Data Analyst Skills for beginners – (SQL,R,Python,Power BI )

Data Analyst Skills for beginners – (SQL,R,Python,Power BI ), available at $74.99, has an average rating of 4.54, with 95 lectures, based on 72 reviews, and has 1328 subscribers.

You will learn about Connect to various data sources Clean and transform data Perform exploratory data analysis Manipulate data using data frames Create visualizations from data Analyse data with SQL Analyse data with Python Analyse data with Power BI Analyse data with R 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: Data Analyst Skills for beginners – (SQL,R,Python,Power BI )

Summary

Title: Data Analyst Skills for beginners – (SQL,R,Python,Power BI )

Price: $74.99

Average Rating: 4.54

Number of Lectures: 95

Number of Published Lectures: 95

Number of Curriculum Items: 95

Number of Published Curriculum Objects: 95

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Connect to various data sources
Clean and transform data
Perform exploratory data analysis
Manipulate data using data frames
Create visualizations from data
Analyse data with SQL
Analyse data with Python
Analyse data with Power BI
Analyse data with R

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, organises and studies data to provide business insight.

Data analyst applies various tools and techniques for data analysis and data visualisation (including the use of business information tools) to identify, collect and migrate data to and from a range of systems manage, clean, abstract and aggregate data alongside a range of analytical studies on that data manipulate and link different data sets summarise and present data and conclusions in the most appropriate format for users.

R is a programming language. R is often used for statistical computing and graphical presentation to analyse and visualize data. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible.

SQL (Structured Query Language) is a programming language designed for managing data in a relational database. It’s been around since the 1970s and is the most common method of accessing data in databases today. SQL has a variety of functions that allow its users to read, manipulate, and change data.

Python is a popular programming language. Python can be used on a server to create web applications and also for data analysis and visualization. Analysing data with Python is an essential skill for Data Scientists and Data Analysts .

Power BI is a cloud-based business analytics service from Microsoft that enables anyone to visualize and analyse data, with better speed and efficiency. It is a powerful as well as a flexible tool for connecting with and analysing a wide variety of data. Power BI also has a desktop version that can be used for data analysis and visualization.

Gain skills you need to succeed as a data analyst! No prior coding experience required.

Course Curriculum

Chapter 1: Setting Up R Environment

Lecture 1: Introduction

Lecture 2: What is R

Lecture 3: Installing R on Windows

Lecture 4: Installing R on Macs

Lecture 5: What is R Studio

Lecture 6: Installing R Studio on Windows

Lecture 7: Installing R Studio on Macs

Lecture 8: Creating a new project in R Studio

Lecture 9: Exploring R Studio Default Interface

Lecture 10: What are Packages

Lecture 11: How to install Packages

Lecture 12: Data sets vs Data frames

Lecture 13: Loading Packages

Lecture 14: Importing data into R Studio

Lecture 15: How to read data in a csv file with R

Lecture 16: Installing Janitor Package

Lecture 17: Cleaning columns

Lecture 18: Selecting a subset of data

Lecture 19: Performing multiple operations using Pipe operator

Lecture 20: Creating new columns from existing columns

Lecture 21: Create a new R Project

Lecture 22: Load data into new project

Lecture 23: What is Data Wrangling

Lecture 24: Data Wrangling steps

Lecture 25: Importance of data wrangling

Lecture 26: Perform Data Wrangling on Data

Lecture 27: Create a scatter plot

Lecture 28: Create a bar graph

Lecture 29: Adding Labels to plots

Chapter 2: SQL Server Environment Setup

Lecture 1: What is SQL Server

Lecture 2: What is SQL

Lecture 3: What is T-SQL

Lecture 4: Download SQL Server

Lecture 5: Install SQL Server

Lecture 6: SQL Server Editions

Lecture 7: Install SSMS

Lecture 8: Connect SSMS to SQL Server

Lecture 9: Download Sample Database

Lecture 10: Database Concepts

Lecture 11: Database Normalisation

Lecture 12: Create database

Chapter 3: Data Exploration with SQL

Lecture 1: Data Preparation

Lecture 2: Importing Datasets into database

Lecture 3: How many continents do we have data for

Lecture 4: What is possibility of dying from COVID

Lecture 5: What percentage of population is infected with COVID

Lecture 6: What countries has highest COVID infection per population

Lecture 7: What countries has the highest deaths from COVID

Lecture 8: What continent has highest deaths from COVID

Lecture 9: What are the global COVID cases and death

Lecture 10: What number of people have been vaccinated against COVID

Lecture 11: Analysing data with SQL CTE

Lecture 12: Using temporary tables for data

Lecture 13: Using Views for data

Chapter 4: Python Environment Setup

Lecture 1: What is Python

Lecture 2: What is Jupyter Notebook

Lecture 3: Installing Jupyter Notebook Server

Lecture 4: Running Jupyter Notebook Server

Lecture 5: Common Jupyter Notebook Commands

Lecture 6: Jupyter Notebook Components

Lecture 7: Jupyter Notebook Dashboard

Lecture 8: Jupyter Notebook Interface

Lecture 9: Creating a new Jupyter Notebook

Chapter 5: Data Analysis and visualization with Python

Lecture 1: Kaggle Datasets

Lecture 2: Tabular data

Lecture 3: Exploring Pandas DataFrame

Lecture 4: Analysing and manipulating pandas dataframe

Lecture 5: What is data cleaning

Lecture 6: Basic data cleaning

Lecture 7: Data Visualization

Lecture 8: Visualizing qualitative data

Lecture 9: Visualizing quantitative data

Chapter 6: Data Analysis & Visualization with Power BI

Lecture 1: Microsoft 365

Lecture 2: Getting started with Microsoft 365

Lecture 3: What is Power BI

Lecture 4: What is Power BI Desktop

Lecture 5: Installing Power BI Desktop

Lecture 6: Exploring Power BI Desktop

Lecture 7: Power BI Overview – Part 1

Lecture 8: Power BI Overview – Part 2

Lecture 9: Power BI Overview – Part 3

Lecture 10: Components of Power BI

Lecture 11: Building blocks of Power BI

Lecture 12: Power BI Service

Lecture 13: Connecting to web data

Lecture 14: Clean and transform data – Part 1

Lecture 15: Clean and transform data – Part 2

Lecture 16: Combining data sources

Lecture 17: Data visualization with Power BI – Part 1

Lecture 18: Data visualization with Power BI – Part 2

Lecture 19: Publishing reports to Power BI Service

Lecture 20: Connect Power BI to SQL Server

Lecture 21: Import SQL Data into Power BI

Lecture 22: Analyze data & create visualization

Instructors

Bluelime Learning Solutions
Making Learning Simple

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 8 votes
4 stars: 31 votes
5 stars: 33 votes

Frequently Asked Questions

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

Can I take my courses with me wherever I go?

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