Python for Data Science Bootcamp (NYC or Online)
- Difficulty
- Beginner
- Format
- Live or Self-paced
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Book live on Noble DesktopSummary
This bootcamp takes you from the basics of Python programming through the foundations of data science and into the starting point of machine learning. You’ll begin by learning Python fundamentals including variables, data types, functions, and control flow before moving into essential tools like NumPy and Pandas for working with arrays and dataframes.
From there, you’ll explore data wrangling, descriptive statistics, and exploratory analysis as well as creating visualizations with Matplotlib. By the end, you’ll have the skills to clean, analyze, and visualize data in Python and will be prepared to continue into machine learning with algorithms such as logistic regression, k nearest neighbors, and decision trees.
Who this course is for
Individuals looking to break into the field of data science with Python People with minimal coding background who want to move into more data-centric work at their current workplace People who work with data in tools like SPSS, STATA, or MATLAB and would like to transition into using Python and SQL. Developers with experience in other arenas, who would like to work in data science
Topics included
Curriculum
What you'll learn
- Learn Python fundamentals, including variables, data types, functions, loops, and control flow, for building robust programs
- Work with complex data structures such as dictionaries and lists to efficiently organize and access data
- Use NumPy and Pandas to import, clean, and manipulate datasets for analysis and exploration
- Generate descriptive statistics and apply filtering, grouping, and pivoting techniques to gain deeper insights
- Visualize data using Matplotlib and create clear, customized charts, including bar graphs, histograms, and scatter plots
- Gain the practical skills needed to transition into machine learning with a solid understanding of data science workflows
Course syllabus
Python Fundamentals
Python Fundamentals: Variables & Data Types
- Declare variables of basic types: integers, floats, strings, booleans
- Perform input/output with print() and input()
- Apply arithmetic, relational, and logical operators
Control Flow I: Conditional Logic
- Use Boolean operators ==, !=, <, >, <=, >=
- Write if/else and nested conditionals
- Combine conditions with and/or for complex logic
Control Flow II: Loops & Iteration
- Implement for loops over ranges and lists; understand iterables
- Understand map and filter operations.
- Use list comprehensions to simplify operations.
DataFrames & Data Manipulation with Pandas
- Construct DataFrames from various data formats via pd.DataFrame()
- Concatenate multiple DataFrames using pd.concat()
- Inspect DataFrame shape and handle missing values (NaN)
- Perform Panda data analysis operations to glean insight
Data Visualization: Charting Basics
- Plot time series with plt.plot() for line charts
- Create scatter plots using plt.scatter() to reveal correlations
- Decide between line vs. scatter based on data context and purpose
Trend Analysis with Regression Lines
- Understand least-squares regression concept and its interpretation
- Compute a best-fit line via numpy.polyfit()
- Overlay regression lines on scatter plots and make predictions
Advanced Plot Customization
- Annotate charts with titles, axis labels, and legends
- Highlight key data points (e.g., min/max) directly on plots
- Use stacked bar charts, pie charts, and animated charts to visualize data
What's included
- Free course retake within one year to refresh the material and gain practice.
- Class recordings
Stated by the provider. Confirm what your tuition covers before enrolling.
Live classes
Choose dates and book your instructor-led class on nobledesktop.com.
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In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
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In-person or live online
Starts at 18:00 · America/New_York (EST); final session ends at 21:00 EST
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In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
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In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
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In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
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In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
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In-person or live online
Starts at 18:00 · America/New_York (EDT); final session ends at 21:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 18:00 · America/New_York (EDT); final session ends at 21:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EDT); final session ends at 17:00 EDT
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In-person or live online
Starts at 10:00 · America/New_York (EST); final session ends at 17:00 EST
Confirm current dates and availability on Noble Desktop before booking.
Self-paced course
Learn through recorded lessons on your own schedule.
This course is available for 30 days. You can choose when to start your access period. Once you activate, you will have 30 days to complete it (access the course materials, quizzes, projects and videos). You may request one extension of seven (7) days. Other extension requests will be evaluated on a case-by-case basis. Videos are not downloadable.
- Tuition
- $1,495
- Course length
- 30 hours
- Schedule
- On your schedule
Our bootcamp is meant to go from the very basics of Python programming to the start of machine learning with Python. In this Bootcamp, you’ll learn how and why Python is used for data science, how to create programs, work with data in Python, create data visualizations, and use statistics to create machine learning models.
