Doing Data Science with Python

By Abhishek Kumar
This course shows you how to work on an end-to-end data science project including processing data, building & evaluating machine learning model, and exposing the model as an API in a standardized approach using various Python libraries.
Play course overview
Course info
Rating
(183)
Level
Beginner
Updated
Dec 28, 2017
Duration
6h 26m
Table of contents
Course Overview
Course Overview 2m
Course Introduction
Course Introduction 5m Target Audience 1m Course Prerequisites 1m Data Science Project Cycle Overview 2m Why Python for Data Science? 2m Course Outline 4m Summary 1m
Setting up Working Environment
Introduction 0m Overview 1m Python Distributions for Data Science 2m Python 3.x vs. Python 2.x 1m Demo: Installing Ananconda Distribution 4m Jupyter Notebook 2m Demo: Setting up Jupyter Notebook on Local Machine 2m Demo: Jupyter Notebook - Basics 7m Demo: Jupyter Notebook - Magic Functions 8m Data Science Project Template 3m Demo: Setting up Cookiecutter Data Science Project Template 5m Versioning for Data Science Projects 2m Demo: Add Project to Git 2m Summary 1m
Extracting Data
Introduction 1m Overview 1m Extracting Data from Databases 1m Demo: Extracting Data from Databases 8m Extracting Data Through APIs 2m Demo: Extracting Data Through APIs 4m Extracting Data Using Web Scraping 2m Demo: Web Scraping Using Requests and BeautifulSoup 7m Demo: Getting Titanic Dataset Using Requests : Part 1 - Initial Preparation 3m Demo: Getting Titanic Dataset Using Requests : Part 2 - Downloading Data 8m Demo: Creating Reproducible Script for Getting Titanic Data 5m Public Datasets 2m Committing Changes to Git 1m Summary 1m
Exploring and Processing Data - Part 1
Introduction 5m Overview 1m Introduction to NumPy and Pandas 2m EDA: Basic Structure 1m Demo: Investigating Basic Structure 10m Demo: Selection, Indexing, and Filtering 6m EDA: Summary Statistics 1m Centrality Measure 0m Centrality Measure: Mean 2m Centrality Measure: Median 2m Spread Measure 1m Spread Measure: Range 2m Spread Measure: Percentiles and Boxplot 3m Spread Measure: Variance and Standard Deviation 2m Demo: Getting Summary Statistics for Numerical Features 5m Counts and Proportions 1m Demo: Summary Statistics for Categorical Feature 5m Summary 1m
Exploring and Processing Data - Part 2
Introduction 1m Overview 1m EDA: Distributions 1m Univariate Distribution: Histogram and KDE Plot 5m Demo: Creating Univariate Distribution Plots 2m Bivariate Distribution: Scatter Plot 1m Demo: Creating Scatter Plots 4m EDA: Grouping 2m Demo: Grouping and Aggregation 5m Crosstab 1m Demo: Crosstab 2m Pivot Table 2m Demo: Pivot Table 3m Summary 1m
Exploring and Processing Data - Part 3
Introduction 2m Overview 1m Data Munging 2m Missing Value: Issues and Solution 3m Missing Value Imputation Techniques 3m Demo: Treating Missing Values Using Pandas - Part 1 7m Demo: Treating Missing Values Using Pandas - Part 2 2m Demo: Treating Missing Values Using Pandas - Part 3 10m Outliers: Detection and Treatment 4m Demo: Detecting and Treating Outliers Using Pandas and NumPy 7m Feature Engineering 2m Demo: Feature Creation Using Pandas and NumPy – Part 1 2m Demo: Feature Creation Using Pandas and NumPy – Part 2 3m Demo: Feature Creation Using Pandas and NumPy – Part 3 1m Demo: Feature Creation Using Pandas and NumPy – Part 4 4m Categorical Feature Encoding 1m Categorical Feature Encoding: Binary Encoding 1m Categorical Feature Encoding: Label Encoding 2m Categorical Feature Encoding: One-hot Encoding 2m Demo: Categorical Feature Encoding Using Pandas 3m Demo: Drop and Reorder Columns Using Pandas 2m Demo: Save Dataframe to File Using Pandas 3m Demo: Reproducible Script for Data Processing Using Pandas and NumPy 7m Demo: Creating Visualization Using MatPlotlib 7m Demo: Committing Changes to Git 1m Summary 1m
Building and Evaluating Predictive Models – Part 1
Introduction 3m Overview 2m Machine Learning Basics 1m Machine Learning Basics: Representation and Generalization 2m Machine Learning Basics: Spam Classification 3m Machine Learning Basics: Supervised Learning 3m Machine Learning Basics: Unsupervised Learning 2m Titanic Disaster Data Challenge 2m Classifier 4m Performance Metrics 1m Performance Metrics: Accuracy 1m Performance Metrics: Precision and Recall 3m Classifier Evaluation 3m Baseline Model 2m Demo: Preparing Data for Machine Learning Model 6m Demo: Building and Evaluating Baseline Model 4m Demo: Making the First Kaggle Submission 5m Linear Regression Model 3m Logistic Regression Model 5m Demo: Building Logistic Regression Using Scikit-Learn 3m Demo: Making Second Kaggle Submission 2m Summary 1m
Building and Evaluating Predictive Models – Part 2
Introduction 2m Overview 2m Underfitting vs. Overfitting 3m Regularization 4m Hyperparameter Optimization: GridSearch 2m Crossvalidation 2m K-Fold Crossvalidation 1m Demo: Hyperparameter Optimization Using GridSearchCV 3m Demo: Making Third Kaggle Submission 1m Feature Normalization and Standardization 2m Demo: Feature Normalization and Standardization Using Scikit-Learn 4m Model Persistence 1m Demo: Model Persistence Using Pickle 4m Machine Learning API Development 2m Demo: Hello World API Using Flask 5m Demo: Machine Learning API Using Flask 7m Demo: Committing Changes to Git 1m Summary 2m Where to Go from Here? 2m
Description
Course info
Rating
(183)
Level
Beginner
Updated
Dec 28, 2017
Duration
6h 26m
Description

