Programming for Data Science with Python

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Product is rated as #11 in category Data Science
Learner rating9.4
  • Course Platform: Udacity
  • Level: Beginner
  • Flexible deadlines
  • Price: Paid Course
  • Class length: Approx. < 3 Months

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Data Science Course

About Data Science?

Udacity Image For About Data Science (DS)

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract

meaningful insights from data. Data science professionals apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value.

Why Data Science is Important?

For the time being more and, more companies are nowadays coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need efficient development and implementation of data science capabilities or risk being left behind.

Udacity Programming for Data Science with Python

What is Python?

Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options. Python programming is absolutely simple, so it is easy to learn since it requires a unique syntax that has been focused on readability. Also, developers can read and translate Python code much easier than other languages. In turn, this reduces the cost of program maintenance and development because it allows teams to work collaboratively without significant language and experience barriers.

About this Data Science course

In this Micro-degree (Nano-degree) program you will learn the python programming fundamentals required for a career in data science. By the end of the data science course or program, you will be able to use Python, SQL, Command-Line, and Git.

What you will learn from this data science course?

You will learn the programming fundamentals required for a career in data science. By the end of the program, you will be able to use Python, SQL, Command-Line, and Git.

Why should you enroll in this Course?

The Programming for Data Science with Python Nano-degree program offers you the opportunity to learn the most important programming languages used by data scientists today.

This is the opportunity to get your start in the fascinating field of data science and learn Python, SQL, terminal, and git with the help of experienced instructors.

Syllabus

This Data Science Course is a kind of micro-degree (Nano-degree) program, it contains syllabus as below:

Course 1: Introduction to SQL

LESSON ONE: Basic SQL
  • Write common SQL commands including SELECT, FROM, and WHERE.
  • Use logical operators like LIKE, AND, and OR.
LESSON TWO: SQL Joins
  • Write JOINs in SQL, as you are now able to combine data from multiple sources to answer more complex business questions.
  • Understand different types of JOINs and when to use each type.
LESSON THREE: SQL Aggregations
  • Write common aggregations in SQL, including COUNT, SUM, MIN, and MAX.
  • Write CASE and DATE functions, as well as work with NULLs.
LESSON FOUR: Advanced SQL Queries
  • Use subqueries, also called CTEs, in a number of different situations.
  • Use other window functions including RANK, NTILE, LAG, LEAD new functions along with partitions to complete complex tasks.

Course 2: Introduction to Python Programming

In this data science course, there are 7 lessons on the basics of the python programming:

LESSON ONE: Why Python Programming
  • Gain an overview of what you’ll be learning and doing in the course.
  • Understand why you should learn python programming.
LESSON TWO: Data Types and Operators
  • Using python programming, represent data using Python’s data types: integers, floats, booleans, strings, lists, tuples, sets, dictionaries, compound data structures.
  • Perform computations and create logical statements using Python’s operators: Arithmetic, Assignment, Comparison, Logical, Membership, Identity.
  • Declare, assign, and reassign values using Python variables and modify values using built-in functions and methods.
LESSON THREE: Control Flow
  • Write conditional expressions using if statements and boolean expressions to add decision making to your Python programs.
  • Use for and while loops along with useful built-in functions to iterate over and manipulate lists, sets, and dictionaries.
  • Skip iterations in loops using break and continue.
  • Condense for loops to create lists efficiently with list comprehensions.
LESSON FOUR: Functions
  • Run and edit python scripts.
  • Interact with raw input from users and identify and handle errors and exceptions in your code.
  • Find and use modules in Python Standard Library and third-party libraries.
  • Experiment in the terminal using a Python Interpreter.
LESSON SIX: NumPy
  • Create, access, modify, and sort multidimensional NumPy arrays (ndarrays).
  • Load and save ndarrays.
  • Use slicing, boolean indexing, and set operations to select or change subsets of a ndarray.
  • Understand the difference between a view and a copy of ndarray and perform element-wise operations on ndarrays.
  • Use broadcasting to perform operations on ndarrays of different sizes.
LESSON SEVEN: Pandas
  • Create, access, and modify the main objects in Pandas, Series, and DataFrames.
  • Perform arithmetic operations on Series and DataFrames.
  • Load data into a DataFrame.
  • Deal with Not a Number (NaN) values.

Course 3: Introduction to Version Control

In this data science course, there are three lessons on using and configuring git:

LESSON ONE: Shell Workshop
  • The Unix shell is a powerful tool for developers of all sorts. Get a quick introduction to the basics of using it on your computer.
LESSON TWO Purpose & Terminology
  • Learn why developers use version control and discover ways you use version control in your daily life.
  • Configure Git using the command line.
LESSON THREE: Create a Git Repo
  • Create your first Git repository with git init.
  • Copy an existing Git repository with git clone.
  • Review the current state of a repository with powerful git status.
LESSON FOUR: Review a Repo’s History
  • Review a repo’s commit history git log.
  • Customize git log’s output using command line flags in order to reveal more (or less) information about each commit
  • Use the git show command to display just one commit.
LESSON FIVE: Add Commits to a Repo
  • Master the Git workflow and make commits to an example project.
  • Use git diff to identify what parts of a file have been changed in a commit.
  • Learn how to mark files as “untracked” using .gitignore.
LESSON SIX: Tagging, Branching, and Merging
  • Tagging, Branching and Merging.
  • Organize your commits with tags and branches and jump to particular tags and branches using git checkout.
  • Learn how to merge together changes on different branches and crush those pesky merge conflicts.
LESSON SEVEN: Undoing Changes
  • Learn how and when to edit or delete an existing commit.
  • Use git commits -amend flag to alter the last commit and use git reset & git revert to undo and erase commits.

What jobs will this program prepare you for?

This data science course is an introductory program and is not designed to prepare you for a specific job. However, as a graduate of this program, you will be proficient in the programming skills used in many data analysis and data science roles, including Python, SQL, Terminal, and Git.

This Nano-degree Programs Includes:

  • Experienced Project reviews.
  • Technical mentor support.
  • Personal career services.

How is the Nano-degree program structured?

The Programming for Data Science with R Nano-degree program is comprised of content and curriculum to support three (3) projects. They also estimated that students can complete the program in three (3) months, working 10 hours per week. Each project will be reviewed by the Udacity reviewer network. Feedback will be provided, and if you do not pass the project, you will be asked to resubmit the project until it passes.

Prerequisites:

There are no prerequisites for this program, aside from basic computer skills. You should feel comfortable performing basic operations on your computer (e.g., opening files, folders, and applications, copying, and pasting).

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

9.4Expert Score
Learner experience
The score is based on the user experience, rated by the learners.
Learner rating
9.4
PROS
  • The exercises on each chapter will provide more clarity.
  • Effective projects will help you to apply the lessons learned in the course.
  • Well-structured, engaging and informative program will definitely surpass your expectations.
CONS
  • Should increase time on each lesson.
  • Need to be familiar with some basic mathematics algorithms.
  • It should have covered the basic points.

Specification: Programming for Data Science with Python

Course Platform Udacity
Level Beginner
Class length <3 months
Program details Microdegree
Enrollment Paid Course (paid certificate)
Course Subjects Data Science, Data Science with 'Python'
Offered by Organization Mode

Videos: Programming for Data Science with Python

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Programming for Data Science with Python
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