1- Python

If you were to deliver Oscars to programming languages, the most-deserving candidate would have been Python. It has been the fastest-growing and most used major programming language today. Thanks to its versatility and user-friendliness, Python can be used for almost all the steps involved in data science processes. The massive libraries of Python, which are extremely easy to learn even for a beginner in the field of data science, are used for data manipulation. Apart from being an independent platform and an open source language, Python also easily integrates with any existing infrastructure, which you can then use to solve the most complex problems in data science. Python is used by many banks for crunching data while several institutions use it for data visualization and processing. Even weather forecast companies like ForecastWatch use and leverage Python.

2- R

Once, this open source language was the primary language for data science. Though it has been replaced by Python as the leading programming language that data scientists need to know, it’s still not far behind Python. The roots of R are in statistics, and it’s still extremely popular with statisticians. Be it statisticians, data scientists, or analysts – anyone wanting to make sense of data can use R for data visualization, statistical analysis, and predictive modeling. Thanks to its open interfaces, R can easily integrate with other applications and systems.

3- Machine Learning and Artificial Intelligence (AI)

If you’re opting for a data scientist career, you should be familiar with ML (machine learning) and AI. Since the field of data science needs the application of skills in different areas of machine learning, you should learn and hone your skills in various machine learning areas and techniques like reinforcement learning, supervised machine learning, neural networks, adversarial learning, logistic regression decision trees, etc. Knowing these will help you to solve different data science problems that are based on forecasts of key organizational outcomes.

Whether you’re doing a full-time course or an intensive short-term data science bootcamp in Silicon Valley, you should also learn (in addition to the above) Hadoop, SQL, and Apache Spark. Apart from the technical skills, your professional life would also demand you to be an expert in some non-technical skills. These include having:

• Intellectual curiosity to ask questions that help you discover and prepare data;
• Business acumen to have a good knowledge of the industry you’re working in and knowing what business problems you’re going to help the company solve;
• Good communication skills to fluently and clearly translate your technical findings to a non-technical team; and
• A collaborative attitude to work in a team, which could involve product managers and designers, server and client software developers, company executives, marketers, etc.

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To learn more about data science, click here and read our another article.

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1- Projects come first

Having a good amount of diverse data science projects can dramatically improve the quality of your portfolio. Projects demonstrate that you have the skills and expertise to work on real-life business problems. If you’re pursuing some sort of data science program from a reputable institute, you shouldn’t have to face any problem in having projects to be solved. If you have opted for the self-learning method, you should focus on carrying out some personal data science projects to build up your portfolio.

2- Explore blogging platforms

While having your own website can surely help you develop your online presence, you should focus on getting some visibility as well. And popular blogging platforms are simply excellent for this purpose. Look for a couple of blogging platforms that get a decent amount of footfalls and come with a good tagging system that would help you reach greater audiences. Once you have your profile set up, post the successful assignments you have completed so far.

3- Have a GitHub profile

Today, Github is one of the most effective online platforms targeted at tech enthusiasts. Over the years, the platform has gained immense popularity. When you have solved a critical problem and truly want people to see the way you have done it, GitHub should be your best bet. Whether it’s a write-up or a code, drop it on the platform and share it with others. There’re lots of companies across the globe keep on looking at GitHub profiles to identify competent and genuine data science professionals.

4- Focus on social media

Having a strong presence on popular social media platforms like Twitter, LinkedIn etc can greatly help you in building a strong data science portfolio. On those platforms, you not only get chances to interact with other data science professionals and go through their inputs but can also share your insights and articles to people who may be your future employer.

Final Thoughts

When you have a strong data science portfolio, it’s up to you to opt for the way to demonstrate it to prospective employers. Depending on the data science position you’re looking it should be decided. Apart from the above tips, there’s one thing you should never overlook – the importance of practice. When people see your work and provide feedback or praise, you can rest assured of getting a bit closer to what the world calls an “expert”.

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To learn more about data science, click here and read our another article.

The post How do you build a Data Science portfolio? appeared first on Magnimind Academy.