Data Analyst Skills For Beginners – (Sql,R,Python,Power Bi )

Gain skills you need to succeed as a data analyst.
Published 2/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.87 GB | Duration: 8h 54m

What you’ll 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
Requirements
No prior coding experience required.
Description
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.
Overview
Section 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 Exploring R Studio Default Interface
Lecture 9 Creating a new project in R Studio
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
Section 2: SQL Server Environment Setup
Lecture 30 What is SQL Server
Lecture 31 What is SQL
Lecture 32 What is T-SQL
Lecture 33 Download SQL Server
Lecture 34 Install SQL Server
Lecture 35 SQL Server Editions
Lecture 36 Install SSMS
Lecture 37 Connect SSMS to SQL Server
Lecture 38 Download Sample Database
Lecture 39 Database Concepts
Lecture 40 Database Normalisation
Lecture 41 Create database
Section 3: Data Exploration with SQL
Lecture 42 Data Preparation
Lecture 43 Importing Datasets into database
Lecture 44 How many continents do we have data for
Lecture 45 What is possibility of dying from COVID
Lecture 46 What percentage of population is infected with COVID
Lecture 47 What countries has highest COVID infection per population
Lecture 48 What countries has the highest deaths from COVID
Lecture 49 What continent has highest deaths from COVID
Lecture 50 What are the global COVID cases and death
Lecture 51 What number of people have been vaccinated against COVID
Lecture 52 Analysing data with SQL CTE
Lecture 53 Using temporary tables for data
Lecture 54 Using Views for data
Section 4: Python Environment Setup
Lecture 55 What is Python
Lecture 56 What is Jupyter Notebook
Lecture 57 Installing Jupyter Notebook Server
Lecture 58 Running Jupyter Notebook Server
Lecture 59 Common Jupyter Notebook Commands
Lecture 60 Jupyter Notebook Components
Lecture 61 Jupyter Notebook Dashboard
Lecture 62 Jupyter Notebook Interface
Lecture 63 Creating a new Jupyter Notebook
Section 5: Data Analysis and visualization with Python
Lecture 64 Kaggle Datasets
Lecture 65 Tabular data
Lecture 66 Exploring Pandas DataFrame
Lecture 67 Analysing and manipulating pandas dataframe
Lecture 68 What is data cleaning
Lecture 69 Basic data cleaning
Lecture 70 Data Visualization
Lecture 71 Visualizing qualitative data
Lecture 72 Visualizing quantitative data
Section 6: Data Analysis & Visualization with Power BI
Lecture 73 Microsoft 365
Lecture 74 Getting started with Microsoft 365
Lecture 75 What is Power BI
Lecture 76 What is Power BI Desktop
Lecture 77 Installing Power BI Desktop
Lecture 78 Exploring Power BI Desktop
Lecture 79 Power BI Overview – Part 1
Lecture 80 Power BI Overview – Part 2
Lecture 81 Power BI Overview – Part 3
Lecture 82 Components of Power BI
Lecture 83 Building blocks of Power BI
Lecture 84 Power BI Service
Lecture 85 Connecting to web data
Lecture 86 Clean and transform data – Part 1
Lecture 87 Clean and transform data – Part 2
Lecture 88 Combining data sources
Lecture 89 Data visualization with Power BI – Part 1
Lecture 90 Data visualization with Power BI – Part 2
Lecture 91 Publishing reports to Power BI Service
Lecture 92 Connect Power BI to SQL Server
Lecture 93 Import SQL Data into Power BI
Lecture 94 Analyze data & create visualization
Lecture 95 How to Publish your report to Power BI Service
Beginner Data Analyst,Beginner Data Scientist

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