Data Science Career Insights by WeCloudData
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This document provides information on data science career paths and training programs from WeCloudData. It includes an overview of WeCloudData's college diploma programs in data science, data engineering, AI, and analytics. It also describes WeCloudData's consulting, corporate training, and career services. The document outlines various learning paths for part-time programs in data science, big data, AWS, and AI. It provides details on WeCloudData's full-time data science immersive program, including the syllabus. Finally, it discusses factors for landing data science jobs and the data science job market in Canada.
Data Science Career Insights by WeCloudData
- 1. Data Science Career Landing Data Scientist Jobs WeCloudData [email protected] | [email protected] @WeCloudData @WeCloudData tordatascience weclouddata WeCloudData tordatascience
- 3. v Data Science Toronto Institute of Data Science and Technology Accredited private college diploma programs vCollege Data Engineering Artificial Intelligence Cloud Computing BI & Data Analytics WeCloudData - Corporate Preferred upskill/reskill training provider for Canadian companies Corporate Banking Retail Telecommunications Tech Insurance Business Schools Beam Data Data/AI consultancy focusing on applied data problems vConsulting Banking B2B Tech Medical E-Commerce Consumer Electronics WeCareer Premium career services: mentorship model vCareer Risk Capital Market Marketing Retail BI Digital Analytics
- 4. Prerequisites Data Science Learning Path Learn to build ML models using Sklearn ML Applied Master data wrangling with Python Data Science w/ Python Harness big data with Hadoop, Hive, Presto, and AWS Big Data Build your portfolio with hands-on Capstone projects ML Advanced Machine Learning at Scale with Spark ML and Real-time Deployment Spark Data Science Toronto Institute of Data Science and Technology Accredited private college diploma programs vCollege Data Engineering Artificial Intelligence Cloud Computing BI & Data Analytics Teaching highly in-demand skills
- 5. Data Science Part-Time Learning Path Prerequisites Data Science Learning Path • ML algorithms • 2 Projects • Interview Practice Applied ML • Data wrangling • Data Visualization • Predictive Modeling Data Science w/ Python • Big data tools • ML at scale • ML deployment • Job referrals Big Data Python Foundation SQL for Data Science
- 6. Scala & Spark for DE Linux Command Line Docker | Kubernetes Scala Programming Spark In Depth ETL for DE Hadoop | Hive | Presto Data Ingestion & Integration Talend Airflow & Pipelines Real-time Analytics Apache Kafka Spark Streaming Apache Flink Apache Beam SparkforDE BigData & ETL Realtim e Analytics Learn to build data pipelines, scale data processing with big data tools, and deployment real-time applications and machine learning models at scale. Data Engineering Learning Path Data Engineering Part-Time Part-time Program
- 7. AWS Big Data - Part-Time Learning Path Learn AWS big data tools and platforms and get certified as AWS Certified Big Data Specialist Cloud Computing AWS Track Learn AWS Big Data Tools Hands-on Project Certification Exam Prep 02/02/202010/12/2019 Learn AWS Solution Architect Hands-on Project Certification Exam Prep
- 8. Applied Deep Learning Applied AI – Part-Time Learning Path Artificial Intelligence Program Deep Learning for NLP Deep Learning Capstone Machine Learning in Healthcare https://www.youtube.com/watch?v=39rSzfpYsvA
- 9. P(Get Interview) = 0.4 +0.25 + 0.25 + 0.1S E R N P(Ace Skills) = 0.25 +0.3 + 0.4 + 0.05S C B P P(Offer) = P(Get Interview) x P(Ace Interview) Landing a Data Scientist Job Key Factors S E N R Skills Experience Resume C Network Communication B P Business Cases Preparation
- 10. Data Science Immersive (PCC Approved Diploma Program)
