Become A Data Analyst - Tableau - Python - Power Bi

What you'll learn
Understand the role and responsibilities of a Data Analyst in various industries.
Gain a foundational understanding of key data analysis and visualization tools, including Tableau, Python, Google Data Studio, and Power BI.
Learn to set up Tableau Public Desktop and navigate its interface for data analysis and visualization projects.
Master the process of connecting to various data sources within Tableau and performing data joins on related datasets.
Acquire the skills to clean and preprocess data in Tableau to ensure accuracy and relevance in analysis.
Develop the ability to create compelling data visualizations and dashboards in Tableau that tell a story or reveal insights.
Install Anaconda and understand the differences between Anaconda and Miniconda for managing Python environments.
Get familiar with Jupyter Notebook as an interactive computational environment for Python programming.
Understand the basics of Python programming, including expressions, statements, data types, and variables.
Learn to work with Python data structures such as lists, tuples, dictionaries, and sets for efficient data manipulation.
Master Python's control structures, including conditional statements and loops, for complex data analysis tasks.
Explore the use of Python functions and modules to organize and reuse code effectively.
Dive into data analysis with Python using the Pandas library for data manipulation and analysis.
Practice data cleaning techniques in Python to prepare datasets for analysis.
Learn the fundamentals of data visualization in Python
Gain an introductory understanding of Google Data Studio for creating interactive reports and dashboards.
Explore the process of connecting Google Data Studio to different data sources and importing data.
Learn to create and customize various types of visualizations in Google Data Studio, including charts, tables, and geo maps.
Understand the basics of Power BI, including setting up Microsoft 365 and installing Power BI Desktop.
Master data transformation and modeling in Power BI to create compelling data visualizations.
Learn the process of publishing reports to Power BI Service and building interactive dashboards.
Acquire foundational knowledge in SQL for querying and analyzing data stored in relational databases.
Understand MySQL database concepts, installation, and the use of MySQL Workbench for database management.
Learn advanced SQL techniques for data analysis, including table joins, subqueries, and the use of aggregate functions to summarize data.
Requirements
No prior experience in data analysis or programming is required. This course starts with foundational concepts, making it accessible for beginners.
Basic Computer Literacy: Comfort with operating computers and navigating the internet will be beneficial.
Familiarity with Excel: While not mandatory, basic knowledge of Excel or any spreadsheet software can be helpful as it introduces concepts like data manipulation and simple formulas, which are foundational to data analysis.
A Computer: A laptop or desktop with internet connectivity is essential for accessing course materials, video lectures, and software used in the course.
Software Installation: You will need to install specific software such as Tableau Public (free version), Anaconda for Python, Google Data Studio (free web-based tool), and Microsoft Power BI Desktop (free version). Installation guides and resources will be provided in the course.
Web Browser: A modern web browser (like Chrome, Firefox, or Edge) will be required to access Google Data Studio and other online resources.
Description
Embark on a transformative journey to become a skilled Data Analyst with our comprehensive course: "Become a Data Analyst - Tableau | Python | Google Data Studio | BI | SQL." This meticulously designed course aims to equip you with the essential tools and techniques of data analysis, visualization, and business intelligence, ensuring you emerge as a proficient data analyst ready to tackle real-world challenges.Course Overview:Introduction to Data Analysis: Dive into the world of data analysis by understanding the pivotal role of a Data Analyst. Explore the responsibilities, tools, and the impact of data analysis in driving business decisions and strategies.Mastering Tableau for Data Visualization: Unlock the power of Tableau, the leading visualization tool, starting from setup to advanced data manipulation techniques. Learn through hands-on exercises on connecting data sources, cleaning data, and crafting compelling stories through visualizations.Python and Jupyter Notebook for Data Analysis: Venture into Python programming, a cornerstone for any Data Analyst. From basic syntax to complex functions, this section covers it all, including an in-depth exploration of Jupyter Notebook for executing Python code in an interactive environment.Exploring Google Data Studio: Navigate through Google Data Studio to create dynamic reports and dashboards. Gain proficiency in importing data, connecting to various data sources, and visualizing data to uncover insights.Analyzing Data with Power BI: Step into the world of Power BI, a premier business intelligence platform. Learn to install, connect to data, transform datasets, and visualize insights, culminating in the publication of reports and dashboards.SQL and MySQL for Data Management and Analysis: Build a strong foundation in SQL and MySQL, from database concepts to advanced data analysis techniques. Master table joins, aggregate functions, and the art of querying databases to extract meaningful information.Advanced Data Analysis Techniques: Elevate your skills with advanced SQL techniques, including inner, left, right, and self joins, subqueries, and the use of aggregate functions to perform complex data analysis.Throughout this course, you'll engage in practical exercises and projects, applying what you've learned in real-world scenarios. Whether you're new to data analysis or looking to enhance your skills, this course offers a path to mastery across the most powerful data analysis and visualization tools available today.Prepare to transform data into actionable insights and propel your career forward as a Data Analyst. Join us on this journey to mastering the art and science of data analysis.
