Python To Tableau: Mastering Data Analysis & Visualization

Posted By: ELK1nG

Python To Tableau: Mastering Data Analysis & Visualization
Published 5/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.74 GB | Duration: 8h 52m

Empowering Visual Insights with Tableau Essentials and Python Data Analysis Techniques

What you'll learn

A comprehensive understanding of Tableau's core functionalities and features

Proficiency in creating a wide range of visualizations for diverse data sets.

The ability to design interactive and intuitive dashboards for effective data communication.

Enhanced storytelling skills to convey insights and influence decision-making.

Confidence in utilizing Tableau tA certificate of completion, acknowledging your mastery of Tableau Essentials.o tackle real-world data challenges with finesse.

A solid foundation in data analysis using Python

Practical experience with essential Python libraries: NumPy, pandas, and Plotly

Ability to clean, manipulate, and visualize data effectively

Hands-on experience with an end-to-end data analysis project

Skills to derive actionable insights from real-world datasets

Requirements

No prior experience with Tableau is necessary, but a basic understanding of data analysis concepts is beneficial.

Basic understanding of Python programming

Familiarity with fundamental programming concepts

Description

Course Description:Welcome to the comprehensive journey of mastering data analysis and visualization with Python and Tableau. In this immersive course, you'll explore the essential techniques and tools needed to transform raw data into actionable insights. Beginning with Tableau Essentials, you'll delve into the realm of dynamic reporting, interactive dashboards, and compelling data storytelling. From mastering basic functionalities to crafting intricate visualizations, you'll elevate your proficiency from novice to expert.Whether you're a data analyst, business intelligence professional, or a decision-maker seeking strategic insights, this course equips you with the skills to navigate the complexities of data with confidence and finesse. Transitioning to Python, you'll embark on a comprehensive introduction to data analysis. Covering essential libraries like NumPy and pandas, you'll learn data manipulation, cleaning, and visualization techniques. Through hands-on exercises and an end-to-end project analyzing the Google Playstore dataset, you'll gain practical experience in applying Python's power to real-world data.Join us on this transformative voyage as we unlock the full potential of Tableau and Python, revolutionizing the way you visualize, analyze, and derive insights from data.Who Should Enroll:Data analysts, business analysts, and BI professionals seeking to enhance their visualization and analysis skills.Managers and decision-makers looking to leverage data visualization for strategic insights. Professionals transitioning into roles requiring data interpretation and storytelling. Aspiring Data Analysts and Data Scientists keen on mastering Python for data-driven decision-making.Prerequisites:No prior experience with Tableau or Python is necessary, but a basic understanding of data analysis concepts and Python programming is beneficial.What You'll Gain:Comprehensive understanding of Tableau and Python's core functionalities and features. Proficiency in creating diverse visualizations and conducting data analysis. Ability to design interactive dashboards and effectively communicate insights. Practical experience with real-world datasets through hands-on projects. Skills to derive actionable insights and influence decision-making processes. Certificate of completion, acknowledging your mastery of data analysis and visualization techniques with Tableau and Python.

Overview

Section 1: Tableau Essentials: From Foundations to Visualization Master

Lecture 1 Introduction to Instructor and Tableau

Lecture 2 Importance of Tableau

Lecture 3 What is tableau

Lecture 4 UseCase

Lecture 5 Links for the Course's Materials and Codes

Section 2: Tableau Fundamentals

Lecture 6 Links for the Course's Materials and Codes

Lecture 7 Installation and Setup

Lecture 8 UI Tour

Lecture 9 Import Data from Various Data Sources (Excel, CSV, etc.)

