About this course
Learn data science with python is a series of courses that contains three parts, before going to the course details let us understand why python is used for data science?
Why Python for data science?
As you know, there are plenty of programming languages that are providing much-needed options to execute Data Science jobs. It has become complex to choose a particular language.
However, It is data that provides a peep into these languages that are making their way into the world of Data Science, i.e., nothing can be as compelling as the data itself unveiling the results of the comparison between different Data Science tools. For almost a decade, researchers and developers have been debating over the topic, “Python for Data Science”.
With the adoption of open-source technologies taking over the traditional, closed-source commercial technologies, Python has become extremely popular among Data Scientists and Analysts.
In many scenarios, Python is the programming language of choice for the daily tasks that data scientists and is one of the top data science tools
If you’re a programmer looking to switch into an exciting new career track, or a data scientist looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists.
About series of courses
Learn data science with python series consists of three parts, compiled differently in three different courses. Commencing with part one, which is introductory covering the basics of python. The second part covers mastering Matplotlib to produce several plots and graphs. The third and last part consists of writing your own functions to solve problems with data. Moreover, at the end of every course, you will be subjected to a project where you can apply your knowledge thought in the class to solve real-world problems.
Introduction to Python will be the first step in your data science journey. In this course, You will learn the python fundamentals used by all data scientists to analyze, implement, and manipulate large amounts of data along with scientific computing using NumPy.
This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to data science.
This course is delivered using HD video lectures and detailed code notebooks that you can download and use to learn at your own pace.
In this course, you will learn how to program with Python, how to use variables and data types, and also how to create and manipulate Python lists along with how to leverage Python code written by other
developers and be introduced to NumPy one of the most important packages in the world of data science.
This course covers:
- Setting up your Python development environment using Anaconda & Jupyter Notebooks.
- An overview of how to use Jupyter Notebooks.
- Python basics and Python lists.
- Python functions & packages.
- Introduction to Learn Data Science with Python
- How to make the most of this class- Skillshare
- Class Frequently Asked Questions
- Python & Jupyter Notebook Environment Set-up
- Jupyter Notebook
- Basics in Python
- Python Lists
- Python Functions & Packages
The Plots, Graphs, Dictionaries, Control Flow & Loops are essential steps to keep moving forward. Right out of the gate, in part 2 you will learn Python visualizations skills that can be applied in the real world. You will learn how to master Matplotlib to produce several plots and graphs.
Also In Part 2, you will learn how to create Python dictionaries which are like lists on steroids and will help you harness and manipulate massive amounts of data.
Soon afterward, You will be introduced to Python topics, the Pandas Data Frame which is the standard way of working with tabular data in Python and you will learn about how to import CSV files so that you can manipulate and access the information within.
Boolean logic is the foundation of giving your programs the power of decision making. You will learn how to combine different comparison operators with Boolean logic to control the flow of your Python programs.
- Class Introduction.
- Information about how to get the most from this class- Skillshare.
- Class Frequently Asked Questions.
- Hands-on how to set-up your development environment.
- Jupyter Notebook.
- Create Graphs, Plots, and Histograms.
- Python Dictionaries.
- Python Pandas & Data Frames.
- Controlling the flow of your programs.
- Python Loops.
This class kicks off with Python Functions. Being a Data Scientist means that you will be constantly writing your own functions to solve problems with data.
You will begin with learning how to write simple functions, then move on to writing functions that accept multiple arguments and return multiple values. Throughout the lesson, you’ll learn how to apply your new skills to various data science scenarios.
This lesson will teach you Lambda functions which allow you to write functions quickly and on-the-go. You’ll also learn how to handle errors that your functions will generate from a time-to-time throw.
Using what you have learned in lesson 3 you’ll build on your knowledge of iterators and learn about list comprehensions, which allow you to create complicated lists and lists of lists in one line of code. It will conclude with a practice lesson that will give you the opportunity to practice your newly learned data science skills. Each lesson in this class is created using Jupyter Notebooks which means that you can download the Python code, experiment, and improve upon it. You also get to keep the class notes for future learning and reference.
- Class Introduction
- How to get the most from this class- Skillshare
- Class Frequently Asked Questions
- How to set-up your development environment
- Jupyter Notebook Introduction
- Python Functions
- Default arguments, variable-length arguments & scope
- Lambda functions & error-handling
- Python Iterators
- List comprehensions & generators
- Practice Lesson
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- Online Course
- 1-4 Weeks
- Paid Course (Paid certificate)
- Data Science Data Science with 'Python'