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Applied Data Science with Python Track

Aug 03, 2020 - Nov 02, 2020
Applied Data Science with Python Track

Time Mon Aug 03 2020 at 07:00 pm to Mon Nov 02 2020 at 10:00 pm

Online

USD 85
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Applied Data Science with Python Track Applied Data Science with Python Track
LEARNING PATH: Data Scientist with Python Track

About this Event

This course will not only help you to understand various data science related concepts, but also help you to implement the concepts in an industry standard approach by utilizing Python and related libraries. First, you will be introduced to the various stages of a typical data science project cycle and a standardized project template to work on any data science project. Then, you will learn to use various standard libraries in the Python ecosystem such as Pandas, NumPy, Matplotlib, Scikit-Learn, Pickle, Flask to tackle different stages of a data science project such as extracting data, cleaning and processing data, building and evaluating machine learning model. Finally you'll dive into exposing the machine learning model as APIs. You will also go through a case study that will encompass the whole course to learn end-to-end execution of a data science project.

By the end of this course, you will have a solid foundation to handle any data science project and have the knowledge to apply various Python libraries to create your own data science solutions.

  • 6 Hours Content;
  • 3 Weeks Duration (2 Hours per Week);
  • Beginner Level.

Market Opportunity

Fueled by big data and AI, demand for data science skills is growing exponentially, according to job sites with an average increase of 29% year over year. The supply of skilled applicants, however, is growing at a slower pace – searches by job seekers skilled in data science grow on average by 14% year over year.

Skills you will gain

  • Data Science & Machine Learning;
  • Web Automation & Web Scrapping;
  • Requests & Beautiful Soup;
  • Python Programming;
  • Data Analysis;
  • Data Visualization (DataViz);
  • Numpy & Pandas;
  • Predictive Modelling;
  • Sci-kit Learn;
  • Git;
  • Data Munging;
  • Feature Engineering & Feature Encoding;
  • MatplotLib;
  • Pickle & Flask.

Prerequisites

A minimum of 2 hours a week. All software used in this course is either available for free or as a demo version. Python Crash Course available upon registration.

What you will learn

Course Introduction

  1. Data Science Project Cycle Overview
  2. Why Python for Data Science?

Setting up Working Environment

  1. Python Distributions for Data Science
  2. Python 3.x vs. Python 2.x
  3. Jupyter Notebook
  4. Data Science Project Template
  5. Versioning for Data Science Projects
  6. Introduction to Git

Extracting Data

  1. Extracting Data from Databases
  2. Extracting Data Through APIs
  3. Extracting Data Using Web Scraping
  4. Public Datasets
  5. Committing Changes to Git

Exploring and Processing Data

  1. Introduction to NumPy and Pandas
  2. EDA: Basic Structure
  3. EDA: Summary Statistics
  4. Centrality Measure (Mean and Median)
  5. Spread Measure (Range, Percentiles, Boxplot, Variance and Standard Deviation)
  6. Counts and Proportions
  7. EDA: Distributions
  8. Univariate Distribution: Histogram and KDE Plot
  9. Bivariate Distribution: Scatter Plot
  10. EDA: Grouping
  11. Crosstab
  12. Pivot Table
  13. Data Munging
  14. Missing Value: Issues and Solution
  15. Missing Value Imputation Techniques
  16. Outliers: Detection and Treatment
  17. Feature Engineering
  18. Categorical Feature Encoding (Binary Encoding, Label Encoding & One-hot Encoding)

Building and Evaluating Predictive Models

  1. Machine Learning Basics: Representation and Generalization
  2. Machine Learning Basics: Spam Classification
  3. Machine Learning Basics: Supervised Learning
  4. Machine Learning Basics: Unsupervised Learning
  5. Classifier
  6. Performance Metrics: Accuracy
  7. Performance Metrics: Precision and Recall
  8. Classifier Evaluation
  9. Baseline Model
  10. Linear Regression Model
  11. Logistic Regression Model
  12. Underfitting vs. Overfitting
  13. Regularization
  14. Hyperparameter Optimization: GridSearch
  15. Crossvalidation
  16. K-Fold Crossvalidation
  17. Feature Normalization and Standardization
  18. Model Persistence
  19. Machine Learning API Development



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Tickets

Tickets for Applied Data Science with Python Track can be booked here.

Ticket Information Ticket Price
Standard Registration USD 85
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About The Host: Helping FMCG and public sector organizations survive and thrive in AI & Data Science disruption.
Website Link: https://jillcannonassociates.com/
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Event Information

Date & Time

Mon Aug 03 2020 at 07:00 pm to Mon Nov 02 2020 at 10:00 pm

Location

Online

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