Data science and Machine learning with Python

Preparatory (40)

• Working with data types
• Slicers
• Control structures
• Understanding Python’s Data Structures
• Implementing Functions
• Error Handling
• Introduction to OOP

Python Programming

• Basic statistics for ML

Statistics

Data Analysis and Visualization with Python (30 Hours)

• NumPy Basics
• Generating Data Using NumPy
• Subsetting Numpy Arrays
• Performing Calculations on a Numpy Array
• Filtering Data
• Sort a NumPy Array
• Applying Functions
• Aggregate Functions
• Set Operations
• Reshaping and Flattening Multidimensional arrays
• NumPy's Structured Arrays
• Introduction to SciPy

Data Analysis using

NumPy

• Basic Select Operations
• Filtering & Sorting
• Applying Scalar Functions
• Aggregative Functions
• SET Operations
• JOIN Opearions
• Schema Modifications
• Pandas Indexes
• Pivot operations
• Input / Output Operations
• Working with Analytic Functions

Data Analysis using

Pandas

• Types of matplotlib charts
• Basic customizations
• Advanced customizations
• 3d graphing

Data Visualization using

Matplotlib

Machine Learning (80 Hours)

Introduction

• Data Types
• Correlations
• Feature Distribution
• Outliers Detection

Data Understanding

• Data Scaling and Normalization
• Feature Selection
• Feature Extraction

Data Preparation

• Generalization
• Bias
• Variance
• Classification
• Regression

Supervised Learning

• train-test-validation
• cross validation
• over\under-fitting Confusion Matrix
• Accuracy
• ROC Curve
• Lift Charts

Model evaluation

• K-NN
• Decision Trees
• Linear Regression
• Logistic Regression
• SVM
• Naïve Bayes

Basic Supervised Learning Algorithms

• Ensemble Methods: Random Forest, Bagging, etc.
• Deep Learning
• NLP

Advanced Methods

• K-Means and GMM
• Association Rules
• Hierarchical Clustering
• Evaluating Unsupervised methods

Unsupervised Learning

• Machine Learning Flow in Production
• Common Technology Stack

Deployment

• ML final project

ML final project

SQL for Data Scientists  (15 Hours)

This module, unique of its kind in Israel, focuses on complex queries, and entirely composed of advanced “real-world” problems and solutions.

This training is based on a decade of experience working with various databases, challenging job interviews questions, and many complex SQL problems. In this course, students learn many different practical applications and “real-world” problems.

Through understanding of the problems this module presents, you will learn many techniques, such as:

• Complex scalar functions
• Advanced techniques for filtering and sorting values
• Working with analytical functions
• Advanced use of Group Functions
• Working with complex Sub-Queries
• Use of CTE and recursive CTE
• Advanced tables integration using JOIN, UNION and CROSS APPLY
• XML techniques for solving complex problems
• Working with PIVOT and UNPIVOT

Introduction to Big Data and Spark (15 Hours)

Introduction to Big Data

• Basics
• RDD Operations
• Spark SQL and DataFrames
• Spark ML

Introduction to Apache Spark (PySpark)