What You Need to Know About Learning Python in Malaysia
Python has become one of the most popular programming languages in the world in recent years. It is found in the top 3 of most rankings for popular programming languages lists. It’s used in everything from machine learning to building websites and software testing. It can be used by developers and non-developers alike. Python has created everything from Netflix’s recommendation algorithm to the software that controls self-driving cars. It is a general-purpose language, which means it’s designed to be used in a range of applications, including data science, software and web development, automation, and generally getting stuff done.
With the rise of Big Data in Malaysia and globally, Python has become the main programming language in data science, allowing data analysts and other professionals to use the language to conduct complex statistical calculations, create data visualizations, build machine learning algorithms, manipulate and analyse data, and complete other data-related tasks.
In addition, Python can build a wide range of different data visualizations, like line and bar graphs, pie charts, histograms, and 3D plots. Python also has a number of libraries that enable coders to write programs for data analysis and machine learning more quickly and efficiently, like TensorFlow and Keras.
Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn’t specialized for any specific problems. This versatility, along with its beginner-friendliness, has made it one of the most-used programming languages today.
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An Insight into the Python Programming Language for Data Scientists
Python is an in-demand, accessible programming language with an active, ever-growing community of users. For anyone looking to switch careers into the tech world through coding, Python is a great place to start.
It’s relatively easy to learn and incredibly versatile, and it’s used in a variety of fields, from data science and machine learning to game design.
Python Developer Roles Are in High Demand
Python developer roles are in high demand, as many major companies — including DropBox, Netflix, and Facebook — use it. Google has even made its own Python style guide. Python developer jobs are also comparatively lucrative.
Data science fresh graduates can demand starting pay in the range of RM4,000-RM8,000 — making it the highest paid entry level job in the country today. Furthermore, an experienced professional in the field can demand up to RM15,000 a month.
What is Python used for?
Python is commonly used for developing websites and software, task automation, data analysis, and data visualization. Since it’s relatively easy to learn, Python has been adopted by many non-programmers such as accountants and scientists, for a variety of everyday tasks, like organizing finances.
What can you do with python?
Some things include:
- Data analysis and machine learning
- Web development
- Automation or scripting
- Software testing and prototyping
- Everyday tasks
Top 3 Uses for Python
- Data Science
- Python is the most-used programming language in data science. In addition to Python’s time-saving libraries, the language’s ability to analyze large data sets very quickly and carry out repetitive tasks make its popularity more than warranted.
- Python has become a staple in data science, allowing data analysts and other professionals to use the language to conduct complex statistical calculations, create data visualizations, build machine learning algorithms, manipulate and analyze data, and complete other data-related tasks.
- Python can build a wide range of different data visualizations, like line and bar graphs, pie charts, histograms, and 3D plots. Python also has a number of libraries that enable coders to write programs for data analysis and machine learning more quickly and efficiently, like TensorFlow and Keras.
- One of the main reasons why Data Analytics using Python has become the most preferred and popular mode of data analysis is that it provides a range of libraries.
- NumPy: NumPy supports n-dimensional arrays and provides numerical computing tools. It is useful for Linear algebra and Fourier transform.
- Pandas: Pandas provides functions to handle missing data, perform mathematical operations, and manipulate the data.
- Matplotlib: Matplotlib library is commonly used for plotting data points and creating interactive visualizations of the data.
- SciPy: SciPy library is used for scientific computing. It contains modules for optimization, linear algebra, integration, interpolation, special functions, signal and image processing.
- Scikit-Learn: Scikit-Learn library has features that allow you to build regression, classification, and clustering models.
- Web Development
- Since Python is such a flexible programming language, it can make it easier to build complex web utilities. Python’s various web frameworks are especially useful — like the popular Django, which can make building both back end and client-side functionality easier and faster. Even large companies use Python for web development; both Google and YouTube have used it extensively for many of their digital infrastructures.
- App Development
- Python is an excellent choice for anyone looking to get into app development. It’s an ideal language for prototyping because of its lower development time and effort. As with web development, app development is made easy by Python’s cross-platform abilities. Specifically, Python has recently become popular in the fast-growing sectors of blockchain app development and gaming app development thanks to its robust frameworks and real-time testing.
What will you Learn in Python Programming?
In the Python programming course you will learn its essential concepts giving you an in-depth knowledge in data analytics, machine learning, data visualization, web scraping, and natural language processing.
As a data scientist, your main work is to use the data to get actionable insights out of it. However, this is a long process in which you will need to write a python program for each of these steps. Therefore you need to have solid python programming fundamentals to be able to write efficient code for your tasks and understand other codes. You will master the essential concepts of data types, variables, operators, tuples, lists, dictionaries, functions, and modules & packages.
- Data types: Python has several built-in data types including integers, floats, and strings. It is important to know how to work with each of these data types and when to use them.
- Variables: A variable is a way to store a value in a program. In Python, you can create a variable by assigning it a value using the equals sign (=).
- Operators: Operators are special symbols in Python that perform specific operations on one or more operands. Some common operators include addition (+), subtraction (-), and multiplication (*).
