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    How To Be A Data Analyst: Essence Of Data Analysis

    Posted By: ELK1nG
    How To Be A Data Analyst: Essence Of Data Analysis

    How To Be A Data Analyst: Essence Of Data Analysis
    Published 12/2023
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 235.82 MB | Duration: 0h 56m

    This is a Beginner's Guide On How To Be A Data Analyst

    What you'll learn

    Master data cleaning, transformation and processing in order to glean insights from it

    Know in depth Data analysis best practises which include Data Requirement Gathering, Data collection, Data Analysis, Data Interpretation, Data Visualisation

    Master Data Analysis techniques like Text Analysis, Statistical Analysis, Diagnostic Analysis, Predictive Analysis and Prescriptive Analysis

    Learn statistical analysis methods such as Mean, Standard deviation, Regression and Hypothesis testing

    Learn the essential chart types needed for Data Visualisation such as bar chart, line chart, bubble plots, Heat maps, Pie charts, Funnel Charts and more

    Requirements

    No programming experience needed, you'll learn everything you need to know in this course

    Description

    A Data analyst is someone who is an expert in cleaning, changing, and processing raw data in order to extract actionable, relevant information that helps businesses make more informed decisionsAccording to Glassdoor labor statistics, the average salary for data analyst is around 80,000 dollars per yearGiven the demand for highly skilled data analysts, there has been a corresponding increase in salaries too.And the demand for data analysts and data science professionals has been growing these past few years and especially so since the Covid-19 pandemic.Data analysis is a process of cleaning, transforming and modelling data to discover useful information for business decision-making. For you to be a Data Analyst you need to be an expert in Data AnalysisThis well-curated online course covers important topics in Data Analysis, as well how companies are making use of these popular tools and techniques to make key decisions in their organisation. Topics covered here include: Data Requirement Gathering, Data Collection, Data Cleaning, Data Analysis, Data Interpretation and  Data VisualisationAlso covered are common statistical analysis methods such as Mean, Standard deviation, Regression, Hypothesis testingAfter taking this Data Analysis online course, you should be able to:Know in depth Data analysis best practises which include Data Requirement Gathering, Data collection, Data Analysis, Data Interpretation, Data VisualisationMaster Data Analysis techniques like Text Analysis, Statistical Analysis, Diagnostic Analysis, Predictive Analysis and Prescriptive AnalysisKnow in details Data Analysis Tools and Techniques, Common Statistical Analysis Methods, Essential Chart Types for Data Visualisation, Data Analysis Myths That Can Hamper Your Business, Companies That Use Big Data Analytics and How Companies Make Use Of  Data Analysis

    Overview

    Section 1: Introduction

    Lecture 1 Introduction

    Lecture 2 Definition and role of Data analysis in the society

    Lecture 3 Why Organisations and individuals need data analysis

    Lecture 4 Definition of Data Analysis

    Lecture 5 What is Data Analysis?