- 11. Prerequisites • ML algorithms • 2 Projects • Interview Practice Applied ML • Data wrangling • Data Visualization • Predictive Modeling Data Science w/ Python • Big data tools • ML at scale • ML deployment • Job referrals Big Data Python Foundation SQL for Data Science + Experience Industry Intern Consulting Project + Career Support Resume Referral (50%) P(Get Interview) = 0.4 +0.25 + 0.25 + 0.1S E R N S E N R Skills Experience Resume C Network Communication B P Business Cases Preparation P(Ace Skills) = 0.25 +0.3 + 0.4 + 0.05S C B P Training Data Science Immersive (PCC Approved Diploma Program)
- 12. Python • Py: Basics • Py: DataTypes • Py: Strings • Py: Functions • Py: Class • Py: IDEs (PyCharm) W2 W3W1 Learning to Code • SQL • Linux | Docker • Github • AWS Data Science w/ Python • Py: Functions • Py: Class/OOP • DS: Numpy • DS: Pandas • DS:Viz • DS:API • Scraping Project ML: Classifier • ML: KNN • ML: Logistic • ML: SVM • ML: Evaluation • ML: Cross-val W4 W5 ML: Classifier • ML:Trees • ML: Ensembles • ML:Tuning • ML: Imbalanced • ML: Pipeline Review Week • Review • SQL Quiz • ML Quiz • Interview Practice • ML Project #1 W6 12-week Diploma Program Data Science Diploma Program – Jan 2020 Syllabus Big Data • BD: Spark DF • BD: NoSQL • Interview Practice W11 W12W10 Big Data • Big Data Project • Spark Machine Learning • Model Deployment • Rest API • Model in Production Big Data • BD: Hadoop • BD: Hive • BD: SQL on Hadoop • BD: Spark ML: Regression • Py: Pandas Adv • ML: Stats • ML: Linear Algebra • ML: Optimization • ML: Regression W7 ML: Clustering/NLP • ML:Text Processing • ML:Topic Model • ML: Clustering • ML Dimension Reduction • Interview Practice • Client Project Kickoff W8 ML: Neural Net • ML: Neural Net • ML: Keras • ML: CNN • ML Project #2 • Interview Practic W9
- 13. Data Science Diploma Program – Jan 2020 Syllabus Python • Py: Basics • Py: DataTypes • Py: Strings • Py: Functions • Py: Class • Py: IDEs (PyCharm) W2 W3W1 Learning to Code • SQL • Linux | Docker • Github • AWS Data Science w/ Python • Py: Functions • Py: Class/OOP • DS: Numpy • DS: Pandas • DS:Viz • DS:API • Scraping Project ML: Classifier • ML: KNN • ML: Logistic • ML: SVM • ML: Evaluation • ML: Cross-val W4 W5 ML: Classifier • ML:Trees • ML: Ensembles • ML:Tuning • ML: Imbalanced • ML: Pipeline Review Week • Review • SQL Quiz • ML Quiz • Interview Practice • ML Project #1 W6 12-week Diploma Program Big Data • BD: Spark DF • BD: NoSQL • Interview Practice W11 W12W10 Big Data • Big Data Project • Spark Machine Learning • Model Deployment • Rest API • Model in Production Big Data • BD: Hadoop • BD: Hive • BD: SQL on Hadoop • BD: Spark ML: Regression • Py: Pandas Adv • ML: Stats • ML: Linear Algebra • ML: Optimization • ML: Regression W7 ML: Clustering/NLP • ML:Text Processing • ML:Topic Model • ML: Clustering • ML Dimension Reduction • Interview Practice • Client Project Kickoff W8 ML: Neural Net • ML: Neural Net • ML: Keras • ML: CNN • ML Project #2 • Interview Practic W9 Client Project Career/Referral Other Bootcamps
- 14. Learning Environment Lab Environment (Tools & Platforms) Python | SQL Cloud | Big DataMachine Learning
- 15. Hands-on Project Bring real industry-level project experience to the classroom By working on real projects, we mean • You will be helping startups set up data pipelines in AWS • You will be working on forecast models to optimize inventories for hundreds of millions of device sales • Your customer segmentation models will shape how a startup manage marketing campaigns • You will help the client save AWS cost by 200% by migrating computing to Apache Spark • Your machine learning models will help companies retain high value customers • Your work will be presented to the CEOs
- 16. Student Success Job Placement 6 months 2 months 56%89% 83k Salary 50% Referral by WCD
- 18. Data Science Job Market
- 19. Coding/Tools Math/ML Storytelling Data Scientist Linux Python/Scala/Java Cloud (AWS) Hadoop, Spark Statistics Linear Algebra Regression Classification Clustering NLP Presentation Use cases Project Mgmt Communications Data Science Essential Skills Business Domain Knowledge Data is a language—every company, if not every business unit, speaks its own dialect.
