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CCNA 200-301 Pearson uCertify Network Simulator
Cisco 200-301-SIMULATOR.AB1
CCNA 200-301 Pearson uCertify Network Simulator
ISBN: 978-1-61691-837-8Cisco 200-301-SIMULATOR.AB1
Python for Data Science Course for Beginners
Use Python for Data Science – because basic is for spreadsheets!
(PYTHON-DS.AE1) / ISBN : 978-1-64459-462-9This course includes
Interactive Lessons
Gamified TestPrep
Hands-On Labs
About This Course
This Python for data science training course is your map, starting from the ABCs of coding and guiding you through the thrilling experience of data analysis. You’ll encounter Google Colab and Jupyter Notebook along the way. In addition, harness the strength of Numpy and Pandas for data conditioning and visualization by enrolling in this Python for Data Science course. Along the way, you’ll decode complex numbers with machine learning (ML), unearth hidden patterns, and become a professional data scientist.
Skills You’ll Get
- Gain proficiency in Python coding
- Learn to install and use essential Python tools
- Learn data handling and processing from various data sources
- Clean and condition data to maintain accuracy and reliability
- Visualize data with graphs and plots, turning raw numbers into compelling stories
- Apply machine learning to identify patterns and trends in data
- Become proficient in using Google Colab and Jupyter Notebooks to streamline your workflow
- Perform exploratory data analysis (EDA) to picture data better
- Optimize models for better performance and maximum impact
Interactive Lessons
23+ Interactive Lessons | 40+ Exercises | 170+ Quizzes | 76+ Flashcards | 76+ Glossary of terms
Gamified TestPrep
Hands-On Labs
30+ LiveLab | 15+ Video tutorials | 24+ Minutes
1
Introduction
- About This Course
- False Assumptions
- Icons Used in This Course
- Where to Go from Here
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
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
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
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
6
Understanding the Tools
- Using the Jupyter Console
- Using Jupyter Notebook
- Performing Multimedia and Graphic Integration
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
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
9
Shaping Data
- Working with HTML Pages
- Working with Raw Text
- Using the Bag of Words Model and Beyond
- Working with Graph Data
10
Putting What You Know in Action
- Contextualizing Problems and Data
- Considering the Art of Feature Creation
- Performing Operations on Arrays
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
12
Visualizing the Data
- Choosing the Right Graph
- Creating Advanced Scatterplots
- Plotting Time Series
- Plotting Geographical Data
- Visualizing Graphs
13
Stretching Python’s Capabilities
- Playing with Scikit-learn
- Performing the Hashing Trick
- Considering Timing and Performance
- Running in Parallel on Multiple Cores
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
15
Reducing Dimensionality
- Understanding SVD
- Performing Factor Analysis and PCA
- Understanding Some Applications
16
Clustering
- Clustering with K-means
- Performing Hierarchical Clustering
- Discovering New Groups with DBScan
17
Detecting Outliers in Data
- Considering Outlier Detection
- Examining a Simple Univariate Method
- Developing a Multivariate Approach
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
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
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
21
Understanding the Power of the Many
- Starting with a Plain Decision Tree
- Making Machine Learning Accessible
- Boosting Predictions
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
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
1
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
2
Shaping Data
- Extracting a Telephone Number
3
Putting What You Know in Action
- Using Vectorization
- Performing Matrix Multiplication
4
Stretching Python’s Capabilities
- Building a Predictor
5
Exploring Data Analysis
- Loading the Iris Dataset
6
Reducing Dimensionality
- Creating a Numpy Array
7
Clustering
- Understanding Centroid-Based Algorithms
8
Exploring Four Simple and Effective Algorithms
- Using K-Nearest Neighbors and PCA
9
Performing Cross-Validation, Selection, and Optimization
- Loading the Boston Housing Dataset
10
Understanding the Power of the Many
- Optimizing the Depth of Decision Tree
Any questions?Check out the FAQs
Learn everything you need to know about our beginner’s guide to Python data science.
Contact Us NowYou need to know how to write basic Python code, work with libraries like Pandas and NumPy, and understand data structures and basic algorithms.
No prerequisites needed! This Python for data science for beginners course starts from scratch, so no prior programming experience is needed. Just bring your curiosity and enthusiasm.
All you need is a fast WiFi connection, a modern browser, and a willingness to learn. We’ll guide you through everything else.
No, this Python for data science course is designed for individual study, but there are tons of online forums and communities where you can share your progress and get help if needed.
The big ones you’ll use are Pandas, NumPy, and Matplotlib. These libraries will help you handle data, perform analysis, and visualize your findings.
