Master Data Analysis Essentials: Sql, Python & Tableau

Learn the tools and techniques to analyze data, uncover insights, and create stunning visualizations

What you'll learn

Extract data efficiently from relational databases using SQL queries

Apply Python libraries such as Pandas and NumPy to clean, manipulate, and analyze datasets

Design and present compelling visualizations and dashboards using Tableau to communicate data-driven insights effectively

Combine SQL, Python, and Tableau to execute end-to-end data analysis workflows

Utilize key statistical techniques to summarize and interpret data insights

Requirements

Familiarity with using operating systems, file management, and basic software tools

A basic understanding of databases and their structure is helpful but not mandatory

Basic knowledge of Python programming, including variables, loops and functions, is recommended.

Comfort with basic algebra and an understanding of mathematical concepts like averages and percentages

No prior experience with Tableau is required, as the module will cover introductory data visualization skills

Description

Unlock the world of data analysis with our comprehensive course, designed for beginners and professionals eager to gain in-demand skills. Thiss course takes you on an exciting journey through the essential tools and techniques used in the data-driven world today.You will start with SQL, learning how to extract meaningful data from databases using efficient queries. No prior experience? No problem! We'll guide you step by step to help you understand the fundamentals of working with data. Next, you'll dive into Python for Data Analysis, one of the most versatile programming languages for data manipulation and analysis. Using libraries like Pands and Numpy, you will learn how to clean, organize, and analyze datasets to uncover insights that matter.Understanding the story behind the numbers is crucial. That's why we cover Statistics, equipping you with the knowledge to interpret data trends and make informed decisions confidently.Finally, transform raw data into captivating visuals using Tableau, a powerful visualization tool. You'll design interactive dashboards to showcase your findings in a way anyone can understand. By the end of this course, you'll be equipped to turn raw data into actionable insights and advance your career in the growing field of data analysis.Ready to begin? Let's dive in!

Overview

Section 1: MySQL

Lecture 1 Introduction to Databases

Lecture 2 CREATE Table Statement

Lecture 3 SELECT Table Statement

Lecture 4 LIMIT, DISTINCT, COUNT, AVG, SUM

Lecture 5 INSERT Statement

Lecture 6 WHERE Statement

Lecture 7 UPDATE and DELETE Statements

Lecture 8 Using String Patterns and Ranges

Lecture 9 Sorting Result

Lecture 10 Grouping Result

Lecture 11 Built-in Database Functions

Lecture 12 Date and Time Built-in Functions

Lecture 13 Subqueries and Nested Selects

Lecture 14 Working with Multiple Tables

Lecture 15 Relational Model Constraints

Lecture 16 Join Table

Lecture 17 Access Databases Using Python

Lecture 18 MySQL Exercise

Lecture 19 SQL Exercise Solution

Lecture 20 Common Table Expression (CTE) in MYSQL

Lecture 21 Window Function in MySQL

Lecture 22 Advanced MySQL Exercise

Lecture 23 Advanced MySQL Exercise Solution

Section 2: Python for Data Analysis

Lecture 24 Introduction to Python for Data Analysis

Lecture 25 Numpy Arrays

Lecture 26 Numpy Indexing and Selection

Lecture 27 Numpy Operations

Lecture 28 Pandas Series and DataFrame

Lecture 29 Pandas Indexing and Selecting Data

Lecture 30 Pandas for DataFrame Manipulation

Lecture 31 Pandas Functionality

Lecture 32 Pandas Merging, Joining, and Concatenating

Lecture 33 Pandas Operations

Lecture 34 Pandas Data Input and Output

Lecture 35 Introduction to Data Wrangling

Lecture 36 Data Cleansing

Lecture 37 Introduction to Regular Expression

Lecture 38 Exercise

Lecture 39 Solution

Section 3: Statistics

Lecture 40 Introduction to Statistics

Lecture 41 Design Thinking of Statistics

Lecture 42 Descriptive Statistics: Numerical and Table Summary

Lecture 43 Descriptive Statistics: Graphical Summary

Lecture 44 Probability

Lecture 45 Inferential Statistics and Estimation

Lecture 46 Introduction to Hypothesis Testing

Lecture 47 Hypothesis Testing for Mean

Lecture 48 Hypothesis Testing for Proportion and Non-parametric Statistics

Lecture 49 Association

Lecture 50 Exercise

Lecture 51 Solution

Section 4: Python for Data Visualization

Lecture 52 Data Visualization Introduction

Lecture 53 Histogram

Lecture 54 Box Plot

Lecture 55 Line Plot

Lecture 56 Scatter Plot

Lecture 57 Bar Plot

Lecture 58 Pie Chart

Lecture 59 Heatmap

Lecture 60 Folium

Lecture 61 Cohort Data Visualization

Lecture 62 Advanced Data Visualization using Plotly

Lecture 63 Exercise

Lecture 64 Solution

Section 5: Tableau

Lecture 65 Introduction to Tableau & Installation

Lecture 66 Connecting Tableau to Multiple Data Sources

Lecture 67 Line Graph, Bar Graph, and Scatter Plot

Lecture 68 Horizontal Bar Plot and Maps

Lecture 69 Area Graph, Heatmap, Tree map, Pages and Filters

Lecture 70 Story and Dashboard

Lecture 71 Exercise

Lecture 72 Solution

Aspiring data analysts, Individuals looking to start a career in data analsis and want to learn tools like SQL, Python, and Tableau,Professionals who want to leverage data insights for decision-making and communicate them effectively through visualization,Students or recent graduates from any discipline who want to acquire practical data skills to enhance their employability,Career changers, those transitioning to a data-driven role or considering a career switch to analytics,Entrepreneurs and small business ownwers, individuals who want to use data analysis to improve their business performance and operations,Learners with a curious mindset, anyone interested in exploring the basics of data analysis and visualization, even without prior experience