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Data Science with Python - WeCloudData

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WeCloudData
WeCloudData

This document discusses trends in data science and the use of Python. It provides an overview of WeCloudData's education and training programs in data science, machine learning, big data, cloud computing, and artificial intelligence. It describes various part-time and full-time learning paths covering topics such as Python, SQL, machine learning algorithms, deep learning, data engineering, big data tools and platforms, and cloud computing with AWS. It also includes information on career services and past student outcomes like job placements and salaries.

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Python for Data Science Trends and Use Cases WeCloudData @WeCloudData @WeCloudData tordatascience weclouddata WeCloudData tordatascience
WeCloudData v v vEducation Career Consulting • Analytics Bootcamp • Career Services • Mentorship • ISA • Diploma Programs • Part-time Programs • Corporate Training • Data Science • Machine Learning • Big Data • Cloud
DS Career Panel Join our kick a** instructor team! Help corporates upskill their employees Data/Cloud Skills Training for Canadians DS Diploma Toronto Institute of Data Science Training Reskill | Upskill AI Bootcamp Communities Meetups Networking Hiring Event AI Expert Instructing Consulting DS/AI DE Cloud 6 months Success rate 89% 75k Salary Project-based Training Upcoming Events40% Referral by WCD Bring real-world client projects to the classroom Apache Spark Event ML/AI Workshops
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
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
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
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
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
Data Science Immersive (PCC Approved Diploma Program)
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
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)
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
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
Learning Environment Lab Environment (Tools & Platforms) Python | SQL Cloud | Big DataMachine Learning
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
153k 13 Market Research Student Success Job Placement 6 months 2 months 56%89% Data Scientist Security Analyst Senior Analyst Data Scientist Data Engineer 70k 0 New grad 98k 2 FSA 73k 0 New Grad 63k 0 New Grad 78k 3 PWC Sr Data Scientist 83k Salary 50% Referral by WCD 120k 13 QAData Scientist Data Scientist 80k 2 Data Analyst 100k 0 Geology (New Grad0 Data Scientist 70k 0 Statistics (New Grad) ML Engineer
Data Science Job Market
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.
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
Data Jobs in Canada Job Categories and Cities
Data Jobs in Canada Industries – Data Scientist
Data Jobs in Canada Industries – Data Analyst
Data Jobs in Canada Industries – Data Engineer
Data Jobs in Canada Industries – ML Engineer
Data Jobs in Canada SQL is among most wanted skills
Data Jobs in Canada Skills – Data Analyst
Data Jobs in Canada Skills – Data Engineer
Data Jobs in Canada Skills – ML Engineer
Data Science Salaries
Data Science Learning Path
Resources Python Coding Practice Coding & Interviews • LeetCode • HackerRank Book Statistics Online Courses Udemy • Complete Python Bootcamp Datacamp • Introduction to Python
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
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 Nov 16 Nov 3 Nov 16Nov 23 Oct 19
Python
Why Python? The ecosystem
Targeting Profiles Personaliz ation Parse/Filter Classify Synthesize POI Prediction Ontology/TaxonomyContexts URL Parsing POI Database Context Extraction Topic Modeling Content Classification Location Classify Signal Aggregation Taste Merging Taste Scoring Data Data Science Pipelines Data Product POI Context Builder Rule-based Predictor ML Predictor Location Attributes Home/Work Predictor Co-location Location Graph • sklearn • gensim • nltk • mrjob • PySpark • PySpark Why Python? Python in a data science project
