Data Science Training in Mississauga, CA

Connecting to Data and introduction to data source concept
Working with data files versus database server
Understanding the Tableau workspace
Dimensions and Measures
Tour of Shelves (How shelves and marks work)
Help Menu and Samples
Saving and sharing your work Building Basic Views

Creating Views
Marks
Size and Transparency
Highlighting
Date aggregations and date parts
Discrete versus Continuous
Dual Axis / Multiple Measures
Combo Charts with different mark types
Geographic Map Page Trails
Heat Map
Density Chart
Scatter Plots
Aggregation and Disaggregation data
Pie and Bar Charts
Analyzing
Sorting & Grouping
Aliases
Filtering and Quick Filters
Cross-Tabs (Pivot Tables)
Totals and Subtotals Drilling and Drill Through
Small Multiples
Percent of Total
Working with Statistics and Trend lines.

Basic Arithmetic Calculations
Date Math
Working with Totals
String Functions
Custom Aggregations
Logic Statements

Formatting your Visualization
Effective Titles and Captions
Introduction to Visual Best Practices
Working with Labels and Annotations

Packaged Workbooks
Publish to Office
Reader
PDF
Tableau Server and Sharing over the Web

Scenario-based Review Exercises
Best Practices.

Data Types
Dimension vs Measures
Discrete vs Continuous
Application of Discrete and Continuous Fields
Rename Hide Unhide and Sort Columns
Default Properties of Fields
Create Aliases
Recap
Knowledge Check

INTRODUCTION TO PYTHON
CONTROL STRUCTURE
STRING
LIST
TUPLES & DICTIONARIES
LAMDA
MAP
REDUCE
FILTER
MODULES & FUNCTIONS
PACKAGES & LIBRARIES IN PYTHON
FILES & EXCEPTION HANDLING

INTRODUCTION TO PANDAS
JUPYTER NOTEBOOK
NUMPY/SCIPY BASICS
MATPLOTLIB BASICS
INTRODUCTION TO SCIKIT
DATA LOAD & PREPROCESSING

INTRODUCTION TO ML TOOL
ML EXAMPLES & ASSIGNMENTS

Why do we need Machine Learning?
Which Industries can use Machine Learning Sample Examples
What is Machine Learning A General Introduction
Supervised Machine Learning
Unsupervised Machine Learning

Model Evaluation
Need of Feature Engineering
Feature Engineering techniques

Regression
Linear & Non-Linear
Examples & Project assignment Linear and Non-linear regression model building
Classification
Examples & Project assignment
Clustering
Project assignment Customer Segmentation

Introduction
Terminologies of statistics
Measures of Centers & Spread
Types of distributions

Overview
Data Types and Variables
Operators
if Statements and lops
Functions in R

Introduction
ML Fundamentals
Learning Technologies
Decision Tree
Random forest classifiers
Deep Learning
Time Series Analysis

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

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

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

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

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
Example of an 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 (UnderfittingvsOverfitting) Trade-off
The Modeling Error Factors
One Way to Visualize Bias and Variance
UnderfittingvsOverfitting 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
Hands-on Exercise
Classification (Supervised ML) Examples
Classifying with k-Nearest Neighbors
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 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 and Terminology
Properties of Decision Trees
Decision Tree Classification in the Context of Information Theory
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
XGBoost
Gradient Boosting
Hands-On Exercise
A Better Algorithm or More Data?

What is Python?
Additional Documentation
Which version of Python am I running?
Python Dev Tools and REPLs
IPython
Jupyter
Jupyter Operation Modes
Jupyter Common Commands
Anaconda
Python Variables and Basic Syntax
Variable Scopes
PEP8
The Python Programs
Getting Help
Variable Types
Assigning Multiple Values to Multiple Variables
Null (None)
Strings
Finding Index of a Substring
String Splitting
Triple-Delimited String Literals
Raw String Literals
String Formatting and Interpolation
Boolean and Boolean Operators
Numbers
Looking Up the Runtime Type of a Variable
Divisions
Assignment-with-Operation
Comments:
Relational Operators
The if-elif-else Triad
An if-elif-else Example
Conditional Expressions (a.k.a. Ternary Operator)
The While-Break-Continue Triad
The for Loop
try-except-finally
Lists
Main List Methods
Dictionaries
Working with Dictionaries
Sets
Common Set Operations
Set Operations Examples
Finding Unique Elements in a List
Enumerate
Tuples
Unpacking Tuples
Functions
Dealing with Arbitrary Number of Parameters
Keyword Function Parameters
The range Object
Random Numbers
Python Modules
Importing Modules
Installing Modules
Listing Methods in a Module
Creating Your Own Modules
Creating a Runnable Application
List Comprehension
Zipping Lists
Working with Files
Reading and Writing Files
Reading Command-Line Parameters
Accessing Environment Variables
What is Functional Programming (FP)?
Terminology: Higher-Order Functions
Lambda Functions in Python
Example: Lambdas in the Sorted Function
Other Examples of Using Lambdas
Regular Expressions
Using Regular Expressions Examples
Python Data Science-Centric Libraries

About the Project
Dataset
Project