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Fundamentals of Scalable Data Science

Approx. 20 hours to complete

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Course Summary

This course is an introduction to Data Science, covering topics such as data preprocessing, visualization, modeling, and evaluation. It is designed for beginners with no prior knowledge of Data Science.

Key Learning Points

Learn how to preprocess data and visualize it using Python libraries
Understand the basics of machine learning and how to apply it to real-world problems
Learn how to evaluate the performance of your models and make data-driven decisions

Learning Outcomes

Ability to preprocess and visualize data using Python libraries
Understanding of machine learning and its application to real-world problems
Ability to evaluate the performance of models and make data-driven decisions

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Familiarity with statistics and linear algebra

Course Difficulty Level

Beginner

Course Format

Video lectures
Hands-on exercises
Real-world examples

Similar Courses

Applied Data Science with Python
Machine Learning
Statistics and Data Science

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Description

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models.

In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks. Through this exercise you'll also be introduced to the most fundamental statistical measures and data visualization technologies. This gives you enough knowledge to take over the role of a data engineer in any modern environment. But it gives you also the basis for advancing your career towards data science. Please have a look at the full specialization curriculum: https://www.coursera.org/specializations/advanced-data-science-ibm If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging. After completing this course, you will be able to: • Describe how basic statistical measures, are used to reveal patterns within the data • Recognize data characteristics, patterns, trends, deviations or inconsistencies, and potential outliers. • Identify useful techniques for working with big data such as dimension reduction and feature selection methods • Use advanced tools and charting libraries to: o improve efficiency of analysis of big-data with partitioning and parallel analysis o Visualize the data in an number of 2D and 3D formats (Box Plot, Run Chart, Scatter Plot, Pareto Chart, and Multidimensional Scaling) For successful completion of the course, the following prerequisites are recommended: • Basic programming skills in python • Basic math • Basic SQL (you can get it easily from https://www.coursera.org/learn/sql-data-science if needed) In order to complete this course, the following technologies will be used: (These technologies are introduced in the course as necessary so no previous knowledge is required.) • Jupyter notebooks (brought to you by IBM Watson Studio for free) • ApacheSpark (brought to you by IBM Watson Studio for free) • Python We've been reported that some of the material in this course is too advanced. So in case you feel the same, please have a look at the following materials first before starting this course, we've been reported that this really helps. Of course, you can give this course a try first and then in case you need, take the following courses / materials. It's free... https://cognitiveclass.ai/learn/spark https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/f8982db1-5e55-46d6-a272-fd11b670be38/view?access_token=533a1925cd1c4c362aabe7b3336b3eae2a99e0dc923ec0775d891c31c5bbbc68 This course takes four weeks, 4-6h per week

Outline

Introduction the course and grading environment
Course Overview and a warm welcome
Overview of technology used within the course
Intro to Apache Spark
Assignment and Exercise Environment Setup
IMPORTANT: How to submit your programming assignments
Challenges, terminology, methods and technology

Tools that support BigData solutions
Data storage solutions
Parallel data processing strategies of Apache Spark
Programming language options on ApacheSpark
Functional programming basics
Introduction of Cloudant
Resilient Distributed Dataset and DataFrames - ApacheSparkSQL
OPTIONAL: Test Data Generator (data is provided for you already)
Apache Parquet (optional)
Create the data on your own (optional)
Data storage solutions, and ApacheSpark
Programming language options and functional programming
ApacheSparkSQL and Cloudant

Scaling Math for Statistics on Apache Spark
Overview of the week...
Averages
Standard deviation
Skewness
Kurtosis
Covariance, Covariance matrices, correlation
Multidimensional vector spaces
Exercise 2
Averages and standard deviation
Skewness and kurtosis
Covariance, correlation and multidimensional Vector Spaces

Data Visualization of Big Data
Overview of the week
Plotting with ApacheSpark and python's matplotlib
Dimensionality reduction
PCA
Exercise on Plotting
Exercise on PCA
Visualization and dimension reduction

Summary of User Reviews

Learn data science online with Coursera's comprehensive course. Students love the interactive approach to learning and the practical applications of the material.

Key Aspect Users Liked About This Course

practical applications

Pros from User Reviews

Interactive approach to learning
Comprehensive course content
Practical applications of material
Great for beginners and professionals alike

Cons from User Reviews

Some material may be too basic for advanced learners
Assignments can be time-consuming
Limited interaction with instructors

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