Python Foundations for Data Analysis Business Intelligence

Python Foundations for Data Analysis Business Intelligence

What you’ll learn

Master the building blocks of base Python, including data types, variables, loops, functions and more
Learn how to use Jupyter Notebooks to write, manage, and comment your Python code
Analyze and manipulate numeric data, text strings, lists, tuples, dictionaries and sets
Explore raw data using conditional logic, nested loops, custom functions, and comprehensions
Use Python’s Openpyxl package to read & write data to Excel worksheets
Build solid, foundational Python skills for data analysis & business intelligence

Requirements

No Python or programming experience required (we’ll cover everything you need to know!)
We’ll use Anaconda & Jupyter Notebooks (a free, user-friendly coding environment)

Description

This is a hands-on, project-based course designed to help you master the core building blocks of Python for data analysis and business intelligence.We’ll start by introducing the Python language and ecosystem, installing Anaconda and Jupyter Notebooks where we’ll write our first lines of code, and reviewing key Python data types and properties.From there we’ll dive into foundational tools like variables, numeric and string operators, loops, custom functions, and more. You’ll learn how to create and manipulate raw data, define conditional logic, loop through iterables or indices, and extract values stored in a wide variety of data types including dictionaries, lists, tuples, and more.Throughout the course you’ll play the role of a Data Analytics Intern for Maven Ski Shop, the world’s #1 store for skis, snowboards and winter gear. Using the skills you learn throughout the course, you’ll help the Maven team track inventory, pricing, and sales performance using your Python skills.COURSE OUTLINE:Why Python for Analytics?Introduce the Python analytics ecosystem and why it’s the programming tool of choice for many data analystsJupyter NotebooksInstall Anaconda and create your first Jupyter Notebook, a user-friendly Python coding environment designed for data analysisPython Data TypesIntroduce native Python data types, common use cases, type conversion methods, and key concepts like iteration and mutabilityVariablesLearn how to name and store values in memory using variables, as well as how to overwrite, delete and track themNumeric DataLearn how to work with numeric data, and use numeric functions to perform a range of arithmetic operationsStringsLearn how to manipulate text via indexing and slicing, calculate string lengths, apply various string methods, and print f-strings to include variablesConditional LogicLearn how to use IF statements and Boolean operators to establish conditional logic and control the flow of your programsSequence Data TypesLearn how to create, modify, and nest lists, tuples, and ranges, all of which allow you to store many values within a single variableLoopsUnderstand the logic behind For and While loops and learn how to refine loop logic and handle common errorsDictionaries & SetsAddress the limitations of working with lists and explore common scenarios for using dictionaries and sets in their placeFunctionsLearn how to create custom functions in Python to boost productivity, and how to import external functions stored in modules or packagesManipulating Excel SheetsImport the openpyxl package and manipulate data from an Excel worksheet using the Python skills you’ve learned throughout the courseFinal ProjectImport and manipulate data from an Excel workbookJoin today and get immediate, lifetime access to the following:11+ hours of high-quality videoPython Foundations PDF ebook (300+ pages)Downloadable project files & solutionsExpert support and Q&A forum30-day money-back guaranteeIf you’re an Analyst, Data Scientist, or Business Intelligence professional looking to build a strong Python foundation and add powerful skills to your resume, this is the course for you!Happy learning!-Chris Bruehl (Python Expert & Lead Instructor, Maven Analytics)__________Looking for our full business intelligence stack? Search for “Maven Analytics” to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!See why our courses are among the TOP-RATED on Udemy:”Some of the BEST courses I’ve ever taken. I’ve studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I’ve seen!” Russ C.”This is my fourth course from Maven Analytics and my fourth 5-star review, so I’m running out of things to say. I wish Maven was in my life earlier!” Tatsiana M.”Maven Analytics should become the new standard for all courses taught on Udemy!” Jonah M.

Overview

Section 1: Getting Started

Lecture 1 Course Structure & Outline

Lecture 2 READ ME: Important Notes for New Students

Lecture 3 DOWNLOAD: Course Resources

Lecture 4 Introducing the Course Project

Lecture 5 Setting Expectations

Section 2: 1. Why Python for Analytics?

