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Python With Data Science

Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases.

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Learning Objectives

NumPy, pandas, Matplotlib, scikit-learn

  • Python REPLs
  • Jupyter Notebooks
  • Data analytics life-cycle phases
  • Data repairing and normalizing
  • Data aggregation and grouping
  • Data visualization
  • Data science algorithms for supervised and unsupervised machine learning

     

    Course Details

    Course Outline

    1 - Python for Data Science
  • Using Modules
  • Listing Methods in a Module
  • Creating Your Own Modules
  • List Comprehension
  • Dictionary Comprehension
  • String Comprehension
  • Python 2 vs Python 3
  • Sets (Python 3+)
  • Python Idioms
  • Python Data Science “Ecosystem”
  • NumPy
  • NumPy Arrays
  • NumPy Idioms
  • pandas
  • Data Wrangling with pandas' DataFrame
  • SciPy
  • Scikit-learn
  • SciPy or scikit-learn?
  • Matplotlib
  • Python vs R
  • Python on Apache Spark
  • Python Dev Tools and REPLs
  • Anaconda
  • IPython
  • Visual Studio Code
  • Jupyter
  • Jupyter Basic Commands
  • Summary
    • 2 - Applied Data Science
  • What is Data Science?
  • Data Science Ecosystem
  • Data Mining vs. Data Science
  • Business Analytics vs. Data Science
  • Data Science, Machine Learning, AI?
  • 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
    • 3 - Data Analytics Life-cycle Phases
  • Big 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
    • 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
    • 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
    • 6 - Data Aggregation and Grouping
  • Data Aggregation and Grouping
  • Sample Data Set
  • The pandas.core.groupby.SeriesGroupBy Object
  • Grouping by Two or More Columns
  • Emulating the SQL's WHERE Clause
  • The Pivot Tables
  • Cross-Tabulation
  • Summary
    • 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
  • Hands-on Exercise
  • Figures
  • Saving Figures to File
  • Visualization with pandas
  • Working with matplotlib in Jupyter Notebooks
  • Summary
    • 8 - Data Science and ML Algorithms in scikit-learn
  • Data Science, Machine Learning, AI?
  • Types of Machine Learning
  • Terminology: Features and Observations
  • Continuous and Categorical Features (Variables)
  • Terminology: Axis
  • The scikit-learn Package
  • scikit-learn Estimators
  • Models, Estimators, and Predictors
  • Common Distance Metrics
  • The Euclidean Metric
  • The LIBSVM format
  • Scaling of the Features
  • The Curse of Dimensionality
  • Supervised vs Unsupervised Machine Learning
  • Supervised Machine Learning Algorithms
  • Unsupervised Machine Learning Algorithms
  • Choose the Right Algorithm
  • Life-cycles of Machine Learning Development
  • Data Split for Training and Test Data Sets
  • Data Splitting in scikit-learn
  • Hands-on Exercise
  • Classification Examples
  • Classifying with k-Nearest Neighbors (SL)
  • k-Nearest Neighbors Algorithm
  • k-Nearest Neighbors Algorithm
  • The Error Rate
  • Hands-on Exercise
  • Dimensionality Reduction
  • The Advantages of Dimensionality Reduction
  • Principal component analysis (PCA)
  • Hands-on Exercise
  • Data Blending
  • Decision Trees (SL)
  • 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
  • SVM
  • 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
  • Regression Analysis
  • Simple Linear Regression Model
  • Linear vs Non-Linear Regression
  • Linear Regression Illustration
  • Major Underlying Assumptions for Regression Analysis
  • Least-Squares Method (LSM)
  • Locally Weighted Linear Regression
  • Regression Models in Excel
  • Multiple Regression Analysis
  • Logistic Regression
  • Regression vs Classification
  • Time-Series Analysis
  • Decomposing Time-Series
  • Summary
    • 9 - Lab Exercises
  • Lab 1 - Learning the Lab Environment
  • Lab 2 - Using Jupyter Notebook
  • Lab 3 - Repairing and Normalizing Data
  • Lab 4 - Computing Descriptive Statistics
  • Lab 5 - Data Grouping and Aggregation
  • Lab 6 - Data Visualization with matplotlib
  • Lab 7 - Data Splitting
  • Lab 8 - k-Nearest Neighbors Algorithm
  • Lab 9 - The k-means Algorithm
  • Lab 10 - The Random Forest Algorithm

    • Actual course outline may vary depending on offering center. Contact your sales representative for more information.

    Who is it For?

    Target Audience

    Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming

    Also familiar with Python

    Python With Data Science

    Call
    Course Length : 2 Days

    There are currently no scheduled dates for this course. Please contact us for more information.

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