Become a Data Analyst – Power BI | SQL | Python | Tableau

Become a Data Analyst – Power BI | SQL | Python | Tableau, available at $49.99, has an average rating of 4.32, with 84 lectures, based on 96 reviews, and has 1347 subscribers.

You will learn about Connect to multiple data sources Analyse data and create visualization with Power BI Create dashboards with Power BI Publish reports to Power BI Service Connect Power BI to databases Data preparation and exploration with SQL Write CTE queries to explore data with SQL Create temp tables and views with SQL Analyse and extract data with Python Learn skills to scrape data from web pages Create data visualizations with Tableau This course is ideal for individuals who are Beginner Data Analyst or Beginner Data Scientist It is particularly useful for Beginner Data Analyst or Beginner Data Scientist.

Enroll now: Become a Data Analyst – Power BI | SQL | Python | Tableau

Summary

Title: Become a Data Analyst – Power BI | SQL | Python | Tableau

Price: $49.99

Average Rating: 4.32

Number of Lectures: 84

Number of Published Lectures: 84

Number of Curriculum Items: 84

Number of Published Curriculum Objects: 84

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will Learn

Connect to multiple data sources
Analyse data and create visualization with Power BI
Create dashboards with Power BI
Publish reports to Power BI Service
Connect Power BI to databases
Data preparation and exploration with SQL
Write CTE queries to explore data with SQL
Create temp tables and views with SQL
Analyse and extract data with Python
Learn skills to scrape data from web pages
Create data visualizations with Tableau

Who Should Attend

Beginner Data Analyst
Beginner Data Scientist

Target Audiences

Beginner Data Analyst
Beginner Data Scientist

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 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 analyst tasks and responsibilities

A data analyst is a person whose job is to gather and interpret data in order to solve a specific problem. The role includes plenty of time spent with data, but entails communicating findings too.

Here’s what many data analysts do on a day-to-day basis:

Gather data: Analysts often collect data themselves. This could include conducting surveys, tracking visitor characteristics on a company website, or buying datasets from data collection specialists.

Clean data: Raw data might contain duplicates, errors, or outliers. Cleaning the data means maintaining the quality of data in a spreadsheet or through a programming language so that your interpretations won’t be wrong or skewed.

Model data: This entails creating and designing the structures of a database. You might choose what types of data to store and collect, establish how data categories are related to each other, and work through how the data actually appears.

Interpret data: Interpreting data will involve finding patterns or trends in data that will help you answer the question at hand.

Present: Communicating the results of your findings will be a key part of your job. You do this by putting together visualizations like charts and graphs, writing reports, and presenting information to interested parties.

Course Curriculum

Chapter 1: Power BI Setup

Lecture 1: Introduction

Lecture 2: Introduction to Data Analytics

Lecture 3: What is Power BI

Lecture 4: Install Power BI Desktop

Lecture 5: Exploring Power BI Desktop

Lecture 6: Office 365 setup

Lecture 7: Getting started with Office365

Lecture 8: Adding users to Office365

Chapter 2: Overview of Power BI

Lecture 1: Power BI Overview : Part 1

Lecture 2: Power BI Overview : Part 2

Lecture 3: Power BI Overview : Part 3

Lecture 4: Components of Power BI

Lecture 5: Building blocks of Power BI

Lecture 6: Exploring Power BI Desktop interface

Lecture 7: Power BI Apps

Chapter 3: Clean Transform and Visualize Data

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 sources

Lecture 5: Creating Visuals in Power BI: Part 1

Lecture 6: Creating Visuals in Power BI: Part 2

Lecture 7: Publishing Report to Power BI Service

Chapter 4: Connecting Databases to Power BI

Lecture 1: Download SQL Server

Lecture 2: Install SQL Server

Lecture 3: Install SSMS

Lecture 4: Connect SSMS to SQL Server

Lecture 5: Attach sample database

Lecture 6: Connect SQL Server to Power BI

Lecture 7: Install PostgreSQL

Lecture 8: Connect to PostgreSQL Server

Lecture 9: Attach sample database to PostgreSQL

Lecture 10: Connect PostgreSQL to Power BI : Part 1

Lecture 11: Connect PostgreSQL to Power BI : Part 2

Chapter 5: Data Exploration with SQL – COVID Datasets

Lecture 1: Create a database

Lecture 2: Data Preparation

Lecture 3: Importing Datasets into database

Lecture 4: How many continents do we have data for

Lecture 5: Possibility of dying from COVID

Lecture 6: Percentage of population infected with COVID

Lecture 7: Countries with highest infection

Lecture 8: Countries with highest deaths

Lecture 9: Continents with highest death

Lecture 10: Global COVID Cases and death

Lecture 11: Number of people vaccinated

Lecture 12: Data Exploration with CTE

Lecture 13: Data Exploration with Temporary Tables

Lecture 14: Data Exploration with Views

Chapter 6: Tableau Setup

Lecture 1: What is Tableau

Lecture 2: Tableau Public Desktop

Lecture 3: Tableau Public Overview: Part 1

Lecture 4: Tableau Public Overview: Part 2

Lecture 5: Tableau Online

Lecture 6: Tableau Data Sources

Lecture 7: Tableau File Types

Lecture 8: Tableau Help Menu

Chapter 7: Data Analysis and Visualization with Tableau

Lecture 1: Connecting to data source

Lecture 2: Join related data sources

Lecture 3: Join data sources with inconsistent fields

Lecture 4: Data cleaning

Lecture 5: Tableau interface

Lecture 6: Reordering Visualization

Lecture 7: Change summary

Chapter 8: Python Setup

Lecture 1: Install Python on Windows

Lecture 2: Install Python on Macs

Lecture 3: Install Visual studio code

Lecture 4: Create a virtual environment on Windows

Lecture 5: Updating pip

Lecture 6: Activate virtual environment on Windows

Lecture 7: Create a virtual environment on Macs

Lecture 8: Activate virtual environment on Macs

Lecture 9: Install Beautiful soup

Chapter 9: Data Extraction with Python ( Web Scraping )

Lecture 1: What is web scraping

Lecture 2: What we will scrape ( extract )

Lecture 3: Inspecting elements

Lecture 4: How internet works

Lecture 5: What is http

Lecture 6: Building a web scraping script : Part 1

Lecture 7: Building a web scraping script : Part 2

Lecture 8: Prototyping the script: part 1

Lecture 9: Prototyping the script: part 2

Lecture 10: Prototyping the script: part 3

Lecture 11: Prototyping the script: part 4

Lecture 12: Prototyping the script: part 5

Lecture 13: Saving extracted data

Instructors

Skill Tree
Skill based learning

Rating Distribution

1 stars: 2 votes
2 stars: 6 votes
3 stars: 12 votes
4 stars: 36 votes
5 stars: 40 votes

Frequently Asked Questions

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