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30-Day Money-Back Guarantee

This course includes:

  • 7 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Python

Python for Data Science – Hands On: 2-in-1

A practitioner's guide covering essential data science principles, tools, and techniques
Rating: 4.0 out of 54.0 (5 ratings)
14 students
Created by Packt Publishing
Last updated 10/2018
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Learn data analysis, manipulation, and visualization using the Pandas library
  • Create statistical plots using Matplotlib and Seaborn to help you get insights into real size patterns hidden in data
  • Become proficient in working with real life data collected from different sources such as CSV files, websites, and databases
  • Get hands on with Numpy for numerical and scientific computation
  • Implement machine learning algorithms and delve into various machine learning techniques, and their advantages and disadvantages
  • Work with regression, classification, clustering, supervised and unsupervised machine learning, and much more!
  • Manipulate, fix, and explore data to solve data science problems

Course content

2 sections • 60 lectures • 6h 54m total length

  • Preview02:09
  • What Is Data Science?
    02:28
  • Python Data Science Ecosystem
    02:25
  • Preview01:11
  • Starting Jupyter
    01:12
  • Basics of Jupyter
    02:02
  • Markdown Syntax
    02:43
  • 1D Arrays with NumPy
    07:42
  • 2D Arrays with NumPy
    11:31
  • Functions in NumPy
    10:24
  • Random Numbers and Distributions in NumPy
    08:45
  • Create DataFrames
    06:44
  • Read in Data Files
    06:26
  • Subsetting DataFrames
    06:04
  • Boolean Indexing in DataFrames
    04:40
  • Summarizing and Grouping Data
    05:28
  • Matplotlib Introduction
    09:53
  • Graphs with Matplotlib
    06:15
  • Graphs with Seaborn
    11:44
  • Graphs with Pandas
    08:45
  • Machine Learning
    03:29
  • Types of Machine Learning
    03:23
  • Introduction to Scikit-learn
    04:00
  • Linear Regression
    12:24
  • Logistic Regression
    06:29
  • K-Nearest Neighbors
    07:59
  • Decision Trees
    05:44
  • Random Forest
    05:47
  • K-Means Clustering
    05:16
  • Preparing Data for Machine Learning
    11:16
  • Performance Metrics
    09:11
  • Bias-Variance Tradeoff
    08:03
  • Cross-Validation
    06:13
  • Grid Search
    08:37
  • Wrap Up
    02:42
  • Test Your Knowledge
    5 questions

  • Preview03:29
  • Introducing Data Science and Python
    03:28
  • Getting Ready
    03:25
  • A Glance at the Essential Packages
    03:32
  • Introducing the Jupyter Notebook
    11:28
  • Scikit-learn Toy Datasets
    13:26
  • Data Loading and Preprocessing
    19:38
  • Working with Categorical and Text Data
    11:54
  • Creating NumPy Arrays
    12:04
  • NumPy's Fast Operations and Computations
    10:05
  • Preview04:17
  • Building New Features
    04:21
  • Dimensionality Reduction
    14:17
  • The Detection and Treatment of Outliers
    10:59
  • Validation Metrics
    06:37
  • Testing and Validating
    07:07
  • Cross-Validation
    06:58
  • Hyperparameter Optimization
    09:09
  • Feature Selection
    09:55
  • Wrapping Everything in a Pipeline
    06:45
  • Preparing Tools and Datasets
    02:54
  • Linear and Logistic Regression
    04:04
  • Naive Bayes
    03:55
  • K-Nearest Neighbors
    01:57
  • An Overview of Unsupervised Learning
    09:56
  • Test Your Knowledge
    5 questions

Requirements

  • Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this course.

Description

The markets of today are always open. With business happening round the clock, data is continuously being generated. If you know how, then you can extract useful information from this data This course teaches you how get your data to talk and tell you information that you can use to strengthen your business or organization.

This course begins by discussing what data science is and the role it can play in business development. You'll then learn the basics of Jupyter, which is the environment that you'll use for coding. Once you are comfortable with the basics, you'll delve into the anatomy of Numpy and learn ways to use it to your advantage. you'll learn how to create DataFrames and summarize and group data. As you progress, you'll learn how to plot graphs using Seaborn and Matplotlib. As the course advances, you'll be introduced to machine learning and explore ways to process data using techniques, such as k-means clustering and random forest. The later lectures show you how to finetune your application by optimizing the hyperparameters. Through linear and logistic regression, you'll learn how to train and fit the regressor in the training set. Before the course concludes, you'll also get an overview of unsupervised learning.

By the end of this course, you'll be ready to work on data science projects or developing your own data science applications.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

  • Ilyas Ustun is a data scientist. He is passionate about creating data-driven analytical solutions that are of outstanding merit. Visualization is his favorite. After all, a picture is worth a thousand words. He has over 5 years of data analytics experience in various fields like transportation, vehicle re-identification, smartphone sensors, motion detection, and digital agriculture. His Ph.D. dissertation focused on developing robust machine learning models in detecting vehicle motion from smartphone accelerometer data (without using GPS). He publishes from time to time on his website.

  • Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from Natural Language Processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meetups, conferences, and other events.

  • Luca Massaron is a data scientist and marketing research director specializing in multivariate statistical analysis, machine learning, and customer insight, with over a decade's experience solving real-world problems and generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. From being a pioneer of web audience analysis in Italy to achieving the rank of a top ten Kaggler, he has always been very passionate about every aspect of data and its analysis, and also about demonstrating the potential of data-driven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, Luca believes that a lot can be achieved in data science just by doing the essentials.

Who this course is for:

  • This learning path is for data science entrants who wish to explore the world of data science by entering in it. This learning path is also for data analysts and data engineers to help them tackle real-world data science problems without wasting any time.

Instructor

Packt Publishing
Tech Knowledge in Motion
Packt Publishing
  • 3.9 Instructor Rating
  • 56,859 Reviews
  • 345,401 Students
  • 1,423 Courses

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.



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