Python Fundamentals
The course will start with the fundamentals of Python, including writing basic statements and expressions, creating variables, understanding different data types, working with lists, indexing and slicing lists, using functions and methods, and more. Concepts such as object-oriented programming are introduced.
Once a learning environment has been set up, we will work with different data types such as strings, lists, dictionaries, and tuples. Each data type has its own particular purpose and knowing when to use each one will be essential.
Structuring Programs
The second part of the course covers conditional statements and control flow tools. This includes the if/else statements, boolean operations, and different types of loops. These topics create a large portion of the logic in your code and this course will help you master these concepts. Learn to work with dictionaries, create functions, write for loops to iterate through data, and work with packages in Python.
Arrays & DataFrames
The third part of the course introduces operations and tools for data science. We will learn how to import and clean data using NumPy and Pandas. You’ll learn to work with Pandas DataFrames, wrangle data, and get descriptive statistics for your data.
Analyzing & Visualizing Data
You’ll learn to analyze and visualize data with key data science libraries including Pandas, NumPy, and Matplotlib. Learn to filter and clean data, group and pivot data, and start generating insights from your data with exploratory data analysis. Then create visualizations including bar charts, histograms, and advanced visualization for easy interpretation and sharing of your data insights.
Next Steps
After learning all the foundational Python programming and data analysis skills in this bootcamp, you will be ready to dive fully into machine learning.
This program builds off this foundational knowledge to turn you into a full machine learning data scientist. Pick up right where the Python for Data Science Bootcamp left off with advanced statistics and create machine learning models with logistic regressions, k-nearest neighbors, and decision trees.
Who the self-paced course is for
Individuals looking to break into the field of data science with Python People with minimal coding background who want to move into more data-centric work at their current workplace People who work with data in tools like SPSS, STATA, or MATLAB and would like to transition into using Python and SQL. Developers with experience in other arenas, who would like to work in data science
Self-paced curriculum
What you'll learn self-paced
- Foundational programming concepts, including loops, functions, and objects
- Handle different types of data such as integers, floats, and strings
- Control the flow of your programs with conditional statements
- Reuse and simplify code with object-oriented programming
- Analyze tabular data with NumPy and Pandas
- Create graphs and visualizations with Matplotlib
- Make predictions with linear regression, using scikit-learn
Self-paced syllabus
Python Fundamentals
Data Types & Variables
- Statements & Expressions
- Variables
- Integers & Floats
- Strings
Data Structures & Attributes
- Functions & Methods
- Lists
- Indexing & Slicing
- Booleans
Structuring Programs
Complex Data Structures & Control Flow
- Dictionaries
- Conditional Statements
- For Loops
- Creating Functions
Packages & Object Oriented Programming
- Classes & Objects
- Modules & Imports
- Packages & Documentation
Arrays & Dataframes
Numpy
- Arrays
- Universal Functions
- Boolean Indexing
Pandas
- Pandas Dataframes
- Pandas Series & dtypes
- Column Manipulation
- Descriptive Statistics
Analyzing & Visualizing Data
Data Analysis
- Filtering & Cleaning Data
- Groupby Operations
- Pivot Tables
Data Visualization
- Plotting with Matplotlib
- Bar Charts
- Scatter Plots
- Histograms
- Customizing Visualizations
What's included with self-paced
- Self-paced video lessons
Self-paced lessons
Watch free previews and explore the lessons included with enrollment at Noble Desktop.
Python for Data Science Course Online (Self-Paced)
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Introduction to Python for Data Science
Lesson 1: Python for Data Science Fundamentals
9:06
Download, unzip, and upload course files to Google Drive, then open and edit copies in Google Colab with AI assistance turned off and line numbers enabled to support hands-on coding practice.
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Variables
Lesson 1: Python for Data Science Fundamentals
21:19
Explain how variables, data types, and string operations function in Python programming.
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Integer and Decimal
Lesson 1: Python for Data Science Fundamentals
20:07
Differentiate integers and decimals, apply math operators, convert between strings and numeric types, and manage string concatenation in Python.
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Input Function
Lesson 1: Python for Data Science Fundamentals
8:30
Capture user input with the input function, convert values to integers or floats, apply the correct order of operations, and use modulus to convert total minutes into hours and minutes.
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Input Function Part 2
Lesson 1: Python for Data Science Fundamentals
4:39
Apply the modulus operator to format movie duration by separating hours from remaining minutes.
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Round Function
Lesson 1: Python for Data Science Fundamentals
6:00
Process numerical input by converting strings to floats, rounding values, and calculating percentages in practical scenarios such as billing.