Python Data Management Structured Data with Pandas DataFrame Row Index Population Area California 423967 38332521 Florida 170312 19552860 Illinois 149995 12882135 New York 141297 19651127 Texas 695662 26448193 Column DataFrame Values Column Row Row Row Row Row Row Index Row Index Row Index Row Index Row Index Column Index Column Index # row access returns Series states.loc['Florida'] # column access returns Series states['area'] California 38332521 Florida 19552860 Illinois 12882135 New York 19651127 Texas 26448193 population 170312 area 19552860 # index based selection states.iloc[1:3, :1] Row Index Population Florida 170312 Illinois 149995 Series Series DataFrame
Python Data Management Pandas - GroupBy() City Ticket Sales Toronto 100 Montreal 50 Toronto 20 Halifax 40 Montreal 30 Halifax 60 City Ticket Sales Toronto 100 Toronto 20 City Ticket Sales Montreal 50 Montreal 30 City Ticket Sales Halifax 40 Halifax 60 City Ticket Sales Toronto 60 City Ticket Sales Montreal 40 City Ticket Sales Halifax 50 City Ticket Sales Toronto 60 Montreal 40 Halifax 50 Input DataFrame Split DataFrameGroupBy Apply (sum) DataFrameGroupBy Combine DataFrame df = pd.DataFrame({'city' : ['Toronto', 'Montreal', 'Toronto', 'Halifax', 'Montreal', 'Halifax'], 'sales' : [100, 50, 20, 40, 30, 60]})
Python Data Management Pandas - Join/Merge # 1-to-1 join pd.merge(employee, hr, how='inner', on='employee') Other features • Pivot Tables • Window Functions
DataVisualizations Matplotlib, Seaborn, Plotly, Bokeh
Python Plotly Dash
Python Plotly Dash
Python Data Munging Database Integration
from mrjob.job import MRJob class MRWordCount(MRJob): def mapper(self, _, line): for word in line.split(): yield(word, 1) def reducer(self, word, counts): yield(word, sum(counts)) if __name__ == '__main__': MRWordCount.run() from pyspark import SparkContext if __name__ == "__main__": if len(sys.argv) != 2: print("Usage: wordcount <file>", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonWordCount") lines = sc.textFile(sys.argv[1], 1) counts = lines.flatMap(lambda x: x.split(' ')) .map(lambda x: (x, 1)) .reduceByKey(add) output = sorted(counts.collect(), key=lambda x: x[1], reverse=True)[:50] for (word, count) in output: print("%s: %i" % (word, count)) sc.stop() API Support for Big Data Platforms Hadoop/Spark
Machine Learning Sklearn | Gensim | NLTK
ML at Scale PySpark ML
Data Engineering Data Pipelines with Airflow
Deep Learning Strong Python Support
Python IoT/Robotics
Python Application
Data Science Trends
Trends Dask
Trends Auto ML https://towardsdatascience.com/automl-for-data-enthusiasts-30582b660cda
Trends Model Explainability
Trends Featuretools
Trends Model Deployment in Cloud SageMaker EMR ECR S3 Notebook Transform Inference 1. ETL on EMR using Spark 2. Save Model to S3 s3://weclouddata/mod els/gbm20190612 SageMaker Spark ML Container 3. Start notebook instance and deploy model SageMaker Spark ML Container 4. Start SageMaker Spark container for prediction API SageMaker Spark ML Container
Trends Kubernetes On-Prem HDFS S3 Azure Blob Storage Google Cloud Storage vYARN Mesos Kubernetes MapReduce Spark Core Spark DataFrame SQL ML Structured Streaming Graph Frame Hive MahoutPig Impala Presto Kylin
Trends ML on Kubernetes
Learn Python and Data Science with WeCloudData
Python Programming Why Python? • Python is the most popular data science and AI programming language • Many employers prefer candidates with python skills • Mastering python will expose you to not only Data Scientist jobs, but also Data Engineers and DevOps
Python Programming Syllabus • Python use cases • Branching • Loops • Data Types: list, tuple, set • Functions • Lab: social media analytics with python Day 1 Python Basics Day 2 Intermediate Python Day 3 Python Data Analysis • Data Types: String • Data Types: Dictionary • Comprehensions • Regular expression • Modules & Packages • Class and Object • Interview – Prepare for Python Interview Tests • Lab – Class and object • Pandas introduction • Intro to visualization with python • Accessing database with python • Use case: Python for ML and AI • Project: Building your first ML algorithm with python • Python Installation • Jupyter Introduction • Python Introduction • DS Introduction • Twitter Dev API Setup Pre-course Installation & Preview