Lecture 6 What is Python?

Lecture 7 Python for Data Analysis

Lecture 8 The Python Analytics Ecosystem

Lecture 9 Data Roles that use Python

Section 3: 2. Jupyter Notebooks

Lecture 10 Jupyter Notebooks Intro & Install

Lecture 11 Launching Jupyter & Creating a Notebook

Lecture 12 The Jupyter Interface

Lecture 13 The Code Cell

Lecture 14 Comments & Markdown

Lecture 15 The Print Function & Function Help

Lecture 16 ALTERNATIVE: Google Colab

Lecture 17 Helpful Resources & Key Takeaways

Section 4: 3. Python Data Types

Lecture 18 Python Data Types

Lecture 19 The Type Function & Type Conversion

Lecture 20 DEMO: Type Function & Conversion

Lecture 21 Iterables & Mutability

Section 5: 4. Variables

Lecture 22 Intro to Variables

Lecture 23 DEMO: Variable Assignment

Lecture 24 ASSIGNMENT: Assigning Variables

Lecture 25 SOLUTION: Assigning Variables

Lecture 26 Overwriting Variables

Lecture 27 Deleting Variables

Lecture 28 DEMO: Overwriting & Deleting Variables

Lecture 29 Variable Naming Rules

Lecture 30 Keeping Track of Variables

Lecture 31 DEMO: Naming & Tracking Variables

Lecture 32 ASSIGNMENT: Variable Naming Rules

Lecture 33 SOLUTION: Variable Naming Rules

Lecture 34 Key Takeaways

Section 6: 5. Numeric Data

Lecture 35 Intro to Numeric Data

Lecture 36 Numeric Data Types

Lecture 37 Numeric Type Conversion

Lecture 38 Arithmetic Operators & Order of Operations

Lecture 39 DEMO: Numeric Data

Lecture 40 ASSIGNMENT: Arithemetic Operators

Lecture 41 SOLUTION: Arithmetic Operators

Lecture 42 Numeric Functions

Lecture 43 DEMO: Numeric Functions

Lecture 44 ASSIGNMENT: Numeric Functions

Lecture 45 SOLUTION: Numeric Functions

Lecture 46 Key Takeaways

Section 7: 6. Strings

Lecture 47 Intro to Strings

Lecture 48 String Arithmetic

Lecture 49 DEMO: String Creation & Arithmetic

Lecture 50 String Indexing

Lecture 51 DEMO: String Indexing

Lecture 52 ASSIGNMENT: String Indexing

Lecture 53 SOLUTION: String Indexing

Lecture 54 String Slicing

Lecture 55 DEMO: String Slicing

Lecture 56 ASSIGNMENT: String Slicing

Lecture 57 SOLUTION: String Slicing

Lecture 58 The Length Function

Lecture 59 ASSIGNMENT: The Length Function

Lecture 60 SOLUTION: The Length Function

Lecture 61 String Methods

Lecture 62 DEMO: String Methods

Lecture 63 ASSIGNMENT: String Methods

Lecture 64 SOLUTION: String Methods

Lecture 65 F-Strings

Lecture 66 DEMO: F-Strings

Lecture 67 ASSIGNMENT: F-Strings

Lecture 68 SOLUTION: F-Strings

Lecture 69 Key Takeaways

Section 8: 7. Conditional Logic

Lecture 70 The Boolean Data Type

Lecture 71 Comparison Operators & Membership Tests

Lecture 72 Boolean Operators

Lecture 73 DEMO: Boolean Data Types & Operators

Lecture 74 ASSIGNMENT: Boolean Operators

Lecture 75 SOLUTION: Boolean Operators

Lecture 76 The IF Statement & Control Flow

Lecture 77 Else & Elif Statements

Lecture 78 DEMO: If, Else, Elif

Lecture 79 ASSIGNMENT: Control Flow

Lecture 80 SOLUTION: Control Flow

Lecture 81 Nested If Statements

Lecture 82 ASSIGNMENT: Nested If Statements