- 20. Data Scientist The Types Operational DS Focus: data wrangling, work with large/small messy data, builds predictive models Strength: data handling, tools, business knowledge ML Engineer Focus: ML model deployment, data pipelines Strength: coding, algorithms, machine learning, platforms and tools ML Researcher Focus: algorithm development, research, IP Strength: ML/DL algorithms, implmentation, research DS Product Mngr Focus: product strategy, business communications, project management Strength: product sense, business requirements, DS acumen
- 21. Data Jobs in Canada Job Categories and Cities
- 22. Data Jobs in Canada Industries – Data Scientist
- 23. Data Jobs in Canada Industries – Data Analyst
- 24. Data Jobs in Canada Industries – Data Engineer
- 25. Data Jobs in Canada Industries – ML Engineer
- 26. Data Jobs in Canada SQL is among most wanted skills
- 27. Data Jobs in Canada Skills – Data Analyst
- 28. Data Jobs in Canada Skills – Data Engineer
- 29. Data Jobs in Canada Skills – ML Engineer
- 31. Develop DS Skills Follow a Data Science Learning Path
- 32. Resources Python Coding Practice Coding & Interviews • LeetCode • HackerRank Book Statistics Online Courses Udemy • Complete Python Bootcamp Datacamp • Introduction to Python
- 33. Data Science Importance of foundations Data Science Machine Learning Big Data Data Engineering Deep Learning ML Engineering Focus on one programming language at a time • Get good at it Must have skills • Python • SQL
- 34. Data Science What’s next? Prerequisites Data Science Learning Path • ML algorithms • 2 Projects • Interview Practice Applied ML • Data wrangling • Data Visualization • Predictive Modeling Data Science w/ Python • Big data tools • ML at scale • ML deployment • Job referrals Big Data Python Foundation SQL for Data Science
- 35. Develop DS Skills Learn Portfolio Experience Search Mentorship Networking Interview OfferResume Referral
- 36. Develop Data Skills Follow a Learning Path Prerequisites Data Science Learning Path Learn to build ML models using Sklearn ML Applied Master data wrangling with Python Data Science w/ Python Harness big data with Hadoop, Hive, Presto, and AWS Big Data Build your portfolio with hands-on Capstone projects ML Advanced Machine Learning at Scale with Spark ML and Real-time Deployment Spark
- 37. Coding Python + SQL Data Science Machine Learning Big Data Data Engineering Deep Learning ML Engineering Focus on one programming language at a time • Get good at it Must have skills • Python • SQL
- 38. Learn Data Science Understand the big picture
- 39. Fraud Detection Project Reference Architecture data lake Learn Data Science Understand Data pipelines
- 40. CNN ID ** 0.87 ** 0.23 … … … Model EvaluationCustomer Churn Definition Deep Learning Models Learn Data Science Understand Business Use Cases
- 41. Develop a Project Portfolio Learn Portfolio Experience Search Mentorship Networking Interview OfferResume Referral
- 42. DS Portfolio Real-time sentiment analysis
- 44. DS Portfolio Suggestion A decent portfolio is the minimum requirement • It’s worth your time and effort to build a strong project portfolio that impresses A good mentor will • point you to the right direction • give you the feedback you needs, and • save you time!
- 45. Web Crawler Project - Aritzia
- 46. Content Motivation Data Interesting Findings Conclusion Challenges
- 47. Info & Motivation ´ Type : Public ´ Traded as : TSX: ATZ ´ Industry : Fashion ´ Founded : 1984 ´ Founder : Brian Hill ´ Headquarters : Vancouver, British Columbia, Canada ´ Products : Clothing
- 48. Website
- 49. Dataframe ´ Total data : 856,452 ´ Date range : 2019-06-08 21:53:50 ~ 2019-06-20 13:57:19 ´ File numbers : 30
- 50. crawler.py
- 51. Interesting Findings ´ Categories & Brand ´ Price Distribution ´ Top 20 Colors ´ Weekdays Vs Weekend - Avg Stock ´ On Sale event - Discount% ´ Price Change Vs Stock Correlation
- 53. Brand Distribution Vs. Brand Average Price
- 54. Top 20 Colors
- 55. Weekdays Vs Weekend - Avg Stock
- 56. SALE !
