Python® for Data Science for Beginners
(PYTHON-DS.AE1)/ISBN:978-1-64459-462-9
This course includes
Lessons
TestPrep
Hand-on Lab
Lessons
23+ Lessons | 40+ Exercises | 170+ Quizzes | 76+ Flashcards | 76+ Glossary of terms
TestPrep
Hand on lab
30+ LiveLab | 15+ Video tutorials | 24+ Minutes
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Here's what you will learn
Download Course OutlineLessons 1: Introduction
- About This Course
- False Assumptions
- Icons Used in This Course
- Where to Go from Here
Lessons 2: Discovering the Match between Data Science and Python
- Defining the Sexiest Job of the 21st Century
- Creating the Data Science Pipeline
- Understanding Python’s Role in Data Science
- Learning to Use Python Fast
Lessons 3: Introducing Python’s Capabilities and Wonders
- Why Python?
- Working with Python
- Performing Rapid Prototyping and Experimentation
- Considering Speed of Execution
- Visualizing Power
- Using the Python Ecosystem for Data Science
Lessons 4: Setting Up Python for Data Science
- Considering the Off-the-Shelf Cross-Platform Scientific Distributions
- Installing Anaconda on Windows
- Installing Anaconda on Linux
- Installing Anaconda on Mac OS X
- Downloading the Datasets and Example Code
Lessons 5: Working with Google Colab
- Defining Google Colab
- Getting a Google Account
- Working with Notebooks
- Performing Common Tasks
- Using Hardware Acceleration
- Executing the Code
- Viewing Your Notebook
- Sharing Your Notebook
- Getting Help
Lessons 6: Understanding the Tools
- Using the Jupyter Console
- Using Jupyter Notebook
- Performing Multimedia and Graphic Integration
Lessons 7: Working with Real Data
- Uploading, Streaming, and Sampling Data
- Accessing Data in Structured Flat-File Form
- Sending Data in Unstructured File Form
- Managing Data from Relational Databases
- Interacting with Data from NoSQL Databases
- Accessing Data from the Web
Lessons 8: Conditioning Your Data
- Juggling between NumPy and pandas
- Validating Your Data
- Manipulating Categorical Variables
- Dealing with Dates in Your Data
- Dealing with Missing Data
- Slicing and Dicing: Filtering and Selecting Data
- Concatenating and Transforming
- Aggregating Data at Any Level
Lessons 9: Shaping Data
- Working with HTML Pages
- Working with Raw Text
- Using the Bag of Words Model and Beyond
- Working with Graph Data
Lessons 10: Putting What You Know in Action
- Contextualizing Problems and Data
- Considering the Art of Feature Creation
- Performing Operations on Arrays
Lessons 11: Getting a Crash Course in MatPlotLib
- Starting with a Graph
- Setting the Axis, Ticks, Grids
- Defining the Line Appearance
- Using Labels, Annotations, and Legends
Lessons 12: Visualizing the Data
- Choosing the Right Graph
- Creating Advanced Scatterplots
- Plotting Time Series
- Plotting Geographical Data
- Visualizing Graphs
Lessons 13: Stretching Python’s Capabilities
- Playing with Scikit-learn
- Performing the Hashing Trick
- Considering Timing and Performance
- Running in Parallel on Multiple Cores
Lessons 14: Exploring Data Analysis
- The EDA Approach
- Defining Descriptive Statistics for Numeric Data
- Counting for Categorical Data
- Creating Applied Visualization for EDA
- Understanding Correlation
- Modifying Data Distributions
Lessons 15: Reducing Dimensionality
- Understanding SVD
- Performing Factor Analysis and PCA
- Understanding Some Applications
Lessons 16: Clustering
- Clustering with K-means
- Performing Hierarchical Clustering
- Discovering New Groups with DBScan
Lessons 17: Detecting Outliers in Data
- Considering Outlier Detection
- Examining a Simple Univariate Method
- Developing a Multivariate Approach
Lessons 18: Exploring Four Simple and Effective Algorithms
- Guessing the Number: Linear Regression
- Moving to Logistic Regression
- Making Things as Simple as Naïve Bayes
- Learning Lazily with Nearest Neighbors
Lessons 19: Performing Cross-Validation, Selection, and Optimization
- Pondering the Problem of Fitting a Model
- Cross-Validating
- Selecting Variables Like a Pro
- Pumping Up Your Hyperparameters
Lessons 20: Increasing Complexity with Linear and Nonlinear Tricks
- Using Nonlinear Transformations
- Regularizing Linear Models
- Fighting with Big Data Chunk by Chunk
- Understanding Support Vector Machines
- Playing with Neural Networks
Lessons 21: Understanding the Power of the Many
- Starting with a Plain Decision Tree
- Making Machine Learning Accessible
- Boosting Predictions
Lessons 22: Ten Essential Data Resources
- Discovering the News with Subreddit
- Getting a Good Start with KDnuggets
- Locating Free Learning Resources with Quora
- Gaining Insights with Oracle’s Data Science Blog
- Accessing the Huge List of Resources on Data Science Central
- Learning New Tricks from the Aspirational Data Scientist
- Obtaining the Most Authoritative Sources at Udacity
- Receiving Help with Advanced Topics at Conductrics
- Obtaining the Facts of Open Source Data Science from Masters
- Zeroing In on Developer Resources with Jonathan Bower
Lessons 23: Ten Data Challenges You Should Take
- Meeting the Data Science London + Scikit-learn Challenge
- Predicting Survival on the Titanic
- Finding a Kaggle Competition that Suits Your Needs
- Honing Your Overfit Strategies
- Trudging Through the MovieLens Dataset
- Getting Rid of Spam E-mails
- Working with Handwritten Information
- Working with Pictures
- Analyzing Amazon.com Reviews
- Interacting with a Huge Graph
Hands-on LAB Activities
Conditioning Your Data
- Checking the Version of Pandas
- Creating Categorical Variables
- Finding the Missing Data
- Encoding Missingness
- Sorting and Shuffling
- Creating n-grams
- Calculating TF-IDF
- Modifying Graphs Using NetworkX
- Creating an Adjacency Matrix Using NetworkX
- Defining a Plot
- Creating a Line Plot
- Creating a Legend
- Creating a Pie Chart
- Creating a Scatterplot
- Creating an Undirected Graph
- Using Parallel Coordinates
- Calculating Descriptive Statistics
- Visualizing the Validation Curve
- Visualizing a Subset of Images
- Adding New Cases and Variables
Shaping Data
- Extracting a Telephone Number
Putting What You Know in Action
- Using Vectorization
- Performing Matrix Multiplication
Stretching Python’s Capabilities
- Building a Predictor
Exploring Data Analysis
- Loading the Iris Dataset
Reducing Dimensionality
- Creating a Numpy Array
Clustering
- Understanding Centroid-Based Algorithms
Exploring Four Simple and Effective Algorithms
- Using K-Nearest Neighbors and PCA
Performing Cross-Validation, Selection, and Optimization
- Loading the Boston Housing Dataset
Understanding the Power of the Many
- Optimizing the Depth of Decision Tree