- Tuples: Tuples are used to store multiple items in a single variable. Tuple is one of 4 built-in data types in Python used to store collections of data, the other 3 are List, Set, and Dictionary, all with different qualities and usage. A tuple is a collection which is ordered and unchangeable. Tuples are written with round brackets.
- Lists: A list is a collection of items in a specific order. Lists are useful for storing data that needs to be accessed in a specific order, or for storing multiple items of the same data type.
- Dictionaries: A dictionary is a collection of key-value pairs. Dictionaries are useful for storing data that needs to be accessed using a unique key.
Control structures: Control structures are blocks of code that determine how other blocks of code are executed. Some common control structures in Python include if statements, for loops, and while loops.
- Functions: A function is a block of code that performs a specific task and can be reused multiple times in a program. Defining and calling functions is an important aspect of programming in Python.
- Modules and packages: A module is a file containing Python code, while a package is a collection of modules. Knowing how to import and use modules and packages is essential for writing larger, more complex Python programs.
Data Manipulation & Analysis
As a data scientist, you will spend a lot of time preparing and manipulating the data to make it ready for analysis and modeling. Therefore it is important to be able to work with python to clean and prepare the data. This includes working with different data types and sizes.
You should be able to use python to manipulate datasets with different sizes and types and analyze them efficiently. Skills in this area include working with libraries like NumPy, and Pandas for structured data manipulation and analysis. In addition to being able to use PySpark for large dataset manipulation as well as use libraries for different types of data such as images, text, and audio if needed.
Data visualization is an important aspect of data science, as it allows you to explore and understand your data, identify patterns and trends, and communicate your findings to others. It is therefore important for data scientists to have a strong understanding and hands-on skills in data visualization tools and how to use them effectively.
There are many libraries and tools available in Python for data visualization, some of the most popular ones include:
- Matplotlib: This is a widely-used library for creating static, animated, and interactive visualizations in Python. It provides a high-level interface for drawing attractive and informative statistical graphics.
- Seaborn: This is a library for creating statistical graphics in Python. It is built on top of Matplotlib and provides a more refined interface for creating visualizations.
Plotly: This is a library for creating interactive visualizations in Python. It is similar to Bokeh, but also includes support for creating visualizations that can be displayed in other contexts such as Jupyter notebooks.
- Bokeh: This is a library for creating interactive visualizations in Python. It is particularly well-suited for creating visualizations that can be displayed in web browsers.
- Altair: This is a library for creating declarative statistical visualizations in Python. It is based on the
- Vega and Vega-Lite visualization grammars, which provide a high-level, concise syntax for creating a wide range of visualizations.
Data Storage and Retrieval
As a data scientist, you will be mainly working with data whether you will have to retrieve or store the data after processing it. Therefore data storage and retrieval skills are important for data scientists because they allow them to efficiently manage and access the data they are working with.
There are many ways to store and retrieve data in Python, depending on the needs of the data scientist and the nature of the data. Here are some common approaches that you may encounter during your career:
- Flat files: Flat files are simple text files that contain tabular data, with each row representing a record and each column representing a field. Flat files can be read and written using Python’s built-in open() function and the various methods of the file object, such as read(), readline(), and write().
- CSV files: CSV (Comma Separated Values) files are a type of flat file that use commas to separate values. They can be read and written using Python’s panda library.
- Relational databases: Relational databases are powerful systems for storing and querying structured data. There are several Python libraries for interacting with popular database management systems (DBMS) such as MySQL, PostgreSQL, and SQLite. Some popular options include psycopg2 for PostgreSQL, mysql-connector-python for MySQL, and sqlite3 for SQLite.
- NoSQL databases: NoSQL databases are designed to handle large amounts of unstructured data, such as that generated by social media, IoT devices, and e-commerce platforms. Some popular NoSQL databases include MongoDB, Cassandra, and Redis. Python provides various libraries for interacting with these databases, such as pymongo for MongoDB and redis-py for Redis.
- Cloud storage: Cloud storage services such as Amazon S3, Google Cloud Storage, and Microsoft Azure Storage provide scalable, flexible options for storing large amounts of data in the cloud. Python provides libraries for accessing these services, such as boto3 for Amazon S3 and google-cloud-storage for Google Cloud Storage.
Applied Machine & Deep Learning
Applied machine learning and deep learning are both important Python skills for data scientists to master. Machine learning involves the use of algorithms and statistical models to enable computers to improve their performance on a given task without explicitly being programmed to perform that task. This is accomplished by training the machine learning model on a dataset and allowing it to learn the relationships and patterns within the data.
Deep learning, on the other hand, involves the use of artificial neural networks to learn and make decisions. Deep learning has proven to be particularly effective in image and speech recognition, natural language processing, and even playing games.
In order to apply machine learning and deep learning in Python, it is important to have a strong understanding of the various algorithms and libraries available. Here are three of the most common libraries you should master as a data scientist:
- Scikit-learn: A machine learning library for Python, which provides a wide range of algorithms for classification, regression, clustering, and dimensionality reduction, as well as tools for model evaluation and selection.
- TensorFlow: An open-source library for deep learning developed by Google, which provides tools for building, training, and deploying machine learning models.
- Keras: A high-level neural network library built on top of TensorFlow, which provides a convenient interface for defining and training deep learning models.