Lecture 83 SOLUTION: Nested If Statements

Lecture 84 Key Takeaways

Section 9: 8. Sequence Data Types

Lecture 85 Sequence & List Basics

Lecture 86 List Operations

Lecture 87 DEMO: List Operations

Lecture 88 ASSIGNMENT: List Operations

Lecture 89 SOLUTION: List Operations

Lecture 90 Modifying Lists

Lecture 91 DEMO: Modifying Lists

Lecture 92 ASSIGNMENT: Adding List Elements

Lecture 93 SOLUTION: Adding List Elements

Lecture 94 ASSIGNMENT: Removing List Elements

Lecture 95 SOLUTION: Removing List Elements

Lecture 96 List Methods & Functions

Lecture 97 DEMO: List Methods & Functions

Lecture 98 ASSIGNMENT: List Methods & Functions

Lecture 99 SOLUTION: List Methods & Functions

Lecture 100 Nesting & Copying Lists

Lecture 101 DEMO: Nested Lists & Copying Lists

Lecture 102 ASSIGNMENT: Nested Lists & Copying Lists

Lecture 103 SOLUTION: Nested Lists & Copying Lists

Lecture 104 Tuples

Lecture 105 DEMO: Tuples

Lecture 106 ASSIGNMENT: Tuples

Lecture 107 SOLUTION: Tuples

Lecture 108 Ranges

Lecture 109 DEMO: Ranges

Lecture 110 ASSIGNMENT: Ranges

Lecture 111 SOLUTION: Ranges

Lecture 112 Key Takeaways

Section 10: 9. Loops

Lecture 113 Loop Basics

Lecture 114 For Loops & Looping Over Items

Lecture 115 DEMO: For Loops

Lecture 116 Looping Over Indices & Multiple Iterables

Lecture 117 DEMO: Looping Over Indices

Lecture 118 PRO TIP: Enumerate

Lecture 119 DEMO: Enumerate

Lecture 120 ASSIGNMENT: For Loops

Lecture 121 SOLUTION: For Loops

Lecture 122 ASSIGNMENT: Enumerate

Lecture 123 SOLUTION: Enumerate

Lecture 124 While Loops

Lecture 125 DEMO: While Loops

Lecture 126 ASSIGNMENT: While Loops

Lecture 127 SOLUTION: While Loops

Lecture 128 Nested Loops

Lecture 129 DEMO: Nested Loops

Lecture 130 ASSIGNMENT: Nested Loops

Lecture 131 SOLUTION: Nested Loops

Lecture 132 Loop Control

Lecture 133 Break, Continue & Pass

Lecture 134 Try-Except

Lecture 135 DEMO: Loop Control

Lecture 136 ASSIGNMENT: Loop Control

Lecture 137 SOLUTION: Loop Control

Lecture 138 Key Takeaways

Section 11: 10. Dictionaries & Sets

Lecture 139 Intro to Dictionaries

Lecture 140 Dictionary Overview

Lecture 141 Accessing & Modifying Dictionary Values

Lecture 142 DEMO: Dictionary Operations

Lecture 143 ASSIGNMENT: Dictionary Basics

Lecture 144 SOLUTION: Dictionary Basics

Lecture 145 ASSIGNMENT: Dictionary Creation

Lecture 146 SOLUTION: Dictionary Creation

Lecture 147 Keys & Values Methods

Lecture 148 Get, Items & Update Methods

Lecture 149 DEMO: Dictionary Methods

Lecture 150 ASSIGNMENT: Dictionary Methods

Lecture 151 SOLUTION: Dictionary Methods

Lecture 152 The Zip Function

Lecture 153 ASSIGNMENT: The Zip Function

Lecture 154 SOLUTION: The Zip Function

Lecture 155 Nested Dictionaries

Lecture 156 DEMO: Nested Dictionaries

Lecture 157 ASSIGNMENT: Nested Dictionaries

Lecture 158 SOLUTION: Nested Dictionaries

Lecture 159 Intro to Sets

Lecture 160 DEMO: Sets

Lecture 161 ASSIGNMENT: Sets

Lecture 162 SOLUTION:Sets

Lecture 163 Set Operations

Lecture 164 Set Use Cases

Lecture 165 DEMO: Set Operations