Overview
Section 1: Introduction to Data Analysis
Lecture 1 Introduction
Lecture 2 What is a Data Analyst
Lecture 3 The role and responsibilities of a Data Analyst
Lecture 4 Overview of Data Analysis Tools
Section 2: Introduction to Tableau and Setup
Lecture 5 What is Tableau
Lecture 6 Tableau Public Desktop
Lecture 7 Tableau Public Desktop Overview: Part 1
Lecture 8 Tableau Public Desktop Overview: Part 2
Lecture 9 Tableau Online
Lecture 10 Tableau Data Sources
Lecture 11 Tableau File Types
Section 3: Data Analysis and Visualization with Tableau
Lecture 12 Connecting to a data source
Lecture 13 Join related data sources
Lecture 14 Join data sources with inconsistent fields
Lecture 15 Data Cleaning
Lecture 16 Exploring Tableau Interface
Lecture 17 Reordering Visualization
Lecture 18 Change Summary
Lecture 19 Split text into multiple columns
Lecture 20 Presenting data using stories
Section 4: Python and Jupyter Notebook Setup
Lecture 21 What is Jupyter Notebook
Lecture 22 Anaconda vs Miniconda
Lecture 23 Installing Anaconda on a Mac
Lecture 24 Verify Anaconda installation on mac
Lecture 25 Installing Anaconda on Windows
Lecture 26 Verify Anaconda on Windows
Lecture 27 What is Anaconda Navigator
Lecture 28 Introduction to Anaconda Navigator
Lecture 29 Anaconda Navigator Overview
Lecture 30 Installing Jupyter Notebook using Anaconda
Lecture 31 How to start Jupyter Notebook Server
Lecture 32 Creating a new notebook
Section 5: Python Fundamentals
Lecture 33 What is Python
Lecture 34 Python Expressions
Lecture 35 Python Statements
Lecture 36 Python Comments
Lecture 37 Python Data Types
Lecture 38 Casting Data Types
Lecture 39 Python Variables
Lecture 40 Python List
Lecture 41 Python Tuple
Lecture 42 Python Dictionaries
Lecture 43 Python Operators
Lecture 44 Python Conditional Statements
Lecture 45 Python Loops
Lecture 46 Python Functions
Section 6: Data Analysis with Python
Lecture 47 Kaggle Data Sets
Lecture 48 Tabular Data
Lecture 49 Exploring Pandas DataFrame
Lecture 50 Manipulating a Pandas DataFrame
Lecture 51 What is data cleaning
Lecture 52 Basic data cleaning
Lecture 53 What is data visualization
Lecture 54 Visualizing Qualitative Data
Lecture 55 Visualizing Quantitative Data
Section 7: Data Analysis and visualization with Google Data Studio
Lecture 56 What is Google Data Studio
Lecture 57 How to access Google Data Studio
Lecture 58 Exploring Google Data Studio Interface
Lecture 59 Data sources and connectors
Lecture 60 Importing data into data studio
Lecture 61 Connecting to sample data source
Lecture 62 Creating data visualization
Lecture 63 Importing data into Googlesheets
Lecture 64 Connecting to Googlesheets
Lecture 65 What are dimensions
Lecture 66 What are metrics
Lecture 67 Data refresh frequency
Lecture 68 Exploring edit and view modes in reports
Lecture 69 Creating a Pie Chart
Lecture 70 Creating a Bar Chart
Lecture 71 Adding a table to report
Lecture 72 Sorting data in columns
Lecture 73 Add bars to table metrics columns
Lecture 74 Creating a time series chart
Lecture 75 Customizing a time series chart
Lecture 76 Creating a Geo Chart
Lecture 77 Creating calculated fields
Lecture 78 Data cleaning using calculated fields
Lecture 79 Control Filters
Lecture 80 Adding a date range control
Lecture 81 Formatting your dashboard
Section 8: Analyzing Data and Visualization with Power BI
Lecture 82 What is Power BI
Lecture 83 Microsoft 365 Setup
Lecture 84 Exploring Microsoft 365
Lecture 85 Installing Power BI Desktop