    Lecture 6 Importance of data analysis for businesses

    Lecture 7 How to use data analysis in your business

    Lecture 8 Another reason Data Analysis is important

    Lecture 9 How accurate data improves data analysis

    Lecture 10 Data Analysis Steps

    Lecture 11 Data Requirement Gathering

    Lecture 12 Data Collection

    Lecture 13 Data cleaning

    Lecture 14 Data Analysis tools

    Lecture 15 Data Interpretation

    Lecture 16 Data Visualisation

    Lecture 17 Types Of Data Analysis

    Lecture 18 Diagnostic Analysis

    Lecture 19 Predictive Analysis

    Lecture 20 Prescriptive Analysis

    Lecture 21 Statistical Analysis

    Lecture 22 Text Analysis

    Lecture 23 Data Analysis Methods

    Lecture 24 Qualitative Data Analysis

    Lecture 25 Quantitative Analysis

    Lecture 26 Artificial Intelligence and Machine Learning

    Lecture 27 Maths and Stats

    Lecture 28 Graphs and Visualisation

    Section 2: Data Analysis: Tools and Techniques

    Lecture 29 Purpose of Data Analysis

    Lecture 30 Example of Data Analysis

    Lecture 31 Data Analysis Tools

    Lecture 32 Types Of Data Analysis

    Lecture 33 Text Analysis

    Lecture 34 Statistical Analysis

    Lecture 35 Descriptive Analysis

    Lecture 36 Inferential Analysis

    Lecture 37 Diagnostic Analysis

    Lecture 38 Predictive Analysis

    Lecture 39 Prescriptive Analysis

    Lecture 40 Data Analysis Process

    Lecture 41 Data Requirement Gathering

    Lecture 42 Data Collection

    Lecture 43 Data Cleaning

    Lecture 44 Data Analysis

    Lecture 45 Data Interpretation

    Lecture 46 Data Visualisation

    Lecture 47 Summary

    Section 3: Common Statistical Analysis Methods

    Lecture 48 The Four Common Methods Used For Statistical Analysis

    Lecture 49 Mean

    Lecture 50 How To Calculate Mean

    Lecture 51 Standard Deviation

    Lecture 52 How To Calculate Standard Deviation

    Lecture 53 Variance

    Lecture 54 Regression

    Lecture 55 How To Calculate Regression

    Lecture 56 Hypothesis Testing

    Section 4: Essential Chart Types for Data Visualisation

    Lecture 57 Introduction

    Lecture 58 How To Choose A Chart Type

    Lecture 59 The Foundational Four

    Lecture 60 Bar Chart

    Lecture 61 Line Charts

    Lecture 62 Example Of A Line Chart

    Lecture 63 Scatterplot

    Lecture 64 Data Analysis Steps

    Lecture 65 Example Of A Scatterplot

    Lecture 66 Common Variations

    Lecture 67 Histogram

    Lecture 68 Example Of A Histogram

    Lecture 69 Stacked Bar Chart

    Lecture 70 Example Of A Stacked Bar Chart

    Lecture 71 Bubble Plots

    Lecture 72 Example Of A Bubble Plot

    Lecture 73 Heatmaps

    Lecture 74 Example Of A Heatmap

    Lecture 75 More On Heatmaps

    Lecture 76 Pie Charts

    Lecture 77 More On Pie Charts

    Lecture 78 Funnel Charts

    Lecture 79 Adwords Conversion Funnel

    Lecture 80 Map-Based Plots

    Lecture 81 Choropleth

    Section 5: Data Analysis Myths That Can Hamper Your Business

    Lecture 82 Data Analysis Myths That Can Hamper Businesses

    Lecture 83 Data Analysis Leads To New Discoveries

    Lecture 84 Data Analysis Is To Expensive

    Lecture 85 Data Analysis Is Too Time-Intensive

    Lecture 86 You Need To Be An Analyst To Get Value From Data

    Lecture 87 All Businesses Are Driven By Data

    Lecture 88 There's No Reason To Track Bounce Rate

    Lecture 89 Machine-made Decisions Eliminates Bias

    Lecture 90 Data Analytics Leads To Job Loss

    Lecture 91 The More Data You Gather, The Better

    Lecture 92 Analysis Can Drive Your Business

    Section 6: Companies That Use Big Data Analytics

    Lecture 93 Top Data Analytics Companies Should Follow

    Lecture 94 Reonomy

    Lecture 95 SAP

    Lecture 96 Sedin Technologies

    Lecture 97 Sisense

    Lecture 98 Sigma Data Systems

    Lecture 99 Snowflake

    Lecture 100 Salesforce

    Section 7: How Companies Use Data Analysis

    Lecture 101 How Companies Use Data Analytics

    Lecture 102 Benefits Of Data Analysis

    Lecture 103 Amazon

    Lecture 104 Apple

    Lecture 105 How Apple Uses Big Data

    Lecture 106 Google

    Lecture 107 Spotify

    Lecture 108 Facebook

    Lecture 109 Instagram

    Lecture 110 Starbucks

    Lecture 111 Netflix

    Every student curious about Data analysis