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Introduction to Data Analysis Using Python

4.6191 ratings at Coursera

Overview

This course is the seventh course in the Google Data Analytics Certificate. The Python programming language is a powerful tool for data analysis. In this course, you’ll learn the basic concepts of Python programming and how data professionals use Python on the job. You'll explore concepts such as object-oriented programming, variables, data types, functions, conditional statements, loops, and data structures.

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will:

-Define what a programming language is and why Python is used by data scientists

-Create Python scripts to display data and perform operations

-Control the flow of programs using conditions and functions

-Utilize different types of loops when performing repeated operations

-Identify data types such as integers, floats, strings, and booleans

-Manipulate data structures such as , lists, tuples, dictionaries, and sets

-Import and use Python libraries such as NumPy and pandas

Syllabus 4

  1. Hello, Python! 5 hours

    You’ll discover the main features and benefits of the Python programming language, and how Python can help power your data analysis. Python is an object-oriented programming language based on objects that contain data and useful code. You’ll become familiar with the core concepts of object-oriented…

  2. Functions and conditional statements 5.5 hours

    Next, you’ll discover how to call functions to perform useful actions on your data. You’ll also learn how to write conditional statements to tell the computer how to make decisions based on your instructions. And you’ll practice writing clean code that can be easily understood and reused by other d…

  3. Loops and strings 7 hours

    You’ll start off by exploring loops, which repeat a portion of code until a process is complete. You’ll learn how to work with different kinds of iterative or repeating code, such as for loops and while loops. Then, you'll explore strings, which are sequences of characters like letters or punctuati…

  4. Data structures in Python 10 hours

    Now, you’ll explore data structures in Python, which are methods of storing and organizing data in a computer. You’ll focus on data structures that are among the most useful for data professionals: lists, tuples, dictionaries, sets, and arrays. You’ll also discover how to categorize data using data…

Advantages and disadvantages

Advantages

  • Structured courses with graded quizzes, assignments and deadlines you can reset.
  • A shareable certificate from the university or company when you pay.
  • Financial aid is often approved for students in India (apply 15 days before you need it).
  • From Google, a well-regarded name.
  • Rated 4.6 out of 5 by 191 learners.
  • Self-paced: start any time.
  • A clear syllabus (4 parts) you can see before you start.

Disadvantages

  • Paid after a 7-day free trial (Coursera Plus or per course).
  • Some courses can be audited for free, but graded work and certificates need payment.

Some points apply to every course of this kind; see how we rank.

This course is paid. Here is how to take it for free

  • Free trials: You can start a 7-day free trial for many individual courses, Specializations, or a Coursera Plus subscription to test full course features. Cancel before the seventh day if you do not want to be charged.
  • Financial aid: If you cannot afford the fee for a certificate, you can apply for financial aid through the link on the course home page by filling out an application about your background and goals.

Taught by

  • Google Career Certificates

Google is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (88/100). They build what they teach.

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