Information Technology
Python With Data Science
Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases.
2 Days
16 Hrs
9 Lessons
Course 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
Agenda
- • 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
- • 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
- • 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
- • 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
- • 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
- • 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
- • 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
- • 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
- 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
FREE
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Course Type: Instructor Led