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Applied Data Science with Python WA2715 - Course Book product photo Front View EL

Delivery Information

You will receive required software set up for install 48 hours from time of purchase. 

Version 1.1

Product Type: Courseware
Level: Professional
Duration: 2 Days

Participants should have a working knowledge of Python (or have the programming background and/or the ability to quickly pick up Python’s syntax), and be familiar with core statistical concepts (variance, correlation, etc.)

Language: English (en-US)
Delivery Format: eBook

Delivery Information

Delivered as a voucher. You can access the vouchers and assign them from Active Vouchers on myLeapest or you can use Classes function to assign the vouchers to a group of learners.

Product Content

This product contains the following items. Upon purchasing, you will get access to all available version prior to the latest version.

Course Description :

Delivery Information

You will receive required software set up for install 48 hours from time of purchase. 

Version 1.1

Course Outline :

Chapter 1

  • Python for Data Science
  • In-Class Discussion
  • Importing Modules
  • Listing Methods in a Module
  • Creating Your Own Modules
  • Random Numbers
  • Zipping Lists
  • List Comprehension
  • Python Data Science-Centric Libraries
  • NumPyNumPy Arrays
  • Select NumPy Operations
  • SciPy
  • Pandas
  • Creating a pandas Data Frame
  • Fetching and Sorting Data
  • Scikit-learnMatplotlib
  • Python Dev Tools and REPLsI
  • PythonJupyter
  • Jupyter Operation Modes
  • Jupyter Common Commands
  • Anaconda
  • Summary

Chapter 2

  • Applied Data Science
  • What is Data Science?
  • Data Science, Machine Learning, AI?
  • Data Science Ecosystem
  • Business Analytics vs. Data Science
  • Who is a Data Scientist?
  • Data Science Skill Sets Venn Diagram
  • Data Scientists at Work
  • Examples of Data Science Projects
  • An Example of a Data Product
  • Applied Data Science at Google
  • Data Science Gotchas
  • Summary

Chapter 3

  • Data Analytics Life-cycle Phases
  • Data Analytics Pipeline
  • Data Discovery Phase
  • Data Harvesting Phase
  • Data Priming Phase
  • Data Logistics and Data Governance
  • Exploratory Data Analysis
  • Model Planning Phase
  • Model Building Phase
  • Communicating the Results
  • Production Roll-out
  • Summary

Chapter 4

  • Repairing and Normalizing Data
  • Repairing and Normalizing Data
  • Dealing with the Missing Data
  • Sample Data Set
  • Getting Info on Null Data
  • Dropping a Column
  • Interpolating Missing Data in pandas
  • Replacing the Missing Values with the Mean Value
  • Scaling (Normalizing) the Data
  • Data Preprocessing with scikit-learn
  • Scaling with the scale() Function
  • The MinMax
  • Scaler Object
  • Summary

Chapter 5

  • Descriptive Statistics Computing Features in Python
  • Descriptive Statistics
  • Non-uniformity of a Probability Distribution
  • Using NumPy for Calculating Descriptive Statistics Measures
  • Finding Min and Max in NumPy
  • Using pandas for Calculating Descriptive Statistics Measures
  • Correlation
  • Regression and Correlation
  • Covariance
  • Getting Pairwise Correlation and Covariance Measures
  • Finding Min and Max in pandas
  • Data Frame
  • Summary

Chapter 6

  • Data Grouping and Aggregation in Python
  • Data Aggregation and Grouping
  • Sample Data Set
  • The pandas.core.groupby.Series
  • GroupBy Object
  • Grouping by Two or More Columns
  • Emulating SQL's WHERE Clause
  • The Pivot Tables
  • Cross-Tabulation
  • Summary

Chapter 7

  • Data Visualization with matplotlib
  • Data Visualization
  • What is matplotlib?
  • Getting Started with matplotlib
  • The Plotting Window
  • The Figure Options
  • The matplotlib.pyplot.plot() Function
  • The matplotlib.pyplot.bar() Function
  • The matplotlib.pyplot.pie () FunctionSubplotsUsing the matplotlib.gridspec.GridSpec Object
  • The matplotlib.pyplot.subplot() Function
  • Figures
  • Example of Using the figure() Function
  • Saving Figures to a File
  • Visualization with pandas
  • Working with matplotlib in Jupyter Notebooks
  • Summary

Chapter 8

  • Data Science and ML Algorithms in scikit-learnIn -Class Discussion
  • Types of Machine Learning
  • Terminology: Features and Observations
  • Representing Observations
  • Terminology: Labels
  • Terminology: Continuous and Categorical Features
  • Continuous Features
  • Categorical Features
  • Common Distance Metrics
  • The Euclidean Distance
  • What is a Model
  • Supervised vs Unsupervised Machine Learning
  • Supervised Machine Learning Algorithms
  • Unsupervised Machine Learning Algorithms
  • Choose the Right Algorithm
  • The scikit-learn Packagescikit-learn Estimators, Models, and Predictors
  • Model Evaluation
  • The Error Rate
  • Feature Engineering
  • Scaling of the Features
  • Feature Blending (Creating Synthetic Features)The 'One-Hot' Encoding Scheme
  • Example of 'One-Hot' Encoding Scheme
  • Bias-Variance (Underfitting vs Overfitting) Trade-off
  • The Modeling Error Factors
  • One Way to Visualize Bias and Variance
  • Underfitting vs Overfitting Visualization
  • Balancing Off the Bias-Variance Ratio
  • Regularization in scikit-learn
  • Regularization, Take Two
  • Dimensionality Reduction
  • PCA and isomap
  • The Advantages of Dimensionality
  • ReductionThe LIBSVM format
  • Life-cycles of Machine Learning Development
  • Data Split for Training and Test Data Set
  • Data Splitting in scikit-learn
  • Hands-on Exercise
  • Classification (Supervised ML) Examples
  • Classifying with k-Nearest Neighborsk-Nearest Neighbors Algorithmk-Nearest Neighbors Algorithm
  • Hands-on Exercise
  • Regression Analysis
  • Regression vs Correlation
  • Regression vs Classification
  • Simple Linear Regression Model
  • Linear Regression Illustration
  • Least-Squares Method (LSM)Gradient Descend Optimization
  • Locally Weighted Linear Regression
  • Regression Models in Excel
  • Multiple Regression Analysis
  • Linear Logistic (Logit) Regression
  • Interpreting Linear Logistic Regression Resullts

Target Audience :

Business Analysts, Developers, IT Architects, and Technical Managers

Course Agenda :

  • Applied Data Science and Business Analytics
  • Common Data Science algorithms for supervised and unsupervised machine learning
  • NumPy, pandas, Matplotlib, scikit-learn
  • Python REPLsJupyter notebooks
  • Data analytics life-cycle phases
  • Data repairing and normalizing
  • Data aggregation and grouping
  • Data visualization

Applied Data Science with Python WA2715 - Course Book

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