Top Python Concepts That Help Get Jobs in 2021

Top Python Concepts That Help Get Jobs in 2021

Python holds the title as one of the most popular data science languages. Because LearningFuze focuses on in-demand data science skills, Python is an essential topic to dive into so students are equipped to tackle the job market. 

Python is versatile, easy to learn, and open source, which means it comes with a strong community. Its simplicity and accessibility make it an ideal language for someone interested in pivoting into the data science world. Because Python has such a great community with a plethora of resources available from it being open source, aspiring data scientists can include libraries in their projects.

In addition to its accessibility, jobs requiring Python are growing and in demand. According to LinkedIn’s Emerging Jobs Report, data science has had a 37% hiring growth for the last three years. Since it is a full-stack, meaning it can be used for front end, back end, and data science, Similar to JavaScript being necessary for web developers, Python is a must for data scientists. 

LearningFuze’s team has spoken with industry experts, hiring partners, and data science instructors who provided an overview of the top Python skills to hone in on to land a data science job. The overview is broken down by general Python skills and Python libraries. While this breakdown is a great guide in laying the foundation for a data science education, LearningFuze instructors encourage practical application. Our data science program provides graduates with actionable ways to get a data science job. 

Here are some concepts LearningFuze delves into through offered courses:

General Python Concepts:

  1. Functions and lambda functions
    A function is a reusable code that performs a single action. It is defined by using def as a keyword and only runs when called. Lambda functions (aka anonymous functions) are one-line functions. They are unnamed because they are types of functions that are only used once. Lambda functions are extensively used in data science since one-time calculations are common. 
  2. Data acquisition from SQL, web scraping, JSON, XML
    In data science, how you read and obtain data is the first step in the process. Since data is usually housed in a database, SQL is a must. Python installation will always contain sqlite3, which is a Python SQL library. Web scraping is a common way to extract data from the web. Because Python is the most popular language for web scraping, it is a basic and essential skill to learn. In addition, understanding JSON and XML is necessary if coming from an API or a machine.  
  3. Data cleaning and preparation
    Data is notoriously messy. It’s very difficult to get good, clean data on the first try. A data scientist must know how to find, correct, remove, and fill in missing values. Data preparation is essential in order to make the data readable and easily analyzed. 
  4. Date and time handling
    Date and time handling will be a large part of Python. Dates and times can be presented in many different formats (for example, DD/MM/YYYY or MM/DD/YYYY), and being able to convert one format to another will be extremely useful.

Python Libraries:
To reiterate, Python’s accessibility is due to its community. There are endless libraries available written by intelligent data scientists. These libraries are one of Python’s biggest advantages. Here are some libraries that will be beneficial to a data scientist:

  1. Pandas
    Pandas is the number one library for data scientists because it is used to put data in a tabular format, similar to how Excel functions. It’s fast and powerful and crucial in landing a data science job.
  2. Scikit-Learn
    Scikit-learn is the machine learning library. Machine learning jobs have increased by about 75% throughout the last four years, which makes Scikit-Learn an invaluable skill when pursuing data science.
  3. Matplotlib and Seaborn
    Matplotlib and Seaborn are visualization libraries. Simply put, visualization is a way to tell a dataset’s story by turning it into trends or patterns. Matplotlib and Seaborn are the libraries to help a data scientist do so. 
  4. Keras
    Keras is an API for deep learning. Deep learning is large neural networks and keras is a deep learning library that is easy to use.
  5. NumPy
    NumPy is a library used for arrays. It is specifically useful for doing logical and mathematical operations on Arrays. Think of an array as a vessel that holds items of the same type.
  6. SciPy
    SciPy, short for Scientific Python, is a library for scientific and technical computing. 

In a nutshell, Python’s open-source nature with a community to back it up, full language, and accessibility to resources make it a powerful language to learn. 

LearningFuze makes it a point to focus on the most valuable skills for those looking to break into the data science world. Our instructors and Program Managers will be there to equip you during every step. It’s up to you to take the first one: change your life by visiting our website to start your journey toward a data science career! 

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