• Anyone interested in learning the basics of Data Science

• Python Programming knowledge

Course Outline:

Day 1:

• Course Introduction
• Installing Anaconda
• Overview of Data Science
• The Difference Between Business Analytics, Data Analytics and Data Science
• The Data Science Process
  • Define the Problem
  • Get the Data
  • Explore the Data
  • Clean the Data
  • Model the Data
  • Communicate the Findings
• Descriptive Statistics Fundamentals
• Central Tendency
  • Mean
  • Median
  • Mode
• Spread of the Data
  • Variance
  • Standard Deviation
  • Range
• Relative Standing
  • Percentile
  • Quartile
  • Inter-quartile Range
• Data Libraries
  • Numpy
  • Pandas
• Accessing Data
  • CSV, TSV, JSON
  • Importing data from a MySQL database into Pandas
• Data Exploration
  • Describe
  • Grouping
  • Feature Selection
  • Feature Engineering
• Milestone 1: Use Pandas to perform exploratory data analysis.

Day 2:

• Data Preparation
• Data Cleaning
  • Dropping Rows
  • Imputing Missing Values
  • Feature Selection
• Data Transformation
  • One-Hot Encoding
  • Standardization
  • Normalization
• Feature Engineering
• Inferential Statistics Fundamentals
  • Normal Distribution
  • Central Limit Theorem
  • Standard Error
  • Confidence Intervals
  • Samples
  • Hypothesis Testing
• Data Visualization
  • Pandas
  • Matplotlib
  • Seaborn
• Machine Learning Overview
• Machine Learning Algorithms
• Linear Regression
• Logistic Regression
• Support Vector Machine
• Decision Tree
• Random Forest
• K-Nearest Neighbors
• K-Means Clustering
• Dimensionality and Sparsity
• Principal Component Analysis
• Singular Value Decomposition
• Factor Analysis
• Milestone 2: Use Pandas to clean, impute and engineer features

Day 3:

• Model Development
• Scikit-learn
• Test/Train Split
• Boosting and Ensembles
• Stacking
• Model Evaluation
• K-Fold Cross Validation
• Grid Search
• Feature Importance
• Confusion Matrix
• Accuracy vs. Precision vs. Recall
• Learning Curve
• Overfitting vs. Underfitting
• Recommendation Systems
• Natural Language Processing
• Processing Text
• Encoding Text
• Building a NLP pipeline
• Model Maintenance
• Roles and Responsibilities
• Documentation
• Close-out Activities
• Milestone 3: Build, evaluate, and refine a machine learning model
• Conclusion: Data Science in the real world, next steps.

Structured Activity/Exercises/Case Studies:

• Milestone Project 1: Use Pandas to perform exploratory data analysis.
• Milestone Project 2: Use Pandas to clean, impute and engineer features
• Milestone Project 3: Build, evaluate, and refine a machine learning model

Training material provided: Yes (Digital format)