Lecture 86 Exploring Power BI Desktop Interface
Lecture 87 Connecting to data
Lecture 88 Transforming Data
Lecture 89 Data Modelling
Lecture 90 Visualizing Data
Lecture 91 Publishing reports to Power BI Service
Lecture 92 Building a dashboard
Lecture 93 Collaborating and sharing
Section 9: Introduction to MySQL and Setup
Lecture 94 What is SQL
Lecture 95 What is MySQL
Lecture 96 Database Concepts
Lecture 97 Installing MySQL (Windows)
Lecture 98 Installing MySQL (Mac )
Lecture 99 What is MySQL Workbench
Lecture 100 Installing MySQL Workbench (Mac)
Lecture 101 MySQL Data Types
Lecture 102 Overview of using MySQL and SQL for Data Analysis
Lecture 103 Introduction to Databases
Section 10: Data Analysis with SQL
Lecture 104 Introduction to Table Joins
Lecture 105 Analysing data using SQL INNER Join
Lecture 106 Analysing data using SQL LEFT Join
Lecture 107 Analysing data using SQL RIGHT Join
Lecture 108 Analysing data using SQL SELF Join
Lecture 109 Analysing data using Sub Query
Lecture 110 Analysing data using SQL Nested Sub Query
Lecture 111 Introduction to Aggregate functions
Lecture 112 Analysing data using SQL AVG Aggregate Function
Lecture 113 Analysing data using SQL COUNT Aggregate Function
Lecture 114 Analysing data using SQL SUM Aggregate Function
Lecture 115 Analysing data using SQL MIN Aggregate Function
Lecture 116 Analysing data using SQL MAX Aggregate Function
Lecture 117 Aggregate functions in SQL GROUPBY Clause
Lecture 118 Aggregate functions in SQL HAVING Clause
Lecture 119 Filtering data with the WHERE Clause
Lecture 120 Sorting data with ORDER BY Clause
Individuals looking to pivot into a data-driven career will find this course an invaluable stepping stone. Whether you're transitioning from a non-technical role or seeking to enter the tech industry, our comprehensive curriculum will guide you through the essentials of data analysis, visualization, and business intelligence tools.,Aspiring data analysts with little to no prior experience in the field are prime candidates for this course. We start with the basics, ensuring that learners gain a solid foundation in data analysis concepts and tools, making the course ideal for those who are starting their journey in data analytics.,College students or recent graduates in fields such as business, economics, computer science, or any other discipline who wish to enhance their data analysis skills will benefit significantly. This course can complement your academic knowledge, providing practical, hands-on experience with tools and techniques used in the industry.,Working professionals in roles that involve data handling, reporting, or decision-making, such as business analysts, marketing professionals, and project managers, will find the course content directly applicable to their work. Enhancing your data analysis skills can lead to improved job performance, opportunities for advancement, or even a specialisation shift within your career.,Entrepreneurs who need to make data-driven decisions to grow their business will benefit from learning how to analyze data effectively. This course will empower you to understand your business data better, identify trends, and make informed decisions.,Individuals with a keen interest in data, technology, and analytics, looking to explore new skills or understand the world of data analysis better, will find the course engaging and enlightening. It's an excellent opportunity for personal growth and intellectual stimulation.
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