Data Analyst | Business Intelligence: Python,BI,Tableau,SQL

Data Analyst | Business Intelligence: Python,BI,Tableau,SQL, available at $64.99, has an average rating of 4.3, with 100 lectures, based on 135 reviews, and has 1515 subscribers.

You will learn about Connect to multiple data sources Clean and transform Data Model Data Transform less structured data Create data relationships Create queries with Power query Merge queries with Power query Create reports Create visualizations Create dashboards This course is ideal for individuals who are Beginner Data Analyst or Beginner Data Scientist or Beginner Data Engineer It is particularly useful for Beginner Data Analyst or Beginner Data Scientist or Beginner Data Engineer.

Enroll now: Data Analyst | Business Intelligence: Python,BI,Tableau,SQL

Summary

Title: Data Analyst | Business Intelligence: Python,BI,Tableau,SQL

Price: $64.99

Average Rating: 4.3

Number of Lectures: 100

Number of Published Lectures: 100

Number of Curriculum Items: 100

Number of Published Curriculum Objects: 100

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

Connect to multiple data sources
Clean and transform Data
Model Data
Transform less structured data
Create data relationships
Create queries with Power query
Merge queries with Power query
Create reports
Create visualizations
Create dashboards

Who Should Attend

Beginner Data Analyst
Beginner Data Scientist
Beginner Data Engineer

Target Audiences

Beginner Data Analyst
Beginner Data Scientist
Beginner Data Engineer

Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. A data analyst gathers, cleans, and studies data sets to help solve problems. Here’s how you can start on a path to become one.

Data analytics is the process of examining data sets to extract information that may be hidden from the casual user and draw conclusions about the impact of that information. Data analytics can range from the simplest of business intelligence, to online analytical processing, all the way up to very advanced analytics.

A data analyst collects, cleans, and interprets data sets in order to answer a question or solve a problem. They can work in many industries, including business, finance, criminal justice, science, medicine, and government.

Data analysis is the process of gleaning insights from data to help inform better business decisions. The process of analysing data typically moves through five iterative phases:

Identify the data you want to analyse
Collect the data
Clean the data in preparation for analysis
Analyse the data
Interpret the results of the analysis

Data analysis can take different forms, depending on the question you’re trying to answer. Briefly, descriptive analysis tells us what happened, diagnostic analysis tells us why it happened, predictive analytics forms projections about the future, and prescriptive analysis creates actionable advice on what actions to take.

Business intelligence (BI) combines business analytics, data mining, data visualisation, data tools and infrastructure, and best practices to help organisations to make more data-driven decisions. In practice, you know you’ve got modern business intelligence when you have a comprehensive view of your organisation’s data and use that data to drive change, eliminate inefficiencies and quickly adapt to market or supply changes. Business intelligence comprises the strategies and technologies used by enterprises for the data analysis and management of business information.

Course Curriculum

Chapter 1: Python and Jupiter Notebook Setup

Lecture 1: Introduction

Lecture 2: What is Jupiter Notebook

Lecture 3: Installing Jupiter Notebook Server

Lecture 4: Running Jupiter Notebook Server

Lecture 5: Common Jupiter Notebook Commands

Lecture 6: Jupiter Notebook Components

Lecture 7: Jupiter Notebook Dashboard

Lecture 8: Creating a new notebook

Lecture 9: Jupiter Notebook User Interface

Chapter 2: Data Analysis and Visualisation with Python and Pandas

Lecture 1: Introduction to Kaggle Datasets

Lecture 2: Tabular Data

Lecture 3: Exploring Pandas DataFrame

Lecture 4: Manipulating Pandas DataFrame

Lecture 5: What is data cleaning

Lecture 6: Performing Data Cleaning

Lecture 7: What is Visualisation

Lecture 8: Qualitative Data Visualisation

Lecture 9: Quantitative Data Visualisation

Chapter 3: Power BI Setup

Lecture 1: Introduction to Data Analytics

Lecture 2: What is Power BI

Lecture 3: What is Power BI Desktop

Lecture 4: Installing Power BI Desktop

Lecture 5: Microsoft 365 Setup

Lecture 6: Exploring Microsoft 365

Lecture 7: Add users to Microsoft 365

Chapter 4: Power BI Overview

Lecture 1: Exploring Power BI Interface

Lecture 2: Power BI Overview: Part 1

Lecture 3: Power BI Overview: Part 2

Lecture 4: Power BI Overview: Part 3

Lecture 5: Components of Power BI

Lecture 6: Building blocks of Power BI

Lecture 7: Exploring Power BI Service

Lecture 8: Power BI Apps

Chapter 5: Connecting Power BI to Data Source

Lecture 1: Connecting to web based data

Lecture 2: Clean and transform data: Part 1

Lecture 3: Clean and transform data: Part 2

Lecture 4: Combining Data Source

Chapter 6: Create Visualizations and Dashboards with Power BI

Lecture 1: Creating Visualizations: Part 1

Lecture 2: Creating Visualizations: Part 2

Lecture 3: Publish report and create dashboards

Chapter 7: Transforming Data with Power BI and Power Query

Lecture 1: Connecting to Data Source

Lecture 2: Power Query Editor and Queries

Lecture 3: Creating and managing query groups

Lecture 4: Renaming Queries

Lecture 5: Splitting Columns

Lecture 6: Changing Data Types

Lecture 7: Removing and Reordering Columns

Lecture 8: Duplicating and Adding Columns

Lecture 9: Creating Conditional Columns

Lecture 10: Connecting to files in a folder

Lecture 11: Appending Queries

Lecture 12: Merge Queries

Lecture 13: Query Dependency View

Lecture 14: Transform less structured data

Lecture 15: Create Tables

Lecture 16: Query Parameters

Chapter 8: Data Modelling with Power BI

Lecture 1: What is Data Modelling

Lecture 2: Creating and managing Data Relationships

Lecture 3: Creating Calculated Columns

Lecture 4: Optmizing Models for Reporting

Lecture 5: Time Intelligence

Lecture 6: Applying Filters to Visuals

Chapter 9: Tableau Setup

Lecture 1: What is Tableau

Lecture 2: Tableau Public Desktop

Lecture 3: Tableau Public Desktop Overview: Part 1

Lecture 4: Tableau Public Desktop Overview: Part 2

Lecture 5: Tableau Online

Lecture 6: Tableau Data Sources

Lecture 7: Tableau File Types

Lecture 8: Tableau Interface

Chapter 10: Data Analysis and Visualisation with Tableau

Lecture 1: Connecting to data source

Lecture 2: Join related data sources

Lecture 3: Join data sources with inconsistent fields

Lecture 4: Cleaning data

Lecture 5: Reordering Visualisation

Lecture 6: Change Summary

Lecture 7: Split text into multiple columns

Lecture 8: Presenting data using stories

Chapter 11: SQL Server Setup

Lecture 1: What is SQL Server

Lecture 2: SQL Server Editions

Lecture 3: SQL Server Installation Requirements

Lecture 4: Download SQL Server

Lecture 5: Install SQL Server

Lecture 6: Install SSMS

Lecture 7: Connect SSMS to SQL Server

Lecture 8: Create a database

Lecture 9: What is SQL

Lecture 10: Data Preparation

Lecture 11: Importing Datasets into database

Instructors

Tech Academy
Real Skills For The Real World

Rating Distribution

1 stars: 0 votes
2 stars: 5 votes
3 stars: 27 votes
4 stars: 39 votes
5 stars: 64 votes

Frequently Asked Questions

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