Become a Data Analyst – (Python, Excel, SQL, Power BI )

Become a Data Analyst – (Python, Excel, SQL, Power BI ), available at $69.99, has an average rating of 4.33, with 104 lectures, 8 quizzes, based on 1345 reviews, and has 8398 subscribers.

You will learn about Perform data analysis & visualization with Python Perform data analysis & visualization with Excel Perform data exploration and analysis with SQL Perform data analysis & visualization with Power BI Write SQL Queries to explore and analyse data Connect to multiple data sources with Power BI Clean & transform data Create Dashboards with Power BI Write SQL temporary table queries to extract and query data Write SQL CTE queries to extract and query data 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 – (Python, Excel, SQL, Power BI )

Summary

Title: Become a Data Analyst – (Python, Excel, SQL, Power BI )

Price: $69.99

Average Rating: 4.33

Number of Lectures: 104

Number of Quizzes: 8

Number of Published Lectures: 104

Number of Published Quizzes: 8

Number of Curriculum Items: 112

Number of Published Curriculum Objects: 112

Original Price: $94.99

Quality Status: approved

Status: Live

What You Will Learn

Perform data analysis & visualization with Python
Perform data analysis & visualization with Excel
Perform data exploration and analysis with SQL
Perform data analysis & visualization with Power BI
Write SQL Queries to explore and analyse data
Connect to multiple data sources with Power BI
Clean & transform data
Create Dashboards with Power BI
Write SQL temporary table queries to extract and query data
Write SQL CTE queries to extract and query data

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.

Data analysts are in high demand across all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education.

The ability to pay attention to detail, communicate well and be highly organised are essential skills for data analysts. They not only need to understand the data, but be able to provide insight and analysis through clear visual, written and verbal communication.

Some responsibilities of a data analyst includes:

Developing records management processes and policies
identify areas to increase efficiency and automation of processes
set up and maintain automated data processes
identify, evaluate and implement external services and tools to support data validation and cleansing
produce and track key performance indicators
develop and support reporting processes
monitor and audit data quality
liaise with internal and external clients to fully understand data content
gather, understand and document detailed business requirements using appropriate tools and techniques
design and carry out surveys and analyse survey data
manipulate, analyse and interpret complex data sets relating to the employer’s business
prepare reports for internal and external audiences using business analytics reporting tools
create data dashboards, graphs and visualisations
provide sector and competitor benchmarking
mine and analyse large datasets, draw valid inferences and present them successfully to management using a reporting tool

In this course we will perform some task of a Data Analyst using Python ,Excel, SQL, and Power BI. We will connect to a variety of data sources, perform data transformation ,cleaning and exploration . We will create dashboards to visual data .

Course Curriculum

Chapter 1: Python and Jupyter Notebook Setup

Lecture 1: Introduction

Lecture 2: What is Data Analysis

Lecture 3: What is Python

Lecture 4: What is Jupyter Notebook

Lecture 5: Installing Jupyter Notebook Server

Lecture 6: Running Jupyter Notebook Server

Lecture 7: Jupyter Notebook Commands

Lecture 8: Jupyter Notebook Components

Lecture 9: Jupyter Notebook Dashboards

Lecture 10: Creating a new notebook

Lecture 11: Jupyter Notebook user interface

Chapter 2: Data Analysis & Visualization with Python & Jupyter Notebook

Lecture 1: Kaggle Datasets

Lecture 2: Tabular data

Lecture 3: Exploring Pandas Data frame

Lecture 4: Manipulating Pandas Data frame

Lecture 5: What is data cleaning

Lecture 6: Performing data cleaning

Lecture 7: What is data visualization

Lecture 8: Qualitative data visualization

Lecture 9: Quantitative data visualization

Chapter 3: Data Analysis & Visualization with Excel

Lecture 1: What is Power Pivot

Lecture 2: Versions of Power Pivot

Lecture 3: Enabling Power Pivot in Excel

Lecture 4: What is Power query

Lecture 5: Please Read

Lecture 6: Connecting to data source

Lecture 7: Preparing your query

Lecture 8: Cleansing data

Lecture 9: Enhancing your query

Lecture 10: Creating a data model

Lecture 11: Build data relationships

Lecture 12: Create lookups as new fields with DAX

Lecture 13: Analyse data using Pivot tables

Lecture 14: Analyse data with Pivot charts

Lecture 15: Refreshing source data

Lecture 16: Updating queries

Lecture 17: Creating new reports

Chapter 4: Microsoft SQL Server Setup

Lecture 1: What is SQL Server

Lecture 2: SQL Server Editions

Lecture 3: Download SQL Server

Lecture 4: Install SQL Server

Lecture 5: Install SSMS

Lecture 6: Connect SSMS to SQL Server

Lecture 7: Create a database

Chapter 5: Data Exploration & Analysis with SQL

Lecture 1: Data Preparation

Lecture 2: Importing datasets into database

Lecture 3: How many continents do we have data for

Lecture 4: Possibility of dying from COVID

Lecture 5: Percent of population infected with COVID

Lecture 6: Countries with highest infection

Lecture 7: Countries with highest COVID deaths

Lecture 8: Continents with highest COVID deaths

Lecture 9: Global Covid deaths

Lecture 10: Number vaccinated against COVID

Lecture 11: Exploring data with temporary tables

Lecture 12: Exploring data with views

Lecture 13: Exploring data with CTE

Chapter 6: Power BI Setup

Lecture 1: What is Power BI

Lecture 2: what is Power BI Desktop

Lecture 3: Install Power BI Desktop

Lecture 4: Explore Power BI Interface

Lecture 5: Microsoft 365 setup

Lecture 6: Exploring Microsoft 365

Lecture 7: Add users to Microsoft 365

Chapter 7: Power BI Overview

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: Power BI Apps

Chapter 8: Data Analysis & Visualization with Power BI

Lecture 1: Connect to data source

Lecture 2: Clean & transform data : part 1

Lecture 3: Clean & transform data : part 2

Lecture 4: Combine data source

Lecture 5: Create visualization : Part 1

Lecture 6: Create visualization : Part 2

Lecture 7: Publish report to Power BI Service

Chapter 9: Analyse & consume database data with Power BI

Lecture 1: Connect to SQL Server with Power BI

Lecture 2: What is PostgreSQL

Lecture 3: Install PostgreSQL

Lecture 4: Connect to PostgreSQL

Lecture 5: Install sample database

Lecture 6: Connect to PostgreSQL with Power BI : Part 1

Lecture 7: Connect to PostgreSQL with Power BI : Part 2

Instructors

247 Learning
An investment in knowledge pays the best interest

Rating Distribution

1 stars: 20 votes
2 stars: 39 votes
3 stars: 205 votes
4 stars: 473 votes
5 stars: 608 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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