Python has become incredibly popular worldwide, especially in the field of data science. In fact, StackOverflow’s 2022 Developer Survey ranked Python as the fourth most popular technology and the third most desired technology for developers to learn. This is because Python offers a wide range of tools, frameworks, and libraries that are invaluable for various data science applications and beyond.

Because Python is so crucial, companies are eager to hire developers who are skilled in it to improve their data insights and more. They achieve this by either hiring permanent, in-house developers or by partnering with outsourcing providers for their Python development needs.

But before diving into hiring Python experts or applying for Python-related roles, both companies and developers need to understand the key Python skills essential for success in data science and related fields.

Why Learn Python?

• Ease of Learning: Python is easy to read and write. Its readability and straightforward syntax make it a beginner-friendly language. This ease of learning accelerates its learning curve.
• Versatility: Python is a versatile language and can be used for a wide range of applications from web development to machine learning and auto mation. Its adaptability makes it a valuable asset in the industry.
• Cross-platform Compatibility: Python is a cross-platform language i.e. the code written in Python can be run on various operating systems without changes. Its portability is a major asset.

Top 10 Python Skills for Data Scientists

Data scientists use Python for a wide range of tasks, from data analysis and visualization to machine learning and deep learning. In this article, we’ll be going through the top Python skills that are needed and important to be learned by every data scientist in today’s time Here are some top 10 Python skills for data scientists:

1. Python Fundamentals

A few basic and important fundamentals that every data scientist should know are:

• Data types: Various data types like integer, float, character etc are offered by Python. A developer should know the use case of each of these data types and the difference between them.
• Operators: Python provides various arithmetic, comparison, assignment, logical, bitwise, membership and identity operators.
• Variables: Variables allow a developer to store intermediate values in the program. A variable can be assigned a value by using the ‘=’ symbol.
• Lists: Lists are used to store multiple values in a single variable. It is a mutable and ordered sequence of elements.
• Dictionaries: The dictionary stores elements as key-value pairs. It is a collection that is ordered, mutable and does not store duplicate values.
• Function: A function is a block of code that is executed when called. It has parameters as input and returns the result.
• Modules: Modules are Python files that contain code or functionality that can be imported into other Python files.

2. Data Manipulation and analysis

Data manipulation is an important step in data analysis. It is the process of cleaning, restructuring and transforming data to make it suitable for analysis. Pandas is one of the most used and basic libraries used for data manipulation in Python. Following are the key concepts of data manipulation using Pandas:

• Data Frame: Pandas data frame is a two-dimensional tabular data structure used to store and manipulate data.
• Loading Data: Pandas provides various functions to load data of various formats.
• Data Information: Pandas provide functions like head() and info() to easily view and understand our data frame.
• Grouping and Merging: Pandas provide useful functions to group various data frame rows based on specific criteria or to merge various data frame rows.
• Functions Application: Pandas allow to application of functions along the data frame axis.

3. Data Visualization

Data visualization is the representation of data in graphical and visual formats. It can be done in the form of charts, graphs, infographics and even animations. It is an important skill to be learned by every data scientist as it provides insights about our data that help us perform our tasks more effectively. Using this technique, complex information can be presented in an easier and more understandable form. Various data visualization libraries within Python are:

• Matplotlib: It is used for creating static, individual and interactive visualizations.
• Seaborn: It provides a high-level interface for creating graphics.
• Plotly: It provides interactive web-based visualizations.

4. NumPy Library

NumPy is an open-source general-purpose array processing package. It provides multidimensional array objects and tools for dealing with these arrays. It is the fundamental library in Python for numerical computing. It is used in various fields like machine learning, physics, engineering etc. Key concepts of this library are:

• Arrays: NumPy provides multidimensional ndarray as its basic data structure.
• Universal Functions: Universal functions or ufuncs operate element-wise on arrays.
• Shape Manipulation: NumPy provides functions to change the shape of arrays or split or concatenate them.
• Broadcasting: Using implicit operations, you can apply operations on arrays of different shapes and sizes.
• Efficiency: NumPy operations are implemented in C and Fortran, which are more efficient than operations performed using Python loops.

5. Machine Learning (ML) Libraries

Machine learning is a field of study that gives computers the ability to learn without being programmed explicitly. Machine learning libraries are a collection of pre-written code and tools that help develop, maintain, train and deploy machine learning models. These libraries are easy to use and can help complex algorithms and functions. Some prominent machine libraries used nowadays are:

• SciPy: SciPy contains various modules for optimization, linear algebra, integration and statistics.
• Scikit-learn: It is a library for classical ML algorithms and is built on top of NumPy and SciPy.
• Pandas: Pandas is a library used for data analysis.