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Cafe Bill Challenge Solution
Lesson 1: Python for Data Science Fundamentals
14:12
Calculate a café bill by collecting food, beverage, and tip inputs, computing subtotals, tax, and tip amounts separately, formatting currency values, and presenting a final total.
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Boolean
Lesson 1: Python for Data Science Fundamentals
5:32
Explain Boolean values and comparison operators, and how they evaluate conditions as true or false.
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List Data Type
Lesson 1: Python for Data Science Fundamentals
15:52
Describe how lists work in Python, including creation, indexing, slicing, and accessing nested lists.
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Methods Functions
Lesson 1: Python for Data Science Fundamentals
18:45
Explain object methods with a focus on list methods such as append, pop, sort, and extend, and distinguish methods from standalone functions.
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Set & Vector Operation
Lesson 1: Python for Data Science Fundamentals
14:37
Use sets to manage unique values, remove duplicates from lists, and perform basic set and list operations.
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If Else Logic
Lesson 2: Boolean Conditions
4:43
Apply if, elif, and else statements with Boolean logic to control program flow based on conditions.
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If Else Logic Challenge Solution
Lesson 2: Boolean Conditions
16:35
Write Python code that uses conditional logic to evaluate prices and manipulate lists using logical operators such as and and or.
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Datetime Part 1
Lesson 3: Datetime & Random
10:28
Summarize Python modules and demonstrate importing libraries to extend functionality, including random number generation and timezone handling with datetime and pytz.
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Datetime Part 2
Lesson 3: Datetime & Random
11:35
Convert and format time values into military and AM/PM formats and generate conditional greetings based on the current hour.
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Random Module
Lesson 3: Datetime & Random
20:22
Use the random module to generate numbers, sample unique values, shuffle lists, and complete exercises involving scores, dates, and lottery selections.
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Loops
Lesson 4: Loops & Strings
4:29
Explore loops by iterating over lists and ranges with for loops while avoiding infinite loops.
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Loop the Nums List Challenge
Lesson 4: Loops & Strings
9:17
Loop through numbers one through ten, square even numbers, cube odd numbers, and print the results.
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List Methods Review Part 1
Lesson 4: Loops & Strings
13:11
Practice list manipulation by extending lists, inserting and removing elements, popping by index, removing duplicates, and rearranging items without sorting.
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List Methods Review Part 2
Lesson 4: Loops & Strings
9:03
Insert specific elements into a list, remove items at given indices, deduplicate values using sets, sort lists, and maintain alphabetical order through targeted insertion.
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Loop Method Exercise
Lesson 4: Loops & Strings
10:23
Loop through a list to create transformed values, store results in a new list, and selectively print items while skipping defined positions.
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Loop Challenges
Lesson 4: Loops & Strings
9:37
Apply conditional logic to generate different outputs based on string length or other criteria, such as creating jelly beans, popsicles, jams, or lollipops.
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In and Not In Operators
Lesson 4: Loops & Strings
16:23
Use in and not in operators to test membership in strings and lists, and apply conditional logic to categorize items based on starting or ending characters.
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String Methods
Lesson 4: Loops & Strings
34:06
Manipulate strings using methods such as lower, upper, capitalize, replace, and split, while creating modified copies since strings are immutable.
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Nested Loop
Lesson 4: Loops & Strings
11:01
Create and shuffle a standard 52-card deck using nested loops and simulate dealing hands in a blackjack-style game.
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Dictionaries Part 1
Lesson 5: Dictionaries
10:16
Explore Python dictionaries by working with key-value pairs, nested structures, and list data within dictionaries.
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Dictionaries Part 2
Lesson 5: Dictionaries
10:39
Add, update, rename, and delete dictionary keys and values, and iterate through dictionary contents.
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Dictionaries Scrabble Exercise
Lesson 5: Dictionaries
13:11
Build a Scrabble score calculator that converts user input to uppercase, looks up letter values in a dictionary, accumulates points, and outputs a final score.
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Numpy
Lesson 6: Numpy
4:33
Introduce NumPy by importing it as np and using it to convert lists into multi-dimensional arrays for advanced data manipulation.
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Numpy Array
Lesson 6: Numpy
18:34
Transform lists into NumPy arrays to enable reshaping, transposing, and other array-based operations.
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2D Selections with Numpy Array
Lesson 6: Numpy
18:27
Perform two-dimensional selection and slicing in NumPy arrays, including creating matrices and extracting subarrays.