Lecture 166 ASSIGNMENT: Set Operations

Lecture 167 SOLUTION: Set Operations

Lecture 168 Key Takeaways

Section 12: 11. Functions

Lecture 169 Intro to Functions

Lecture 170 Anatomy of a Function

Lecture 171 Defining Functions

Lecture 172 The Docstring

Lecture 173 DEMO: Defining a Function

Lecture 174 ASSIGNMENT: Defining a Function

Lecture 175 SOLUTION: Defining a Function

Lecture 176 Argument Types

Lecture 177 DEMO: Argument Types

Lecture 178 Return Values

Lecture 179 DEMO: Return Values

Lecture 180 Variable Scope

Lecture 181 DEMO: Variable Scope

Lecture 182 Creating Modules

Lecture 183 Importing Modules

Lecture 184 DEMO: Creating & Importing Modules

Lecture 185 ASSIGNMENT: Creating a Module

Lecture 186 SOLUTION: Creating a Module

Lecture 187 ASSIGNMENT: Importing a Function

Lecture 188 SOLUTION: Importing a Function

Lecture 189 Importing External Functions

Lecture 190 PRO TIP: Naming Conflicts

Lecture 191 Installing & Managing Packages

Lecture 192 DEMO: Installing Packages

Lecture 193 The Map Function

Lecture 194 ASSIGNMENT: The Map Function

Lecture 195 SOLUTION: The Map Function

Lecture 196 Lambda Functions

Lecture 197 DEMO: Lambda Functions

Lecture 198 ASSIGNMENT: Lambda Functions

Lecture 199 SOLUTION: Lambda Functions

Lecture 200 PRO TIP: Comprehensions

Lecture 201 DEMO: List Comprehensions

Lecture 202 ASSIGNMENT: List Comprehensions

Lecture 203 SOLUTION: List Comprehensions

Lecture 204 PRO TIP: Dictionary Comprehensions

Lecture 205 DEMO: Dictionary Comprehensions

Lecture 206 ASSIGNMENT: Dictionary Comprehensions

Lecture 207 SOLUTION: Dictionary Comprehensions

Lecture 208 PRO TIP: Comprehensions vs. Map()

Lecture 209 Key Takeaways

Section 13: 12. Manipulating Excel Sheets

Lecture 210 The Openpyxl Package

Lecture 211 Navigating Excel Workbooks, Worksheets & Cells

Lecture 212 DEMO: Navigating Excel Workbooks With Python

Lecture 213 ASSIGNMENT: Missing Sales Tax

Lecture 214 SOLUTION: Missing Sales Tax

Lecture 215 Determining Ranges & Writing to Cells

Lecture 216 DEMO: Writing To Excel From Python

Lecture 217 ASSIGNMENT: Pound & Yen Columns

Lecture 218 SOLUTION: Pound & Yen Columns

Lecture 219 Inserting & Deleting Columns

Lecture 220 Saving Workbooks

Lecture 221 Bringing it All Together

Lecture 222 Key Takeaways

Section 14: 13. Final Course Project

Lecture 223 Welcome to the Final Project!

Lecture 224 SOLUTION: Final Project (Part 1)

Lecture 225 SOLUTION: Final Project (Part 2)

Section 15: BONUS LESSON

Lecture 226 BONUS LESSON

Analysts or BI professionals looking for a deep introduction to basic Python,Aspiring data scientists who want to build foundational Python programming skills,Anyone interested in learning one of the most popular open source programming languages in the world,Students looking to learn powerful, practical skills with hands-on projects and course demos

Course Information:

Udemy | English | 11h 18m | 2.44 GB
Created by: Maven Analytics

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