• Web scraping basics • BeautifulSoup • Selenium • Project #1 kickoff • Matplotlib Review Data Collection Signup • Project #1 Presentation • Seaborn | Plotly • Map Visualization • Building analytics dashboard with Dash • Project #2 kickoff EDA & Data Visualizations • Project #2 Presentation • Predictive modeling lifecycle • Introduction to sklearn • Regression analysis Predictive Modeling • Data Science Intro • Analyze Toronto Open Data Data Science • Advanced Pandas (Merge and Joins) • Advanced Aggregations • Querying databases • Reporting with Pandas and Pivot Data Wrangling • Intro to Statistics and Linear Algebra • Scipy for statistics • Numpy for linear algebra • Time series forecasting with Prophet Statistics and Linear Algebra W1 W3 W5 W2 W4 W6 Final Review Data Science with Python Syllabus (Weekend Cohort – 8 sessions/32 hours) • Python review • Intro to Pandas • Intro to Visualization Self-paced Lectures • Classification models • Model evaluation • Predicting Toronto TTC delay using Sklearn Predictive Modeling W7 • Course review • Introduction to Machine Learning • Introduction to Big Data Final Review W8
Data Science with Python Hands-on Projects This course is instructor-led and project-based. Students will be able to apply the data science skills acquired during the lectures to 2 hands-on projects. The 2 projects will make your 2 Data Science Projects • Web Data Analytics • Data Storytelling (Dashboard + Heroku Deployment) Data Collection BeautifulSoup Selenium Data Cleaning Pandas Matplotlib Data Analysis Matplotlib Pandas SQLAlchemy Story telling Insight Analysis Presentations App Deployment Heroku Flask Visualization Dash Plotly Project #1: Web Data Analysis Project #2: Data Storytelling Project 2 Demo
Data Science with Python Student Project Demo
Data Science with Python Student Project Demo Analyzing the Top Travel Influencers on Instagram
Data Science with Python Student Project Demo
Open a webpage and get HTML of search results page Fishing Online Process data using pandas and save data into .csv file Locating page elements by XPath Extract Target Data NextURL? Yes No Data Science with Python Student Project Demo
Data Science with Python Student Project Demo
Learn Data Science Understand the big picture
Web Crawler Project - Aritzia Student Project Demo
Content Motivation Data Interesting Findings Conclusion Challenges
Info & Motivation ´ Type : Public ´ Traded as : TSX: ATZ ´ Industry : Fashion ´ Founded : 1984 ´ Founder : Brian Hill ´ Headquarters : Vancouver, British Columbia, Canada ´ Products : Clothing
Website
Dataframe ´ Total data : 856,452 ´ Date range : 2019-06-08 21:53:50 ~ 2019-06-20 13:57:19 ´ File numbers : 30
crawler.py
Interesting Findings ´ Categories & Brand ´ Price Distribution ´ Top 20 Colors ´ Weekdays Vs Weekend - Avg Stock ´ On Sale event - Discount% ´ Price Change Vs Stock Correlation
Category Distribution
Brand Distribution Vs. Brand Average Price
Top 20 Colors
Weekdays Vs Weekend - Avg Stock
SALE !
Discount % of Each Brand
Top 10 products – stock change
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
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)
Next Step ´ Detailed size distribution of brands / products ´ Influences of the strength of discount ´ Stock refill timing ´ Long term data analyzing (winter vs. summer)
Data Science with Python - WeCloudData
Data Science with Python - WeCloudData

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Data Science with Python - WeCloudData

  • 1. Python for Data Science Trends and Use Cases WeCloudData @WeCloudData @WeCloudData tordatascience weclouddata WeCloudData tordatascience
  • 2. WeCloudData v v vEducation Career Consulting • Analytics Bootcamp • Career Services • Mentorship • ISA • Diploma Programs • Part-time Programs • Corporate Training • Data Science • Machine Learning • Big Data • Cloud
  • 3. DS Career Panel Join our kick a** instructor team! Help corporates upskill their employees Data/Cloud Skills Training for Canadians DS Diploma Toronto Institute of Data Science Training Reskill | Upskill AI Bootcamp Communities Meetups Networking Hiring Event AI Expert Instructing Consulting DS/AI DE Cloud 6 months Success rate 89% 75k Salary Project-based Training Upcoming Events40% Referral by WCD Bring real-world client projects to the classroom Apache Spark Event ML/AI Workshops
  • 4. 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
  • 5. 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