- 57. Discount % of Each Brand
- 58. Top 10 products – stock change
- 59. Conclusion ´ Business casual clothes prices are higher than others ´ More transactions/purchases happens in weekends ´ Sale event – good deal for famous brands ´ Promotion influences stock change
- 60. Challenges ´ Save data as Tree structure (.json) ´ Load data ´ Move root node properties to children node ´ Data analyzing using Pandas ´ Visualization - Plotly (multi-chart types)
- 61. Next Step ´ Detailed size distribution of brands / products ´ Influences of the strength of discount ´ Stock refill timing ´ Long term data analyzing (winter vs. summer)
- 63. Learn Portfolio Experience Search Mentorship Networking Interview OfferResume Referral Gain Real Experience
- 64. DS Portfolio Suggestion Nothing beats real experience! • When you have real project experience, hiring managers rarely ask about the academic projects • Real client experience gives hiring managers the confidence and proof It’s extremely hard to find real project to work on. • Find non-profit organizations • Build a portfolio first so people are more interested in working with you
- 65. 1 month later http://104.154.205.162/superset/dashboard/1/Client Project Example A – Marketing Dashboard
- 66. Client Project Example A – Data Flow
- 67. Client Project Accounting App Startup Item Description Industry Accounting app Company ~ Project Phase Phase 1 ~ Phase 2 Impact of your effort Data strategy impact | Product Features Student Focus Machine Learning, Production Deployment Data Size Millions of rows, 50GB+ Data Collection No Data Retrieval Pandas, Spark Data Tools Python, Spark ML Scikit-learn, Spark, Keras Pipeline/Automation Yes Model API Yes Dashboard Yes Resume Experience Client Company Accounting Machine Learning Platform Data ML OCR JSON data Pipeline API Visualization
- 68. Client Project Example B – Business Problem
- 69. Client Project Example B – Model Performance
- 70. Client Project Example B – Deployment
- 71. Client Project Gain Project Management Exp
- 72. Client Project Learn the big picture
- 73. Client Project Improve Communication Skills
- 74. Client Project Having an impact
- 75. Client Project Build up work experience
- 76. Client Project Get endorsed by your client
- 77. Client Project Get endorsed by your client
- 80. • New Grad • Awesome GPA • No work experience Academic Background Qualifications Academic Background Qualifications Irrelevant Work Experience Work Experience Academic Background Qualifications Analytics Work Experience • Career switcher • Little analytics background • Data professionals • Upskilling Resume Typical Candidates
- 81. Resume Sample Resume – Data Analyst
- 82. Candidate z Hadoop, Hive, Spark ML, Classification, Clustering Summary Experience Kaggle Competition 1 • Titanic Kaggle Competition 1 • Click-through Prediction Kaggle • Chemical Engineering • MOOCs Education Resume How the candidate sees herself/himself
- 83. Candidate x Big Data, ML Churn Modeling, Risk Analysis Summary Senior Data Analyst – 1+ years • -------------- • -------------- Data Analyst - 2+ years • -------------- • -------------- Experience • Master of Statistics Education Candidate y Machine Learning Matlab, R, Python Summary Machine Learning – 4 years • Publications #1 • Publications #2 Research Post-doc in Computer Science Education Candidate z Hadoop, Hive, Spark ML, Classification, Clustering Summary Freelance Data Projects • Web scraping for client A • Forecasting for client B Relevant Experience Portfolio Project #1 • -------------- Portfolio Project #2 • -------------- Projects • DS Bootcamp • MOOCs Education Kaggle Competitions • Titanic – Top 10% • CTR – Top 5% Experience Resume How the recruiters see the candidates
- 84. Candidate x SAS, Predictive Modeling Churn Modeling, Risk Analysis Summary Senior Data Analyst – 1+ years • -------------- • -------------- Data Analyst - 2+ years • -------------- • -------------- Experience • Master of Statistics Education Candidate y Machine Learning Matlab, R, Python Summary Machine Learning – 4 years • Publications #1 • Publications #2 Research Post-doc in Computer Science Education Candidate z Hadoop, Hive, Spark, SQL ML, Classification, Clustering Summary Freelance Data Projects • Web scraping for client A • Forecasting for client B Relevant Experience Portfolio Project #1 • -------------- Portfolio Project #2 • -------------- Projects • DS Bootcamp • MOOCs Education Kaggle Competitions • Titanic – Top 10% • CTR – Top 5% Experience • Has relevant experience • Need to learn new tools • May be expensive • Has a PhD • ML/Kaggle experience plus • Need to learn SQL • Need real experience • No relevant work experience • Knows all the tools • Strong and relevant portfolio • Need to test his potential Resume Hiring manager’s perspective
- 85. • Foundations (Skills) • Coding (Python, R, SAS) • Data Munging (SQL, Big Data, Pipelines) • Analytics (Machine Learning, Predictive Modeling,Visualization) • Knowledge Sharing (documentation, presentation, communications) • Ability to learn and adapt • Passion and motivation • Problem solving • Culture fit Junior Data Scientist Applicants Referrals Recruiters Direct Resume What does the hiring managers want?