Do you want to become a Data Scientist? If so, this course will equip you with concepts and tools that can bring you to speed and you can utilize the skills acquired in this course to work on any data science project in a standardized approach. This course, Doing Data Science with Python, follows a pragmatic approach to tackle end-to-end data science project cycle right from extracting data from different types of sources to exposing your machine learning model as API endpoints that can be consumed in a real-world data solution. 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.

Course FAQ
Course FAQ
Is Python good for data science?

Yes! Python's robust libraries are ideal for manipulating data and it is a relatively easy language to learn for data analyst beginners!

Is Python better than R for data science?

Python and R are both great programming languages geared towards data science. However, Python is often easier for beginners, and is a more general purpose language with easy to read syntax. Python is better for raw data scraping, while R is more useful in analyzing already scrubbed data.

Will we be using Python libraries?

Yes. We will go over various standard Python libraries such as NumPy, Scikit-Learn, Pandas, Pickle, Matplotlib, and Flask to help with extracting, cleaning, and processing data, and building machine learning models.

What is data science with Python?

Simply put, it is a combination of statistical and machine learning techniques through the use of Python programming to help analyze and interpret data.

Are there prerequisites to this course?

Some previous exposure to Python or its libraries may come in handy, but is not required. Just come with an interest in data science.

Why learn data science?

Data science is a super popular field these days. Through data science we can find meaningful and valuable insights, and provide data-driven evidence to help organizations be more efficient and successful.

About the author
Abhishek Kumar
About the author

Abhishek Kumar is a data science consultant, author and speaker.He holds Master’s degree from University of California, Berkeley.His focus area is machine learning & deep learning at scale.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone. My name is Abhishek Kumar with Pluralsight, and welcome to my course on Doing Data Science with Python. Data science is one of the hottest fields these days, and no wonder data scientist has been termed as the sexiest job of the century, because with the help of data science you can unravel meaningful insights, and generate data-drive evidences that can benefit organizations in a significant way, and provide them a competitive edge. So if you also want to make a jump-start in this fascinating field of data science, or if you are already in this field and want to learn the standardized way of tackling end-to-end data science project cycle using Python, then this course is for you. In this course, we will dive into various phases of a data science project such as data extraction, data processing and visualization, building, evaluating, and fine-tuning predictive models, and finally, exposing your predictive models as APIs for real-time integration. The only prerequisite to this course is that you should be familiar with the basics of programming with Python. This course will not only help you to build the foundations of data science, but to also help you learn to implement the concepts through lots and lots of demos. by the end of this course, you'll be in a position of knowledge and skills necessary to kick start your data science journey in the Python world. So please join me in this very exciting course on Doing Data Science with Python, at Pluralsight.

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