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Grokking Data Science
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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
Introduction
Statistical Features - Basics
Statistical Features - Working With Box Plots
Basics of Probability
Bayesian Statistics
Probability Distributions - An Introduction
Types of Distributions - Uniform, Bernoulli, and Binomial
Types of Distributions - Normal
Types of Distributions - Poisson and Exponential
Probability Distributions Recap
Statistical Significance
Quiz: Statistics
Machine Learning 101
Introduction
Understanding Machine Learning
Types of Machine Learning Algorithms
Machine Learning Algorithms I
Machine Learning Algorithms II
Quiz: Machine Learning Algorithms
Evaluating a Model
Quiz: Evaluating a Model
Key Points to Remember
Machine Learning Project Checklist
End-to-End Machine Learning Project
Introduction
Kaggle Challenge - Exploratory Data Analysis
Kaggle Challenge - Data Preprocessing
Kaggle Challenge - Data Transformation
Kaggle Challenge - Machine Learning Models
Kaggle Challenge - Fine Tune Parameters
Kaggle Challenge - Present, Launch and Maintain the System
Assignment
Further Study Material
The Real Talk
How to Get That High-Paying Job
Imposter Syndrome
Final Thoughts
Exercises: NumPy
We'll cover the following
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!
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