- 86. Resume JDs
- 90. Resume Example No relevant experience | Awesome blogs
- 91. Resume Example Too many courses | Little proof of experience
- 93. Job Search The DS job market is crowded Skills Experience Job Applicants Applications Post #1 Post #2 Post #3 Post #4 Interviews Offer
- 94. Job Search Most candidates – Lack of skills Skills Experience Job Applicants Applications Post #1 Post #2 Post #3 Post #4 Interviews Offer
- 95. Job Search Most candidates – Lack of experience Skills Experience Job Applicants Applications Post #1 Post #2 Post #3 Post #4 Interviews Offer
- 96. Job Search Most candidates – Not diligent enough Skills Experience Job Applicants Applications Post #1 Post #2 Post #3 Post #4 Interviews Offer
- 97. Job Search Most candidates – Bad Interview Skills Skills Experience Job Applicants Applications Post #1 Post #2 Post #3 Post #4 Interviews Offer
- 98. Job Search Suggestion Don’t get discouraged • Job market is seasonal sometimes Know what’s wrong • If you get no interviews after applying for 100 jobs, your resume definitely needs to be fixed Don’t’ apply too many filters • Apply as many jobs as possible if you are a beginner with no relevant background • If your goal is DS job, apply for DA jobs as well Work with recruiters • Staffing companies play very important roles in Toronto job market • Some staffing companies deal with junior candidates Referrals • Referrals usually get your resumes in front of the hiring manager
- 99. Referral | Networking | Mentorship Learn Portfolio Experience Search Mentorship Referral Networking Interview OfferResume
- 100. Networking | Mentorship Suggestion Things usually don’t come free • Reaching out on LinkedIn and asking for referral directly is not useful strategy • Why should someone refer you? • How well do they know you? Don’t get discouraged • DS/Managers are usually busy people Be creative • What value do you bring to the mentorship relationship? • Anything you can help them with?
- 101. Interview Learn Portfolio Experience Search Mentorship Referral Networking Interview OfferResume
- 102. Based on the interviewers LinkedIn profiles, we can summarize their experience and prepare a list of questions that are relevant to their work/personal experience ● Agile Project Management ○ Did your previous experience include agile approach? ○ How does consulting projects get managed at beam data? ● Segmentation ○ What is the segmentation strategy used in the FG client project at beam data? ○ What approaches did you use to help your client understand your segmentation models? ● Predictive modeling ○ How does customer life cycle analytics work? ○ Do you have experience with predictive churn modeling? ○ What are some of the customer acquisition strategy for digital channels? ■ Can you tell me about the social media acquisition project you did? Job Search Interview Preparation
- 103. Based on the interviewers LinkedIn profiles, we can summarize their experience and prepare a list of questions that are relevant to their work/personal experience ● Cloud ○ How much do you know about GCP? ○ Why did you choose Amazon S3 and Redshift database for the client project? ○ What are some of the cloud tools you’ve worked with? ● Machine Learning ○ How do handle outliers? ○ How do you interpret models to business team? ○ How do you make sure that your model has a good balance between variance and bias? ○ Have you worked with unstructured data? ■ How does topic model work? ■ How can you use topic models to analyze survey response? ○ How does weight of evidence work? ○ What are some of the practical considerations when you build credit scoring models ○ Have you used sentiment analysis for call center logs? Job Search Interview Preparation
