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All LessonsFree Lessons (4)
Python Fundamentals for Data Science
Creating the Workspace - Jupyter Notebooks
Python Libraries
Learning NumPy - An Introduction
NumPy Basics - Creating NumPy Arrays and Array Attributes
NumPy Basics - Array Indexing and Slicing
NumPy Basics - Reshaping and Concatenation
NumPy Arithmetic and Statistics - Computations and Aggregations
NumPy Arithmetic and Statistics - Comparison and Boolean Masks
Exercises: NumPy
Learning Pandas - An Introduction
Pandas Core Components - The Series Object
Pandas Core Components - The DataFrame Object
Pandas DataFrame Operations - Read, View and Extract Information
Pandas DataFrame Operations - Selection, Slicing, and Filtering
Pandas DataFrame Operations - Grouping and Sorting
Pandas DataFrame Operations - Dealing With Missing and Duplicates
Pandas DataFrame Operations - Pivot Tables and Functions
Pandas: Further Readings and Cheat Sheet
Exercises: Pandas
Data Visualization - An Introduction
Data Visualization - Matplotlib Tips
Data Visualization Techniques - Scatter, Line, and Histogram
Data Visualization Techniques - Bar and Box Plot
Data Visualization Cheat Sheet
Quiz: Data Visualization
The Fundamentals of Statistics
IntroductionStatistical Features - BasicsStatistical Features - Working With Box PlotsBasics of ProbabilityBayesian StatisticsProbability Distributions - An IntroductionTypes of Distributions - Uniform, Bernoulli, and BinomialTypes of Distributions - NormalTypes of Distributions - Poisson and ExponentialProbability Distributions RecapStatistical SignificanceQuiz: Statistics
Machine Learning 101
IntroductionUnderstanding Machine LearningTypes of Machine Learning AlgorithmsMachine Learning Algorithms IMachine Learning Algorithms IIQuiz: Machine Learning AlgorithmsEvaluating a ModelQuiz: Evaluating a ModelKey Points to RememberMachine Learning Project Checklist
End-to-End Machine Learning Project
IntroductionKaggle Challenge - Exploratory Data AnalysisKaggle Challenge - Data PreprocessingKaggle Challenge - Data TransformationKaggle Challenge - Machine Learning ModelsKaggle Challenge - Fine Tune ParametersKaggle Challenge - Present, Launch and Maintain the SystemAssignmentFurther Study Material
The Real Talk
How to Get That High-Paying JobImposter SyndromeFinal Thoughts
Grokking Data Science/

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Exercises: NumPy

Exercises: NumPy

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    • Time To Test Your Skills!
        • Q1.Create a null vector (all zeros) of size 10 and set it in the variable called “Z”.
        • Q2. Create a 1D array of numbers from 0 to 9 and set it in the variable called “arr”.
        • Q3. Create a 3x3x3 array with random values and set it in the variable called “arr”.
        • Q4. Create a 10x10 array with random values called “arr4”. Find its minimum and maximum values and set them in the variables called “min_val” and “max_val” respectively.
        • Q5. First create a 1D array with numbers from 1 to 9 and then convert it into a 3x3 grid. Store the final answer in the variable called “grid”.
        • Q6. Replace the maximum value in the given vector, “arr6”, with -1.
        • Q7. Reverse the rows of the given 2D array, “arr7”.
        • Q8. Subtract the mean of each row of the given 2D array, “arr8”, from the values in the array. Set the updated array in “transformed_arr8”.

Time To Test Your Skills!

Q1.Create a null vector (all zeros) of size 10 and set it in the variable called “Z”.

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# Your solution goes here (Z = ....)

Q2. Create a 1D array of numbers from 0 to 9 and set it in the

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