Getting Digital

Data Analyst- Business Intelligence Python Pandas SQL

Develop essential data science & ai skills with expert instruction and practical examples.

Online Course
Self-paced learning
Flexible Schedule
Learn at your pace
Expert Instructor
Industry professional
Certificate
Upon completion
What You'll Learn
Master the fundamentals of data science & ai
Apply best practices and industry standards
Build practical projects to demonstrate your skills
Understand advanced concepts and techniques

Skills you'll gain:

Professional SkillsBest PracticesIndustry StandardsPython
Prerequisites & Target Audience

Skill Level

IntermediateSome prior knowledge recommended

Requirements

Basic understanding of data science & ai
Enthusiasm to learn
Access to necessary software/tools
Commitment to practice

Who This Course Is For

Professionals working in data science & ai
Students and career changers
Freelancers and consultants
Anyone looking to improve their skills
Course Information

About This Course

The data analyst serves as a gatekeeper for an organization's data so stakeholders can understand data and use it to make strategic business decisions. Business intelligence (BI) helps organizations analyze historical and current data, so they can quickly uncover actionable insights for making strategic decisions. Business intelligence tools make this possible by processing large data sets across multiple sources and presenting findings in visual formats that are easy to understand and share.

There are four keys steps that business intelligence follows to transform raw data into easy-to-digest insights for everyone in the organization to use. The first three-data collection, analysis, and visualization-set the stage for the final decision-making step. Before using BI, businesses had to do much of their analysis manually, but BI tools automate many of the processes and save companies time and effort.

Step 1: Collect and transform data from multiple sourcesBusiness intelligence tools typically use the extract, transform, and load (ETL) method to aggregate structured and unstructured data from multiple sources. This data is then transformed and remodeled before being stored in a central location, so applications can easily analyze and query it as one comprehensive data set. Step 2: Uncover trends and inconsistenciesData mining, or data discovery, typically uses automation to quickly analyze data to find patterns and outliers which provide insight into the current state of business.

BI tools often feature several types of data modeling and analytics-including exploratory, descriptive, statistical, and predictive-that further explore data, predict trends, and make recommendations. Step 3: Use data visualization to present findingsBusiness intelligence reporting uses data visualizations to make findings easier to understand and share. Reporting methods include interactive data dashboards, charts, graphs, and maps that help users see what's going on in the business right now.

Provider
Udemy
Estimated Duration
10-20 hours
Language
English
Category
Technology & Programming

Topics Covered

Data Science & AIPython

Course Details

Format
Online, Self-Paced
Access
Lifetime
Certificate
Upon Completion
Support
Q&A Forum
Course Details
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This course includes:

Lifetime access to course content
Access on mobile and desktop
Certificate of completion
Downloadable resources

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