- 104. Based on the interviewers LinkedIn profiles, we can summarize their experience and prepare a list of questions that are relevant to their work/personal experience ● Project Experience ○ Tell me about the loan default models you built ○ Tell me about the fannie mae models you built ○ Tell me about the bank churn modeling project ■ Where is the data kept? In the cloud? ■ How did the client make sure that customer data privacy compliance is met? ■ Do you know anything about GDPR? ■ How was the churn target defined? ● Digital Transformation ○ Have you helped any client look into customer acquisition via digital channels? ○ How much do you know about digital channel customer acquisition funnel analysis? ○ How can you use Google Analytics to track site traffic? ○ How much do you know about Google Ads, Facebook Ads? Job Search Interview Preparation
- 105. Phone Screening Technical (Onsite|Remote) Take-home Assignment Team Interview Hiring Manager | Director Call Job Search Interview Process
- 108. • What is the difference between UNION and UNION ALL (SQL) • Describe the difference between ridge and lasso regularization for regression and give examples of where you might use each of them. • Explain the advantages and disadvantages of having more/fewer predictors in a model. • What are some of the options to deploy a machine learning model in production? • Use the whiteboard and describe how you’d architect a real-time sentiment analysis big data processing pipeline Interview Technical Interview
- 109. • Walk me through one of the ML projects you worked on • Here’s my business problem and the data I collect.Tell me how you would approach it • Describe the entire workflow of the machine learning project you did at Company X Open-ended Questions Interview Open-ended Questions
- 110. Be prepared for story telling!!! Interview Onsite Presentation
- 113. How can WeCloudData help?
- 114. Data Science Immersive Program Program Details • Faculty • 12-week program structure • Weekly schedule • Curriculum • Use cases • Learning environment • Projects • Job support
- 115. Data Science Bootcamp What is a bootcamp? Learn Portfolio Experience Search Mentorship Networking Interview OfferResume Referral
- 116. Bootcamp Instructor Bio Instructor – Python | SQL | Data Science • Machine Learning Engineer, Beam Data • Corporate trainer and Data Science instructor at WeCloudData • Career development mentor • Expertise • Python | Machine Learning | Data Science | Mobile App Dev Machine Learning Engineer Beam Data Vinny Nguyen is a machine learning engineer currently working at Beam Data, a Toronto- based AI consulting company. Coming from a web development background, Vinny transitioned into a Machine Learning engineer role by working closely with clients on machine learning projects, model deployment, and building data pipelines. Vinny also has a strong passion for teaching. He not only helps WeCloudData deliver ML workshops, corporate training, but also teaches the Data Science course where he teaches students to build data portfolios through hands-on projects.
- 117. 2005 2007 2008 2010 2011 2015 2012 2014 2016 2018 Bootcamp Faculty Instructor - Big Data & Cloud • Certified SAS Predictive Modeler since 2007 (among the first 20 in the world) • Helped build and lead the data science team at BlackBerry (2010 – 2015) • Helping Communitech incubator and Open Data Exchange mentor startups on data strategies • Co-founder and CEO of WeCloudData. Lead instructor for the corporate training program • Specialize in machine learning, big data, and cloud computing
- 118. Faculty Guest Lecturers – Industry Best Practices Guest speakers from various industry backgrounds will teach students best practices once a week!
