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Applied Data Science with Python Training


This intensive training course provides theoretical and practical aspects of using Python in the realm of Data Science, Business Analytics, and Data Logistics.  The coverage of the related core concepts, terminology, and theory is provided as well.  This training course is supplemented by a variety of hands-on labs (the list of which is provided at the bottom of this outline) that help attendees reinforce their theoretical knowledge of the learned material.


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



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
  • NumPy
  • NumPy Arrays
  • Select NumPy Operations
  • SciPy
  • pandas
  • Examples of Using pandas' DataFrame
  • Scikit-learn
  • Matplotlib
  • Python Dev Tools and REPLs
  • IPython
  • Jupyter
  • 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 MinMaxScaler 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 DataFrame
  • Summary

Chapter 6. Data Grouping and Aggregation in Python

  • Data Aggregation and Grouping
  • Sample Data Set
  • The pandas.core.groupby.SeriesGroupBy 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 () Function
  • Subplots
  • Using 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-learn

  • In-Class Discussion
  • Types of Machine Learning
  • Terminology: Features and Observations
  • Terminology: Labels
  • Terminology: Continuous and Categorical Features
  • Continuous Features
  • Categorical Features
  • Common Distance Metrics
  • The Euclidean Metric
  • What is a Model
  • Supervised vs Unsupervised Machine Learning
  • Supervised Machine Learning Algorithms
  • Unsupervised Machine Learning Algorithms
  • Choose the Right Algorithm
  • The scikit-learn Package
  • scikit-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
  • 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 Reduction
  • The LIBSVM format
  • Life-cycles of Machine Learning Development
  • Data Split for Training and Test Data Sets
  • Data Splitting in scikit-learn
  • Hands-on Exercise
  • Classification (Supervised ML) Examples
  • Classifying with k-Nearest Neighbors
  • k-Nearest Neighbors Algorithm
  • k-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 Results
  • Decision Trees
  • Decision Tree Terminology
  • Decision Tree Classification in Context of Information Theory
  • Information Entropy Defined
  • The Shannon Entropy Formula
  • The Simplified Decision Tree Algorithm
  • Using Decision Trees
  • Random Forests
  • Hands-On Exercise
  • Support Vector Machines (SVMs)
  • Naive Bayes Classifier (SL)
  • Naive Bayesian Probabilistic Model in a Nutshell
  • Bayes Formula
  • Classification of Documents with Naive Bayes
  • Unsupervised Learning Type: Clustering
  • Clustering Examples
  • k-Means Clustering (UL)
  • k-Means Clustering in a Nutshell
  • k-Means Characteristics
  • Global vs Local Minimum Explained
  • Hands-On Exercise
  • Time-Series Analysis
  • Decomposing Time-Series
  • A Better Algorithm or More Data?
  • Summary

Lab Exercises

Lab 1. Using Jupyter Notebook
Lab 2. Python with NumPy and pandas
Lab 3. Repairing and Normalizing Data
Lab 4. Data Grouping and Aggregation 
Lab 5. Data Visualization with matplotlib 
Lab 6. Data Splitting
Lab 7. The k-Nearest Neighbors Algorithm
Lab 8. The Random Forest Algorithm 
Lab 9. The k-Means Algorithm




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.)




This course is aimed towards Business Analysts, Developers, IT Architects, and Technical Managers.



2 Days Course

Class Dates

Good to Run
Remote Live

This class runs from 10:00 AM to 06:00 PM EST

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