Python For Data Analysis: Projects To Power Your Resume

Master Python for Data Analysis: Dive into Pandas, Visualization, and Real-World Projects to add to your Resume!

What you'll learn

Complete hands-on projects analyzing real-world data, such as e-commerce sales and social media sentiments.

Master basic Python syntax and data types, setting a strong foundation for advanced data analysis.

Effectively manipulate and clean data using Pandas, preparing for real-world data analysis projects.

Create powerful data visualizations with Matplotlib and Seaborn to derive insights from datasets.

Understand and apply Python's advanced structures like lists, tuples, sets, and dictionaries in data analysis.

Gain introductory knowledge in machine learning, focusing on applications in sentiment analysis.

Develop a portfolio of practical Python projects, demonstrating skills to potential employers in data analysis.

Requirements

Basic Knowledge of Computers

Internet

Description

Launch Your Data Analysis Journey with Real Python Projects!Welcome to an exhilarating ride through the world of Python data analysis, where each line of code you write brings you closer to becoming a data wizard! Learning python can be hard, I've been there. I've designed this course so you learn in practically and complete 5 projects using real data. These projects will look GREAT on your resume!Why Python? Python is not just a programming language; it's a gateway to a universe of possibilities in data analysis, machine learning, and beyond. It's versatile, user-friendly, and, most importantly, in high demand across industries!My Unique Approach: Practical, Project-Based LearningPractical and Hands-On: Forget about dull lectures! Dive head-first into coding exercises and real data challenges.Project-Based Brilliance: Each module introduces a project tied to a real-world scenario, helping you build a portfolio that speaks louder than words.Resume-Ready Projects: Walk away with a portfolio packed with projects like analyzing Amazon sales, dissecting e-commerce patterns, and even getting insights from social media data on trending topics like ChatGPT.Real Data, Real Skills: Work with datasets from actual businesses, learning to clean, manipulate, and visualize data just like a pro data analyst.What's Inside the Course?Python Basics: The ABCs of Python, including syntax, variables, and loops, to solidify your coding foundation.Data Analysis Tools: Become a Pandas powerhouse and a maestro of data manipulation and cleaning.Advanced Python Structures: Lists, tuples, sets, dictionaries – handle them all with finesse!Data Visualization: Paint stories with data using Matplotlib and Seaborn.Introduction to Machine Learning: Dip your toes into the future with sentiment analysis.Comprehensive Curriculum: Covering everything from Python introduction to advanced data analysis techniques.Interactive Coding Exercises: Cement your learning with engaging, hands-on coding challenges.Who Is This Course For?Aspiring data analysts looking to jumpstart their careers.Python enthusiasts eager to apply their skills to real-world projects.Anyone looking to add high-impact projects to their portfolio.Career switchers aiming to break into the data science and analytics field.Your Learning Journey Each step on this journey equips you with critical skills. You'll not just learn Python; you'll think, analyze, and solve problems like a seasoned data analyst. And by the end of this course, you'll have a portfolio that opens doors and a skill set that turns heads.Enroll now and transform from Python learner to Python developer!

Overview

Section 1: Introduction to the Course and Installation

Lecture 1 Introduction to the Course

Lecture 2 Install Python and Anaconda on Windows

Lecture 3 Install Python and Anaconda on Mac

Lecture 4 Accessing the materials needed for the course

Section 2: Introduction to Spyder and Python

Lecture 5 Introduction to Spyder

Lecture 6 Basic Run Through of Python

Lecture 7 Basic Foundations of Python

Section 3: Introduction to Numpy

Lecture 8 Introduction to Numpy

Lecture 9 Calculating Statistics with Numpy

Lecture 10 Indexing and Slicing with Numpy

Section 4: Introduction to Pandas

Lecture 11 Introduction to Pandas

Lecture 12 Accessing Data in a DataFrame

Lecture 13 Grouping and Aggregating Data with DataFrames

Lecture 14 How to Merge DataFrames

Section 5: Project 1 Analyzing Amazon Sales Data

Lecture 15 Analyzing Amazon Sales Data - Introduction

Lecture 16 Importing, Exploring and Cleaning Data

Lecture 17 Aggregating Sales Data

Lecture 18 Renaming Columns and Exporting Data

Lecture 19 Uploading code to Github

Section 6: Project 2 Analyzing E-commerce Orders

Lecture 20 Analyzing E-commerce Orders - Introduction

Lecture 21 Setting the Working Directory in Python

Lecture 22 Loading Data Files and Checking Data Quality

Lecture 23 Handling Missing Values in Python

Lecture 24 Checking for Duplicate Data

Lecture 25 Filtering Data on Python

Lecture 26 Merging and Joining DataFrames

Lecture 27 Creating Data Visualizations

Lecture 28 Editing and Customizing Plots in Python

Lecture 29 Creating a Scatter Plot

Lecture 30 Creating a Stacked Bar Chart

Lecture 31 Creating Boxplots on Python

Lecture 32 Creating Subplots in Python

Section 7: Project 3 Analyzing Pizza Sales

Lecture 33 Analyzing Pizza Sales and Importing Data

Lecture 34 Exploring Data Frames and Descriptive Statistics

Lecture 35 Dealing with Rows and Columns in Pandas

Lecture 36 Understanding Indexing in DataFrames

Lecture 37 Truncating DataFrames and Series in Python

Lecture 38 Filtering DataFrames

Lecture 39 Working with missing data

Lecture 40 Deleting specific rows and columns in a DataFrame

Lecture 41 Sorting DataFrames

Lecture 42 Grouping on Python

Lecture 43 Merging and Concatenating in Python

Lecture 44 Changing cases in Python

Lecture 45 Replacing text in Dataframe Columns

Lecture 46 Removing Whitespaces from Columns

Lecture 47 Generating a boxplot

Lecture 48 Project Closeoff

Section 8: Project 4 Loan Analysis Overview

Lecture 49 Loan Analysis Overview - Introduction

Lecture 50 Importing Data on Python

Lecture 51 Joining Data on Python

Lecture 52 Steps to clean data in Python

Lecture 53 Introduction to Functions in Python

Lecture 54 Creating a Function on the Loan Dataset

Lecture 55 Conditional Statements on Python

Lecture 56 Practical Application of Functions and Conditions

Lecture 57 Working with Conditional Statements and Averages in Functions

Lecture 58 Classes in Python

Lecture 59 Data Visualizations on Python

Lecture 60 Quick Overview of Subplots in Python

Section 9: Project 5 Sentiment Analysis

Lecture 61 Sentiment Analysis - Introduction

Lecture 62 Loading and Reviewing Data

Lecture 63 Detecting Languages and using try and except

Lecture 64 Cleaning Text Data

Lecture 65 Developing a sentiment function

Lecture 66 Creating a Wordcloud

Lecture 67 Creating a countplot for sentiment

Section 10: Conclusion

Lecture 68 Conclusion

This course is for anyone who wants to kickstart their career in Data Analytics,This course is for anyone who wants to learn more about Python,This course is for anyone who wants to learn more about programming languages,This course is for anyone who wants to learn more about data visualizations,This course is for anyone who wants to create a portfolio of coding projects for their resume.