Small Basic meets Python, #15 Dictionaries ...
Post #175: YouTube, Socratica, Python Tutorial, 15/49 The Use Of Dictionaries in Python, 2023.
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As a coding person: Coding does require creativity and some people have different ways of coding
But i dont know if it can be considered art
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How to start your career as a coder - guidance of programming for beginners
As we know , the new generation is becoming so advanced with technology. With technology we can do a number of things in less time and effort. Now, learning to code is also a significant part of technology. We can say that coding is an important part of technology. It is a basic and demanding skill for any company which wants to be part of this digital world.
Here are some significant topics that are discussed to become a good coder -.
1.Find out why you want to learn to code-
Before you start studying, think about why you want to learn to code. Think clearly with full focus what thing you want to learn in coding and why. It is too much. After entering there are many parts available which you can explore.
2.Make a great choice in choosing which coding language for you want to go -
In coding, there are too many programming languages which you can learn but learning each language is a difficult task. As a beginner, you can go with HTML or CSS programming languages which do not contain data structure and algorithms.
3.Selecting Best coding bootcamps -
Coding bootcamps are educational programs which are made for development of practical skills. While the institute will different for each bootcamps, you can typically expect to learn:
Programming fundamentals like javascript, CSS and HTML.
Languages which are popular like java, python or C.
Web development.
HTML codes for website development.
4.How to choose a coding bootcamp-
There are various important things which you can remember while choosing a good coding bootcamp -
Learning format - Both online and offline mode for learning is available. You should choose which environment is suitable for you. In online mode you can take classes according to your needs. And in offline mode you get a chance to interact face to face.
Cost - As we discussed earlier, coding bootcamps can be expensive. You should think about how much you have to spend and how much you want to spend on bootcamps.
5.Benefits of joining a coding bootcamp-
Boost your salary potential -
In technical professions, demand is increasing with time in comparison to other professions. For software developers, new opportunities are increasing day by day. Now,any tech professional can join any field and department according to their interest.
Expand your career possibilities -
The best advantage to join a coding bootcamp is you can increase your skill level. You can learn any new thing with the help of that. The following list details some of the more common jobs you may be able to get after your finish a bootcamp:
1. Back - end developer
2. Full - stack developer
3. Junior developer
4. Software engineer
5. Application developer
6. And so on
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Data Science
📌Data scientists use a variety of tools and technologies to help them collect, process, analyze, and visualize data. Here are some of the most common tools that data scientists use:
👩🏻💻Programming languages: Data scientists typically use programming languages such as Python, R, and SQL for data analysis and machine learning.
📊Data visualization tools: Tools such as Tableau, Power BI, and matplotlib allow data scientists to create visualizations that help them better understand and communicate their findings.
🛢Big data technologies: Data scientists often work with large datasets, so they use technologies like Hadoop, Spark, and Apache Cassandra to manage and process big data.
🧮Machine learning frameworks: Machine learning frameworks like TensorFlow, PyTorch, and scikit-learn provide data scientists with tools to build and train machine learning models.
☁️Cloud platforms: Cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure provide data scientists with access to powerful computing resources and tools for data processing and analysis.
📌Data management tools: Tools like Apache Kafka and Apache NiFi allow data scientists to manage data pipelines and automate data ingestion and processing.
🧹Data cleaning tools: Data scientists use tools like OpenRefine and Trifacta to clean and preprocess data before analysis.
☎️Collaboration tools: Data scientists often work in teams, so they use tools like GitHub and Jupyter Notebook to collaborate and share code and analysis.
For more follow @woman.engineer
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