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Pandas Dataframes Part 1
Lesson 7: Pandas Dataframes
5:21
Create and analyze pandas DataFrames by converting lists and dictionaries into tabular structures for data analysis.
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Pandas Dataframes Part 2
Lesson 7: Pandas Dataframes
10:18
Load external images and display structured data such as a chessboard using pandas DataFrames with customized rows and columns.
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Pandas Dataframes Part 3
Lesson 7: Pandas Dataframes
14:01
Select specific rows and columns from a DataFrame using the iloc method.
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Pandas Dataframes Part 4
Lesson 7: Pandas Dataframes
12:21
Manipulate DataFrame sections to model chessboard layouts by slicing rows and columns and assigning pieces through indexing.
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Pandas Dataframes Part 5
Lesson 7: Pandas Dataframes
6:51
Arrange structured data, such as chess pieces or food items, into DataFrames with associated attributes.
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Pandas Dataframes Part 6
Lesson 7: Pandas Dataframes
12:17
Create DataFrames by defining column names and value lists of equal length to ensure proper row alignment.
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Dataframes Location
Lesson 7: Pandas Dataframes
16:20
Access and modify DataFrame data using loc for labels and iloc for integer-based indexing.
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Select Rows by Condition
Lesson 7: Pandas Dataframes
24:16
Filter DataFrames with Boolean conditions, update or remove rows and columns, and use describe to generate statistical summaries.
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AT & IAT
Lesson 7: Pandas Dataframes
13:29
Use at and iat for efficient scalar value assignment, apply string filtering with str.contains, and clean text data with str.replace.
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Dataframes from Dictionary
Lesson 7: Pandas Dataframes
25:15
Create DataFrames from dictionaries, concatenate multiple DataFrames, clean mismatched columns, and manage indices appropriately.
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Dataframes Challenges
Lesson 7: Pandas Dataframes
6:28
Add new rows to a DataFrame, then create filtered DataFrames based on calorie limits or multi-word names.
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String Split
Lesson 7: Pandas Dataframes
12:39
Split strings into lists using delimiters, remove file extensions, and manipulate string components with list operations.
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Select and Filter by Condition
Lesson 7: Pandas Dataframes
21:09
Filter and update DataFrames using multiple logical conditions and create new columns based on calculated values.
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Vector Operations
Lesson 7: Pandas Dataframes
9:16
Perform vectorized operations on DataFrame columns, sort and rename columns, and rearrange column order using pop and insert.
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Introduction to Matplotlib
Lesson 8: Bar Charts with Matplotlib
14:46
Use Matplotlib to create charts and visualizations, load CSV data into pandas, connect to Google Drive, and calculate summary statistics.
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Python Data Selection
Lesson 8: Bar Charts with Matplotlib
18:01
Review slicing techniques with loc and iloc, shuffle and split datasets, and group and sort data by defined criteria.
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Data Visualization with Matplotlib
Lesson 8: Bar Charts with Matplotlib
21:21
Create bar charts with Matplotlib, label bars clearly, sort data by multiple criteria, and adjust visual presentation.
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Line and Scatter Charts with MatPlotlib
Lesson 9: Line & Scatter Charts with Matplotlib
13:56
Plot scatter and line charts with Matplotlib, add regression lines, and highlight minimum and maximum values.
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Linear Regression with Matplotlib
Lesson 9: Line & Scatter Charts with Matplotlib
26:39
Apply NumPy and Matplotlib together to perform linear regression, visualize scatter data, and calculate best-fit lines for analysis.
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Pivot, Plot, Animate
Lesson 10: Pivot Population Data & Charts
9:59
Pivot datasets, generate pie charts and bar chart races, and manage missing data in data science workflows.
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Data Cleaning
Lesson 10: Pivot Population Data & Charts
27:01
Handle missing data by dropping, filling with averages, removing sparse columns, and exporting clean DataFrames to CSV files.
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Two Different Ways to Make a Bar Chart
Lesson 10: Pivot Population Data & Charts
18:52
Create bar charts by extracting and indexing country population data for accurate labeling.
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Pivoting the Data
Lesson 10: Pivot Population Data & Charts
16:20
Pivot population data to restructure years and countries, then visualize trends with line and bar charts.
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There is Always Time for Pie (Charts)
Lesson 10: Pivot Population Data & Charts
10:54
Generate a pie chart illustrating population shares of selected countries and save the visualization as an image file.
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Animated Bar Chart Race
Lesson 10: Pivot Population Data & Charts
13:10
Install and configure the bar chart race library and generate an animated visualization showing population changes over time.
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