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. Data Science Immersive (PCC Approved Diploma Program)
  • 10. 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
  • 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. 153k 13 Market Research Student Success Job Placement 6 months 2 months 56%89% Data Scientist Security Analyst Senior Analyst Data Scientist Data Engineer 70k 0 New grad 98k 2 FSA 73k 0 New Grad 63k 0 New Grad 78k 3 PWC Sr Data Scientist 83k Salary 50% Referral by WCD 120k 13 QAData Scientist Data Scientist 80k 2 Data Analyst 100k 0 Geology (New Grad0 Data Scientist 70k 0 Statistics (New Grad) ML Engineer
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  • 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
  • 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 Nov 16 Nov 3 Nov 16Nov 23 Oct 19
  • 37. Targeting Profiles Personaliz ation Parse/Filter Classify Synthesize POI Prediction Ontology/TaxonomyContexts URL Parsing POI Database Context Extraction Topic Modeling Content Classification Location Classify Signal Aggregation Taste Merging Taste Scoring Data Data Science Pipelines Data Product POI Context Builder Rule-based Predictor ML Predictor Location Attributes Home/Work Predictor Co-location Location Graph • sklearn • gensim • nltk • mrjob • PySpark • PySpark Why Python? Python in a data science project
  • 38. Python Data Management Structured Data with Pandas DataFrame Row Index Population Area California 423967 38332521 Florida 170312 19552860 Illinois 149995 12882135 New York 141297 19651127 Texas 695662 26448193 Column DataFrame Values Column Row Row Row Row Row Row Index Row Index Row Index Row Index Row Index Column Index Column Index # row access returns Series states.loc['Florida'] # column access returns Series states['area'] California 38332521 Florida 19552860 Illinois 12882135 New York 19651127 Texas 26448193 population 170312 area 19552860 # index based selection states.iloc[1:3, :1] Row Index Population Florida 170312 Illinois 149995 Series Series DataFrame
  • 39. Python Data Management Pandas - GroupBy() City Ticket Sales Toronto 100 Montreal 50 Toronto 20 Halifax 40 Montreal 30 Halifax 60 City Ticket Sales Toronto 100 Toronto 20 City Ticket Sales Montreal 50 Montreal 30 City Ticket Sales Halifax 40 Halifax 60 City Ticket Sales Toronto 60 City Ticket Sales Montreal 40 City Ticket Sales Halifax 50 City Ticket Sales Toronto 60 Montreal 40 Halifax 50 Input DataFrame Split DataFrameGroupBy Apply (sum) DataFrameGroupBy Combine DataFrame df = pd.DataFrame({'city' : ['Toronto', 'Montreal', 'Toronto', 'Halifax', 'Montreal', 'Halifax'], 'sales' : [100, 50, 20, 40, 30, 60]})
  • 40. Python Data Management Pandas - Join/Merge # 1-to-1 join pd.merge(employee, hr, how='inner', on='employee') Other features • Pivot Tables • Window Functions
  • 45. from mrjob.job import MRJob class MRWordCount(MRJob): def mapper(self, _, line): for word in line.split(): yield(word, 1) def reducer(self, word, counts): yield(word, sum(counts)) if __name__ == '__main__': MRWordCount.run() from pyspark import SparkContext if __name__ == "__main__": if len(sys.argv) != 2: print("Usage: wordcount <file>", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonWordCount") lines = sc.textFile(sys.argv[1], 1) counts = lines.flatMap(lambda x: x.split(' ')) .map(lambda x: (x, 1)) .reduceByKey(add) output = sorted(counts.collect(), key=lambda x: x[1], reverse=True)[:50] for (word, count) in output: print("%s: %i" % (word, count)) sc.stop() API Support for Big Data Platforms Hadoop/Spark
  • 46. Machine Learning Sklearn | Gensim | NLTK
  • 57. Trends Model Deployment in Cloud SageMaker EMR ECR S3 Notebook Transform Inference 1. ETL on EMR using Spark 2. Save Model to S3 s3://weclouddata/mod els/gbm20190612 SageMaker Spark ML Container 3. Start notebook instance and deploy model SageMaker Spark ML Container 4. Start SageMaker Spark container for prediction API SageMaker Spark ML Container
  • 58. Trends Kubernetes On-Prem HDFS S3 Azure Blob Storage Google Cloud Storage vYARN Mesos Kubernetes MapReduce Spark Core Spark DataFrame SQL ML Structured Streaming Graph Frame Hive MahoutPig Impala Presto Kylin
  • 60. Learn Python and Data Science with WeCloudData