Lecture 10 Explore Data Pane and Data Source Pane

Section 3: Tableau Functions

Lecture 11 Links for the Course's Materials and Codes

Lecture 12 Functions (Presentation Canva)

Lecture 13 Basic Functions (ABS, Ceiling, Floor, Max, Min, Div, ZN)

Lecture 14 String Functions (Contains, endswith, find, findnth, len, lower, upper, split, trim)

Lecture 15 Date Functions (Dateadd, DateDiff, Datename, Day, Month, Year, isdate, Makedate, Maketime, Makedatetime, Now)

Lecture 16 Aggregate Functions (avg, max, median, min, sum)

Lecture 17 Logical Functions (if, and, else, or)

Section 4: Mastering Visualizations

Lecture 18 Links for the Course's Materials and Codes

Lecture 19 Visualizations – Bar Chart

Lecture 20 Visualizations – Line Chart

Lecture 21 Visualizations – Bullet Chart

Lecture 22 Visualizations – Text Table

Lecture 23 Visualizations – Gantt Chart

Lecture 24 Visualizations – Pie Chart

Lecture 25 Visualizations – Scatter Plot

Lecture 26 Visualizations – Area Chart

Lecture 27 Visualizations – Dual Axis Chart

Lecture 28 Visualizations – Bubble Chart

Section 5: Exploring Geographic Mapping

Lecture 29 Links for the Course's Materials and Codes

Lecture 30 Introduction to Geo Data Visualization

Lecture 31 Geographic Mapping

Lecture 32 Customizing Map Layers and Styles

Lecture 33 Creating a Dashboard

Section 6: End-to-End Project Implementation

Lecture 34 Links for the Course's Materials and Codes

Lecture 35 Starting the Projects

Lecture 36 PROJECT Part 1

Lecture 37 PROJECT Part 2

Lecture 38 PROJECT Part 3

Lecture 39 PROJECT Part 4

Lecture 40 PROJECT Part 5

Section 7: Mastering Data Publication and Collaboration with Tableau Cloud

Lecture 41 Links for the Course's Materials and Codes

Lecture 42 Publishing to Tableau Cloud

Section 8: Data Analysis Using Python: Practical Skills and Projects

Lecture 43 Introduction to Course

Lecture 44 Course Content

Lecture 45 Links for the Course's Materials and Codes

Section 9: Introduction to Numpy

Lecture 46 Links for the Course's Materials and Codes

Lecture 47 Introduction to Numpy

Lecture 48 key Features

Lecture 49 Array vs List

Lecture 50 Array Attributes

Lecture 51 Array Slicing Reshaping

Lecture 52 Stacking Adding

Lecture 53 Copy View

Lecture 54 Advance Slicing

Lecture 55 Universal Functions

Section 10: Introduction to Pandas

Lecture 56 Links for the Course's Materials and Codes

Lecture 57 Introduction to Pandas

Lecture 58 Dataframe Methods

Lecture 59 Install Import Read

Lecture 60 Data Filtration

Lecture 61 Sorting Data

Lecture 62 Casting Types

Lecture 63 Arithmetic Operations

Lecture 64 Concat Data

Lecture 65 Duplicate Handling

Lecture 66 Missing Handle

Lecture 67 Groupby Aggregate

Section 11: Data Visualization with Plotly

Lecture 68 Links for the Course's Materials and Codes

Lecture 69 Introduction to Plotly

Lecture 70 Fundamentals Plotly

Lecture 71 Scatter Line Plots

Lecture 72 Pie Bar Histogram Plots

Lecture 73 Cutomizable Plots

Lecture 74 Interactive Subplots

Section 12: Introduction to Project

Lecture 75 Links for the Course's Materials and Codes

Lecture 76 introduction to project

Lecture 77 key Features

Lecture 78 Pre Processing

Lecture 79 Top Installed Categories

Lecture 80 Top Installed Apps

Lecture 81 Top Rated Apps

Lecture 82 Distribution Free vs Paid

Lecture 83 Rating in Free vs Paid

Lecture 84 Price Free vs Paid

Data analysts, business analysts, and BI professionals seeking to enhance their visualization skills.,Managers and decision-makers looking to leverage data visualization for strategic insights.,Professionals transitioning into roles that require data interpretation and storytelling,Students and individuals interested in mastering Tableau for career advancement,Aspiring Data Analysts and Data Scientists,Professionals looking to enhance their data analysis skills,Individuals interested in leveraging Python for data-driven decision making