- 119. Python • Py: Basics • Py: DataTypes • Py: Strings • Py: Functions • Py: Class • Py: IDEs (PyCharm) W2 W3W1 Learning to Code • SQL • Linux | Docker • Github • AWS Data Science w/ Python • Py: Functions • Py: Class/OOP • DS: Numpy • DS: Pandas • DS:Viz • DS:API • Scraping Project ML: Classifier • ML: KNN • ML: Logistic • ML: SVM • ML: Evaluation • ML: Cross-val W4 W5 ML: Classifier • ML:Trees • ML: Ensembles • ML:Tuning • ML: Imbalanced • ML: Pipeline Review Week • Review • SQL Quiz • ML Quiz • Interview Practice • ML Project #1 W6 12-week Bootcamp 12-week Program Structure Week View Big Data • BD: Spark DF • BD: NoSQL • Interview Practice W11 W12W10 Big Data • Big Data Project • Spark Machine Learning • Model Deployment • Rest API • Model in Production Big Data • BD: Hadoop • BD: Hive • BD: SQL on Hadoop • BD: Spark ML: Regression • Py: Pandas Adv • ML: Stats • ML: Linear Algebra • ML: Optimization • ML: Regression W7 ML: Clustering/NLP • ML:Text Processing • ML:Topic Model • ML: Clustering • ML Dimension Reduction • Interview Practice • Client Project Kickoff W8 ML: Neural Net • ML: Neural Net • ML: Keras • ML: CNN • ML Project #2 • Interview Practic W9
- 120. Schedule Weekly View Bootcamp classes run from Monday to Friday 9:30-5:30pm • Lectures, labs happen daily in the morning • Guest lectures are delivered every Wednesday in the evening • Every Friday afternoon students will take on weekly quizzes Lectures Labs Labs/Projects Guest Lecture Quiz
- 121. 9:30 | 12:30 13:30 | 17:30 18:30 | 20:30 Morning • Lecture • Labs • Use Cases Afternoon • Labs • Projects • Quizzes • Interview Practice Evening (only Wed) • Guest Lectures • Project Presentations Schedule Classes – Daily View TA hours
- 122. Lecture Content Lecture Content Week 1 Programming Basics • Linux Command Line (Bash) • Docker Introduction • AWS Introduction (EC2, S3, IAM) • SQL: Introduction to Databases • SQL: Structured Query Language (SQL) • SQL: Advanced Aggregation • SQL: Advanced Joins • SQL: Window Functions • Guest Lecture: Data Science at BMW & Amazon Week 3 Data Science with Python • DS: Introduction to Data Science • DS: Pandas DataFrame • DS: Web Scraping • DS: Data Visualizations with Plotly, Matplotlib, Seaborn, Folium, Dash • DS: Working with Rest APIs • Guest Lecture: Retail Analytics (Loblaws Digital) Week 2 Python Programming • Python: Programming Basics • Python: Data Types • Python: String • Python: Functions and Modules • Python: Class • Python: IDEs – Notebook vs PyCharm • Guest Lecture: Business Analytics (Capital One) Week 4 ML: Regression • ML: Introduction to Machine Learning • ML: Machine Learning Use Cases • ML: Introduction to Statistics • ML: Introduction to Linear Algebra • ML: Introduction to Optimization • ML: Regression Analysis • Guest Lecture: Visual Storytelling with PowerBI Syllabus (Month 1) Curriculum Syllabus – Month 1 Week 2 Week 3 Week 4Week 1 SQL | Linux Python Programming Data Science with Python ML: Regression
- 123. Curriculum Syllabus – Month 2 Week 6 Week 7 Week 8Week 5 ML: Classification ML: Classification Review Week ML: Clustering & NLP Lecture Content Lecture Content Week 5 ML: Classification • ML: K-nearest Neighbors • ML: Logistic Regression • ML: Support Vector Machine • ML: Model Evaluation • ML: Cross-validation & Parameter Tuning • Guest Lecture: Customer Lifecycle Management (Telecom) Week 7 Mid-term Review • Review: SQL/Python (Quiz) • Review: Machine Learning (Quiz) • Project Catchup • Guest Lecture: Marketing Analytics A/B Testing (Ritual.co) Week 6 ML: Classification • ML: Decision Tree • ML: Ensemble Trees (Random Forest, Gradient Boosting) • ML: Imbalanced Data • ML: Pipeline • ML: Model Interpretation with LIME/SHAP • Guest Lecture: Personalization Recommendation (Canadian Tire) Week 8 ML: Clustering & NLP • ML: Text Processing Basics • ML: Topic Modeling with LDA (Gensim) • ML: Word Embedding (TD-IDF, Word2Vec) • ML: Text Classification (Sentiment Analysis) • ML: Clustering (K-Means) • ML: Dimension Reduction • Guest Lecture: Sales Forecasting (Samsung Electronics Canada) Syllabus (Month 2)
