power BI Desktop, Service, and Mobile to support data-driven decision-making for organizations of all sizes.

What You’ll Learn

Python for Data Analytics: Develop proficiency in Python programming for data manipulation, analysis, and visualization using libraries like NumPy, Pandas, and Matplotlib.
Power BI Integration: Learn to connect Python with Power BI to enhance analytics workflows and create custom visualizations.
Data Transformation: Master data cleaning, preprocessing, and transformation techniques to prepare datasets for analysis.
Interactive Dashboards: Build dynamic and interactive dashboards and reports using Power BI to support insightful decision-making.
AI-Powered Insights: Leverage Power BI’s AI-driven capabilities to uncover trends, patterns, and predictive insights.
End-to-End Analytics: Gain hands-on experience in the complete data analytics pipeline, from data acquisition to visualization and reporting.
Microsoft Ecosystem: Understand how to seamlessly integrate Power BI with other Microsoft tools like Excel, Azure, and SharePoint for streamlined workflows.

By the end of the course, participants will confidently create intelligent, interactive analytics and visualizations to drive impactful business decisions.

Shopping cart

OUR TOP Program

Data Analytics & Visualization

admin

January 18, 2025

About Course

Course Curriculum

Python Fundamentals

I/O functions, control statements

Collections: List, Tuple, Dict, Set

Functions, modules, and object-oriented programming

Data file handling (text files, CSV, JSON)

Database Management

Introduction to databases: MariaDB/MySQL, SQLite

Basics of MongoDB (NoSQL database)

CRUD operations and querying in all three databases

Data Science Libraries

Numpy: Arrays, numerical operations

Pandas: DataFrames, data manipulation, and cleaning

Data Visualization

Matplotlib: Basic plots, customizing visuals

Seaborn: Statistical plots, heatmaps, pair plots

Plotly and Bokeh: Interactive visualizations

Introduction to Power BI Desktop

Overview of Power BI ecosystem (Power BI Desktop, Service, and Mobile)

Installing and setting up Power BI Desktop

Key features and interface walkthrough

Importing data from different sources (Excel, CSV, databases)

Data model basics

Data Transformation with Power Query

Introduction to Power Query Editor

Cleaning and transforming data (renaming, splitting, merging columns)

Filtering, sorting, and replacing values

Handling missing data

Appending and merging queries

Data Modelling

Understanding relationships in Power BI

Creating relationships between tables

Cardinality and filter direction

Star and Snowflake schema basics

Optimizing models for performance

Introduction to DAX (Data Analysis Expressions)

Overview of DAX and its role in Power BI

Basic DAX calculations: SUM, AVERAGE, COUNT

Creating calculated columns and measures

Using quick measures

Advanced DAX

Logical functions: IF, SWITCH

Time intelligence functions: TODAY, DATESYTD, SAMEPERIODLASTYEAR

Working with context: Row context vs. Filter context

Aggregations with CALCULATE

Visualization Basics

Creating bar, line, and pie charts

Working with tables and matrices

Formatting visuals for better readability

Adding slicers and filters to reports

Using drill-through and tooltips

Advanced Visualizations

Custom visuals and themes

Waterfall, Funnel, and Gauge charts

Maps and geospatial visualizations

KPI and Card visuals

Creating hierarchical visuals

Interactive Dashboards

Designing interactive dashboards

Syncing slicers across pages

Bookmarking and storytelling with Power BI

Adding navigation buttons

Data Analysis and Insights

Analyzing trends and patterns

Building dynamic reports with parameters

Grouping and binning data

Analyzing data with the Q&A feature

Connecting to Advanced Data Sources

Connecting to SQL Server and other databases

Using DirectQuery vs. Import Mode

Setting up incremental data refresh

Accessing cloud data (SharePoint, Azure)

Working with Power BI Service

Publishing reports to Power BI Service

Review and Q&A
• Reviewing all major topics • Common challenges and solutions • Advanced tips and tricks • Certification guidance and resources (if applicable) • Wrap-up and feedback