6. Deep Learning Frameworks

Deep learning frameworks help design, train and validate deep neural networks through a high-level programming interface. These algorithms provide pre-implemented algorithms, optimization techniques and utilities. Some of the recent deep learning frameworks are as follows:

• Tensorflow: Tensorflow is a library used for high-performance numerical computation.
• PyTorch: It is a library supporting computer vision, natural language processing and many more machine learning algorithms.
• Keras: Keras is a high-level neural networks API capable of running on top of Tensorflow, CNTK or Theano.
• Theano: Theano is a popular Python library that is used to define, evaluate and optimize mathematical expressions efficiently involving multi-dimensional arrays.

7. Data Cleaning and Preprocessing

Data pre-processing is the process of transforming the data into a manageable form and understandable by the model we are using. Data cleaning is part of the pre-processing, where data is modified to correct erroneous data, remove redundancies, or deal with incomplete or missing data. Some important steps in data cleaning and preprocessing are:

• Handling Missing Data: Missing values can be filled with values like mean, median and mode of column or advanced techniques like interpolation can be applied.
• Handling Outliers: Outliers can be removed, or transformed or methods that do not affect less sensitive areas can be applied.
• Handling Duplicates: Deciding on which occurrence of a duplicate to keep or to keep all the occurrences.
• Handling Inconsistent Data: Standardize data by applying functions to convert it into a specific format.
• Feature Engineering: Derive new features or choose among existing features for better results.

8. Data Storage and Retrieval

Efficient data storage and retrieval skills are essential for data scientists who work with large amounts of data. Data scientists must know the various approaches for storing and retrieving data, depending on the nature of the data and their needs.

• Proficiency in efficient data storage and retrieval is crucial for data scientists handling large datasets.
• Understanding various methods for storing and accessing data based on its nature and requirements is essential.
• In Python, multiple approaches exist for data storage and retrieval, including flat files, CSV files, JSON files, relational databases, NoSQL databases, and cloud storage services.
• Relational databases, such as those using SQL, are powerful for storing structured data and conducting queries.
• Cloud storage services like Amazon S3, Google Cloud Storage, and Microsoft Azure Storage offer scalable options for storing vast amounts of data in the cloud.
• Python provides libraries like boto3 and google-cloud-storage for efficient access to these services.

9. Big Data Technologies

Big data technologies are tools that are used to process large volumes of data that exceed the capabilities of traditional data processing systems. Big data technologies can be categorized into four main types: data storage, data mining, data analytics, and data visualization. Some key components are:

• Hadoop: Hadoop is a Java-based open-source framework that manages the storage and processing of large amounts of data for applications. Hadoop uses distributed storage and parallel processing to handle big data and analytics tasks, breaking down the workload into smaller tasks that can be executed concurrently.
• Apache Spark: Apache Spark is an open-source integrated analytics engine for large-scale data processing. Spark provides an interface for a programming cluster with built-in data parallelism and fault tolerance.

10. Web Frameworks

Web frameworks help in the development of Web applications, providing a systematic and standardized approach to developing, deploying, and maintaining web-based software. Some web frameworks provided by Python are:

• Django: Django is a free and open-source, Python-based web framework that follows the model–template–views architectural pattern. It provides built-in features for everything including Django Admin Interface, default database – SQLlite3, etc.
• Flask: Flask is a micro web framework written in Python. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions.

Web Scraping using Python (Bonus)

Web scraping is the process of using bots to extract content and data from a website. It involved getting web pages, parsing HTML content and extracting useful information. It is used for data mining, data extraction and data analysis. Web scraping is a powerful tool for data collection and analysis, but it must be done responsibly and ethically while respecting the rights and policies of website owners you must be informed about legal considerations and best practices and to ensure that web scraping is used properly and respectfully implement methods.

Conclusion

In conclusion, acquiring the top Python skills is crucial for aspiring data scientists. In the above article, we have discussed the necessary skills that are required by every data scientist given the versatility of Python. Some of these important skills include Python fundamentals, data manipulation, data visualization, numerical computing, machine learning, deep learning, data preprocessing, database management, big data, web scraping and web frameworks.