  • 61. Python Programming Why Python? • Python is the most popular data science and AI programming language • Many employers prefer candidates with python skills • Mastering python will expose you to not only Data Scientist jobs, but also Data Engineers and DevOps
  • 62. Python Programming Syllabus • Python use cases • Branching • Loops • Data Types: list, tuple, set • Functions • Lab: social media analytics with python Day 1 Python Basics Day 2 Intermediate Python Day 3 Python Data Analysis • Data Types: String • Data Types: Dictionary • Comprehensions • Regular expression • Modules & Packages • Class and Object • Interview – Prepare for Python Interview Tests • Lab – Class and object • Pandas introduction • Intro to visualization with python • Accessing database with python • Use case: Python for ML and AI • Project: Building your first ML algorithm with python • Python Installation • Jupyter Introduction • Python Introduction • DS Introduction • Twitter Dev API Setup Pre-course Installation & Preview
  • 63. • Web scraping basics • BeautifulSoup • Selenium • Project #1 kickoff • Matplotlib Review Data Collection Signup • Project #1 Presentation • Seaborn | Plotly • Map Visualization • Building analytics dashboard with Dash • Project #2 kickoff EDA & Data Visualizations • Project #2 Presentation • Predictive modeling lifecycle • Introduction to sklearn • Regression analysis Predictive Modeling • Data Science Intro • Analyze Toronto Open Data Data Science • Advanced Pandas (Merge and Joins) • Advanced Aggregations • Querying databases • Reporting with Pandas and Pivot Data Wrangling • Intro to Statistics and Linear Algebra • Scipy for statistics • Numpy for linear algebra • Time series forecasting with Prophet Statistics and Linear Algebra W1 W3 W5 W2 W4 W6 Final Review Data Science with Python Syllabus (Weekend Cohort – 8 sessions/32 hours) • Python review • Intro to Pandas • Intro to Visualization Self-paced Lectures • Classification models • Model evaluation • Predicting Toronto TTC delay using Sklearn Predictive Modeling W7 • Course review • Introduction to Machine Learning • Introduction to Big Data Final Review W8
  • 64. Data Science with Python Hands-on Projects This course is instructor-led and project-based. Students will be able to apply the data science skills acquired during the lectures to 2 hands-on projects. The 2 projects will make your 2 Data Science Projects • Web Data Analytics • Data Storytelling (Dashboard + Heroku Deployment) Data Collection BeautifulSoup Selenium Data Cleaning Pandas Matplotlib Data Analysis Matplotlib Pandas SQLAlchemy Story telling Insight Analysis Presentations App Deployment Heroku Flask Visualization Dash Plotly Project #1: Web Data Analysis Project #2: Data Storytelling Project 2 Demo
  • 65. Data Science with Python Student Project Demo
  • 66. Data Science with Python Student Project Demo Analyzing the Top Travel Influencers on Instagram
  • 67. Data Science with Python Student Project Demo
  • 68. Open a webpage and get HTML of search results page Fishing Online Process data using pandas and save data into .csv file Locating page elements by XPath Extract Target Data NextURL? Yes No Data Science with Python Student Project Demo
  • 69. Data Science with Python Student Project Demo
  • 70. Learn Data Science Understand the big picture
  • 71. Web Crawler Project - Aritzia Student Project Demo
  • 73. Info & Motivation ´ Type : Public ´ Traded as : TSX: ATZ ´ Industry : Fashion ´ Founded : 1984 ´ Founder : Brian Hill ´ Headquarters : Vancouver, British Columbia, Canada ´ Products : Clothing
  • 75. Dataframe ´ Total data : 856,452 ´ Date range : 2019-06-08 21:53:50 ~ 2019-06-20 13:57:19 ´ File numbers : 30
  • 77. Interesting Findings ´ Categories & Brand ´ Price Distribution ´ Top 20 Colors ´ Weekdays Vs Weekend - Avg Stock ´ On Sale event - Discount% ´ Price Change Vs Stock Correlation
  • 79. Brand Distribution Vs. Brand Average Price
  • 81. Weekdays Vs Weekend - Avg Stock
  • 83. Discount % of Each Brand
  • 84. Top 10 products – stock change
  • 85. 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
  • 86. 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)
  • 87. Next Step ´ Detailed size distribution of brands / products ´ Influences of the strength of discount ´ Stock refill timing ´ Long term data analyzing (winter vs. summer)