- 124. Curriculum Syllabus – Month 3 Week 10 Week 11 Week 12Week 9 ML: Deep Learning Big Data: Hadoop Big Data: Spark Big Data: Deployment Lecture Content Lecture Content Week 9 ML: Deep Learning • ML: Introduction to Neural Networks • ML: Deep Learning with Keras | Tensorflow • ML: Convolutional Neural Networks • ML: Recurrent Neural Net with LSTM • Guest Lecture: Credit Risk Modeling with Advanced Boosting Week 11 Big Data: Apache Spark • BD: Apache Spark RDD • BD: Spark DataFrame/SQL • BD: Spark Internals and Tuning • BD: Spark Machine Learning • Guest Lecture: Survival Analysis with Deep Learning Week 10 Big Data: Hadoop • BD: Introduction to Big Data • BD: Introduction to Hadoop Ecosystem • BD: MapReduce & HDFS • BD: Apache Hive • BD: SQL on Hadoop (Imapala | Presto) • BD: Introduction to Apache Spark • Guest Lecture: Real-time Advertising Week 12 Big Data: Model Deployment • BD: NoSQL Database • BD: Spark Machine Learning • BD: Model Deployment with Flask • BD: Model Deployment with Amazon SageMaker • BD: Model in Production Considerations Syllabus (Month 3)
- 125. Applied Analytics Business Case Studies & Best Practices • Visual Storytelling & Presentations • Customer Lifecycle Management (Acquisition, Churn, Loyalty) • Retail Sales Forecasting • Supply Chain SKU Optimization • Retail Fraud Analytics It is our belief that data science bootcamp is not just about coding. Teaching students the skills required to turn business requirements into technical implementation plan and analytical results to drive outcome is essential! • Personalized Recommendation • Credit Risk Modeling (retail credit scoring) • Marketing Campaigns (A/B Testing) • Real-time Advertising Real-life data science use cases covered
- 126. Learning Environment Online Learning Portal (LMS) We have built a learning portal to support • Remote | Mobile access • Progress tracking • Discussion board • Quizzes and assignments
- 127. Learning Environment Online Learning Portal (LMS)
- 128. Learning Environment Lab Environment (Tools & Platforms) Python | SQL Cloud | Big DataMachine Learning
- 129. Hands-on Project Bring real industry-level project experience to the classroom To make the learning environment as real as possible, we partner with several companies such as Samsung Electronics Canada, IGM, Toromont, and many startups to help bring real-life data science problems to the classroom. The mandate of WeCloudData’s Bootcamp is to teach students hands-on skills so that they have the practical knowledge to apply these skills to generate value for the businesses they work (or going to work) for.
- 130. Client Project Example Item Description Industry Fundraising app Company Blockchain Startup Project Phase POC/Pre-release Impact of your effort Data strategy level impact Student Focus Scraping, ML, Visualization Data Size TBD Data Collection Data API, Scraping, Mechanical Turk Data Retrieval NoSQL, SQL Data Tools Spark ML Scikit-learn, Gensim Pipeline/Automation Yes Model API No Dashboard Yes Blockchain Startup Platform Data Process Pipeline Visualization ML Help a blockchain-based fundraising app build data strategy around user engagement via event scraping, ML-based topic classification, and event dashboard
- 131. Data Science Diploma Program – Jan 2020 Syllabus Python • Py: Basics • Py: DataTypes • Py: Strings • Py: Functions • Py: Class • Py: IDEs (PyCharm) W2 W3W1 Learning to Code • SQL • Linux | Docker • Github • AWS Data Science w/ Python • Py: Functions • Py: Class/OOP • DS: Numpy • DS: Pandas • DS:Viz • DS:API • Scraping Project ML: Classifier • ML: KNN • ML: Logistic • ML: SVM • ML: Evaluation • ML: Cross-val W4 W5 ML: Classifier • ML:Trees • ML: Ensembles • ML:Tuning • ML: Imbalanced • ML: Pipeline Review Week • Review • SQL Quiz • ML Quiz • Interview Practice • ML Project #1 W6 12-week Diploma Program Big Data • BD: Spark DF • BD: NoSQL • Interview Practice W11 W12W10 Big Data • Big Data Project • Spark Machine Learning • Model Deployment • Rest API • Model in Production Big Data • BD: Hadoop • BD: Hive • BD: SQL on Hadoop • BD: Spark ML: Regression • Py: Pandas Adv • ML: Stats • ML: Linear Algebra • ML: Optimization • ML: Regression W7 ML: Clustering/NLP • ML:Text Processing • ML:Topic Model • ML: Clustering • ML Dimension Reduction • Interview Practice • Client Project Kickoff W8 ML: Neural Net • ML: Neural Net • ML: Keras • ML: CNN • ML Project #2 • Interview Practic W9 Client Project Career/Referral Other Bootcamps
- 132. Data Science Immersive (PCC Approved Diploma Program) Jan 20, 2020 Ask about out Financing and Second Career options