Six-Month Deep Dive Into Data Science and Machine Learning

info@adalegit.com • 647-271-1460 (Canada) • 516-217-2612 (USA)

Data Science and Machine Learning Course Details: 
Two Part Course – January 27th 2019 – July 19th 2020
(9 Hours a Week: Sunday 7:30-10:30pm, Monday 7:30pm-10:30pm, and Wednesday 7:30-10:30pm)
Module 01: Data Analytics & Business Intelligence 
1. Excel
2. SQL & Databases 
3. Business Intelligence Tools 
Module 02: Data Science & Machine Learning
1. Python for Data Science
2. Probability & Statistics 
3. Machine Learning 
Here is a Detailed Overview

What will I learn?

The program is currently offered in an accelerated format, which runs for 6 months, on Mondays, Wednesdays, and Sundays from 7:30pm to 10:30pm.


With over 140 hours of in-class learning, you will learn the foundations of data science in a hands-on, fast-paced environment.  


The program starts on January 20th and ends on July 19th 2020.

How much will it cost?

$3000 + HST per Module / Both Modules $6000 + HST (Compared to other programs in Data Science and Machine Learning, this program is at half the cost.)

Detailed Program Curriculum

> Excel
Excel is one of the most widely used data analysis tool used by businesses, both large and small, to analyze data. This course aims to provide the practical data analysis skills needed to answer business-driven questions – from analyzing financial data, calculating summary statistics, and automating repetitive, procedural data transformation tasks. The course will teach common data analysis operations (aggregations, lookups, conditional statements) that will serve as the foundational prerequisite skills that students need to master before moving on to more advanced topics such as SQL and Python.
> SQL & Databases

Most of the world’s data – from medical records to retail transaction histories – resides in organized structures of tables called databases. An effective data professional must be able to retrieve, transform, aggregate, and analyze data from databases using a standard language called Structured Query Language (SQL). This course teaches the standard ANSI syntax in SQL shared
across many types of commercial databases, such as SQL Server, DB2, MySQL, PostgreSQL, Oracle, among many others. The main objective of this course is to leverage the SQL language to turn raw
data stored in databases into actionable, data-driven insights

> Business Intelligence Tools
The advent of the data revolution this has created a huge market to present data in a compelling, interactive format to a non-technical audience. Software companies such as Microsoft, SAS, Salesforce, among many others, have capitalized on this market need to offer tools that enable data scientists to create dashboards that communicate insights by visualizing data. This course will teach two of the most popular business intelligence & data visualization tools in the market today: Tableau and Microsoft Power BI. At the end of the course, students are expected to create dashboards on a selected data set and present their findings and insights to a panel audience.
> Python for Data Science
Python is a general-purpose programming language that is becoming ever more popular for data science. In the 2018 World Developer Survey, Python is now ranked 3rd overall globally as the most widely used programming language. Unlike other Python courses, this module will focus specifically on programming within the context of data science. The course will focus on enabling and teaching data analysis and manipulation skills such as writing mathematical functions, manipulating data frames, and visualizing data using the vast ecosystem of data science libraries in Python, including: Numpy, Scipy, Matplotlib, Pandas, and Seaborn. Real-world data sets containing numeric, text, and time series data will be utilized in the course to contextualize the learning in a relevant and practical manner.
> Probability & Statistics
This course emphasizes the application of probability and statistics within the realm of business to support managerial decisions in the various functional areas of business. The course will focus on teaching fundamental skills including describing, summarizing, and analyzing statistical data, probability distributions, variance applications, sampling distributions, and hypothesis testing. As a continuation of the Python module, this course will leverage Python-based statistical tools and libraries to aid students in applying their newly-acquired programming skills to solve statistically oriented business problems. The course will culminate in a practical business case, where students will perform a fictitious A/B test, an experimental framework ubiquitous within the realm of marketing and advertising.
> Machine Learning
Machine Learning is the study and application of algorithms that learn from data to make predictions. From predicting stock prices to estimating the financial risk of mortgages, the practical applications of machine learning in the real world are endless. how to build and evaluate predictive models, how to fine-tune them for optimal performance, and how to preprocess data for more accurate results. The overall goal of the course is to provide a foundational framework for understanding how to use machine learning models in a real-world context. The course will culminate in a project that will provide an opportunity to apply the various ML models in the course to a real-world business dataset.

Data Science and Machine Learning Course Instructor,
 Mendelssohn Chan

Mendelsohn Chan is a Data Analytics & Cloud Computing Consultant at Massive Insights. He has a broad range of experience implementing data projects across many industries, including financial services, consumer-packaged goods (CPG), retail, healthcare, mining, and government. Mendelsohn has over 5 industry certifications in the data and cloud computing space, among them being an AWS-certified Cloud Architect, SysOps Administrator, and Developer. He is also certified in both Tableau and Power BI, two of the most popular business intelligence and data visualization tools in the market today. Mendelsohn’s main passion lies in tackling complex problems that lie at the intersection of Data Science, Machine Learning, and Business Strategy. His core competencies include: R/Python programming, Relational Databases, Business Intelligence (Tableau, Power BI), and Cloud Computing (AWS). 
Mendelsohn also teaches Data Science at the University of Toronto. In his spare time, Mendelsohn is an amateur pianist who enjoys playing classical music.
Please Come to our Free Series of Workshops and Talks on AI, Machine Learning, and Data Science to Learn More


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What our past students have to say

Moshe Gottfryd, MaxiMind
“I suggest this course to anyone who wants to jump right into the IT world. The classes are well-organized, the instructor is still helpful, and the labs gave us a hands-on experience and a sense of independence.”
Eli Wasserman, TNS Group
“This course will help you to leap into the deep end of a whole new industry.”
Mendel Bisk, Wolf Adar Inc
“Thanks to Adaleg IT I’m gainfully employed. Adaleg IT is not just a school that…once you are finished they are done with you, they try to help you out in the industry and lead the way.”
Daniel Goldstein, Compugen Inc
“Adaleg IT’s CCNA and CCENT courses helped me begin a new career in IT. I am very grateful. Thanks Adaleg IT!”
Ari Berman, Compugen Inc
“Adaleg IT”s course gave me an opportunity to literally start a new career! It was a game changer!”




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