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This Applied Data Science with Python 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 intensive 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.


Topics:

  •     Python for Data Science
  •     Defining Data Science
  •     Data Processing Phases
  •     Descriptive Statistics Computing Features in Python
  •     Repairing and Normalizing Data
  •     Data Visualization in Python
  •     Data Science and ML Algorithms in scikit-learn

Applied Data Science with Python

Price €1,250.00

Course Code

GTBDP1

Duration

2 Day

Course Fee

POA

Accreditation

N/A

Target Audience

  • Business Analysts, Developers, IT Architects, and Technical Managers

Attendee Requirements

  • 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.)

Ways to Attend this Course

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Course Description

This Applied Data Science with Python 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 intensive 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.


Topics:

  •     Python for Data Science
  •     Defining Data Science
  •     Data Processing Phases
  •     Descriptive Statistics Computing Features in Python
  •     Repairing and Normalizing Data
  •     Data Visualization in Python
  •     Data Science and ML Algorithms in scikit-learn
Course Outline


Chapter 1. Python for Data Science 

• In-Class Discussion
• Python Data Science-Centric Libraries
• NumPy
• NumPy Arrays
• Select NumPy Operations
• SciPy
• pandas
• Creating a pandas DataFrame
• Fetching and Sorting Data
• Scikit-learn
• Matplotlib
• Seaborn
• Python Dev Tools and REPLs
• IPython
• Jupyter
• Jupyter Operation Modes
• Jupyter Common Commands
• Anaconda


  Chapter 2. Defining Data Science 

• What is Data Science?
• Data Science, Machine Learning, AI?
• The Data-Related Roles
• The Data Science Ecosystem
• Tools of the Trade
• Who is a Data Scientist?
• Data Scientists at Work
• Examples of Data Science Projects
• An Example of a Data Product
• Applied Data Science at Google
• Data Science Gotchas

 Chapter 3. Data Processing Phases 

• Typical Data Processing Pipeline
• Data Discovery Phase
• Data Harvesting Phase
• Data Priming Phase
• Exploratory Data Analysis
• Model Planning Phase
• Model Building Phase
• Communicating the Results
• Production Roll-out
• Data Logistics and Data Governance
• Data Processing Workflow Engines
• Apache Airflow
• Data Lineage and Provenance
• Apache NiFi

Chapter 4.  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

 

 Chapter 5.  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

 

 Chapter 6. Data Visualization in Python 

•    Data Visualization
•    Data Visualization in Python
•    Matplotlib
•    Getting Started with matplotlib
•    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
•    Saving Figures to a File
•    Seaborn
•    Getting Started with seaborn
•    Histograms and KDE
•    Plotting Bivariate Distributions
•    Scatter plots in seaborn
•    Pair plots in seaborn
•    Heatmaps
•    Ggplot

 

Chapter 7.  Data Science and ML Algorithms in scikit-learn

•    In-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
•    Choosing the Right Algorithm
•    The scikit-learn Package
•    scikit-learn Estimators, Models, and Predictors
•    Model Evaluation
•    The Error Rate
•    Confusion Matrix
•    The Binary Classification Confusion Matrix
•    Multi-class Classification Confusion Matrix Example
•    ROC Curve
•    The AUC Metric
•    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 Reduction
•    The LIBSVM format
•    Life-cycles of Machine Learning Development
•    Data Splitting into Training and Test Datasets
•    ML Model Tuning Visually
•    Data Splitting in scikit-learn
•    Cross-Validation Technique
•    Classification (Supervised ML) Examples
•    Classifying with k-Nearest Neighbors
•    k-Nearest Neighbors Algorithm
•    Regression Analysis
•    Regression vs Correlation
•    Regression vs Classification
•    Simple Linear Regression Model
•    Linear Regression Illustration
•    Least-Squares Method (LSM)
•    Gradient Descent Optimization
•    Multiple Regression Analysis
•    Evaluating Regression Model Accuracy
•    The R2 Model Score
•    The MSE Model Score
•    Logistic Regression (Logit)
•    Interpreting Logistic Regression Results
•    Decision Trees
•    Decision Tree Terminology
•    Properties of Decision Trees
•    Decision Tree Classification in the Context of Information Theory
•    The Simplified Decision Tree Algorithm
•    Using Decision Trees
•    Random Forests
•    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
•    k-Means Clustering (UL)
•    k-Means Clustering in a Nutshell
•    k-Means Characteristics
•    Global vs Local Minimum Explained
•    XGBoost
•    Gradient Boosting
•    A Better Algorithm or More Data?

Lab Exercises

    Lab 1 – Using Jupyter Notebook

    Lab 2 -  Understanding Python

    Lab 3 -  Understanding NumPy

    Lab 4 -  Understandng pandas

    Lab 5 – Repairing and Normalizing Data 

    Lab 6 - Data Visualization in Python            

    Lab 7 - Data Visualization in Python Project

    Lab 8 - Data Splitting     

    Lab 9-  k-Nearest Neighbors Algorithm

    Lab 10 -  The Random Forest Algorithm

    Lab 11 – Th k-Means Algorithm

    Lab 12 – Building Regression Models with XGBoost Library

Learning Path
Ways to Attend
  • Attend a public course, if there is one available. Please check our schedule, or register your interest in joining a course in your area.
  • Private onsite Team training also available, please contact us to discuss. We can customise this course to suit your business requirements.

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