Python® for Data Science for Beginners
- Price: $139.99
- Delivery method: eLearning
- DIR Discount: 20%
Submit form to obtain discount
Test Prep
Features
30+ LiveLab |
15+ Video tutorials |
24+ Minutes
Why choose TOPTALENT?
- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
- Access an expansive range of over 3,000 training courses with a strong focus on Information Technology, Business Applications, and Leadership Development.
- Have confidence in an exceptional 95% approval rating from our students, reflecting outstanding satisfaction with our course content, program support, and overall customer service.
- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
Outline
Lessons 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