Harvard University
Introduction to Data Science with Python
4.3174 ratingsLearn the concepts and techniques that make up the foundation of data science and machine learning.
Overview
This course provides foundational big data practitioner knowledge and analytical skills using popular big data tools, including Hadoop and Spark. Learn and practice your big data skills hands-on.
What you'll learn
- Describe Big Data, its impact, processing methods and tools, and use cases.
- Describe Hadoop architecture, ecosystem, practices, and applications, including Distributed File System (HDFS), HBase, Spark, and MapReduce.
- Describe Spark programming basics, including parallel programming basics, for DataFrames, data sets, and SparkSQL.
- Describe how Spark uses RDDs, creates data sets, and uses Catalyst and Tungsten to optimize SparkSQL.
- Apply Apache Spark development and runtime environment options.
Skills you'll practise
Advantages and disadvantages
Advantages
- University courses you can audit for free, with lectures, readings and practice quizzes.
- A verified certificate from the university if you pay for it.
- From IBM, a well-regarded name.
- Self-paced: start any time.
Disadvantages
- Graded assignments and the certificate need the paid track.
- Audit access can expire a few weeks after the course ends.
- Learning is free, but the certificate costs money.
- Some parts (graded work, certificate) are paid.
Some points apply to every course of this kind; see how we rank.
Free to audit
- Free: Choose "Audit this course" when you enrol: lectures, readings and practice are free.
- Paid: Graded assignments and the verified certificate (Certificate $99). Audit access may end after the course closes.
Before you start
Computer and IT literacy. Curiosity about how data is managed.
Taught by
- Karthik MuthuramanSoftware Engineer (Machine Learning)
- Aije EgwaikhideSenior Data Scientist
IBM is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (84/100).
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