Data science may still be an emerging field however, several colleges are already certifying students in it. It also opens new horizons to some of the highest paying jobs in the world that allow data scientists to scan through petabytes of data for useful information. Currently it is considered to be one of the most challenging and difficult fields to get admission in, and also even more challenging to master. However, if this challenge does not stop you, you can go through this guide to learn the best strategies to put you on the path to being a data science master.
Your education will always be your biggest asset, and this holds especially true for data scientists. Most data scientists are already exceptionally educated and if you wish to compete with them, you will need to have a solid foundation as well. Your best option is to start with a bachelor’s degree in computer science engineering, followed by a master’s degree to specialize as a data scientist. Your mathematical background must also be exceptional since data science involves going through statistical data and making functions that can sort through the data in an effective manner.
However, having a degree isn’t enough. You need to show extra performance that can help you to develop real skills to help in the process of data mining. You can do this by either enrolling in online courses, or by practicing data science projects for your own portfolio.
Having an in-depth knowledge of this very useful programming language will be quite fruitful as it is one of the most preferred language for data science applications along with Python. However, unlike Python, R tends to be harder to learn with its steep learning curve.
However, you can still try several internet courses that will help you get the basics of R Programming in check and in turn help you to make great progress towards your data science career.
As mentioned above, Python is also a very preferred programming language for data science applications. Because Python is very versatile, you can use it for almost all of the involved steps in the data science roles and activities. It can also allow you to use a variety of data formats and even use SQL based databases. Moreover, Python will also allow you to make a GUI based version of your program for the end users in your company to use more easily.
While this isn’t a skill that is explicitly mentioned as a requirement of becoming a data scientist, it is something that can be beneficial. Along with Handoop, being familiar with Hive and Pig can also be a game changer for your career prospects in data science. You should also learn to use various cloud-based data science and machine learning platforms such as AWS, Microsoft Azure, and Google Cloud.
While working as a data scientist, it is quite likely that you can encounter situations where the volume of the data you need to process will be too much for your computer to handle, and you will need to take help from cloud computing services. That is where the above experience will really come in handy for you.
While the Handoop platform has become quite compatible with NoSQL as an important part of data science, you will still be expected to know how to write SQL queries and code. SQL is an important programming language that deals with the handling of databases and allows you to create various queries to search through databases in an effective manner. When you combine this with your python skills, you will be able to make very effective programs to manage a company’s various departments.
To become a data scientist, your proficiency should be that of an expert when it comes to SQL. This will significantly improve the way you are able to communicate with the given data and use it for various purposes of learning and mining data. The concise commands of SQL can also be used with some of the above-mentioned cloud computing services such as AWS, Microsoft Azure, and Google Cloud. Showing SQL projects with the above services will also increase your chances of getting hired at one of the best data science companies in the world such as Google’s DeepMind.
While Handoop may lead the world in cloud computing, Apache spark is the one that data scientists prefer the most for its exceptional speed and performance. This can mostly be attributed to its caching factor. It is also specifically designed for the purposes of data science and will help you run various complicated algorithms at really fast speeds. You will also need to deal with giant seas of data and will need to work to save time on the same which can be achieved easily with this technology. It can also help you to use unstructured data sets.
Apache Spark can either work on a single computer, or multiple computers connected with a high-speed connection. The strength of Apache Spark relies on its platform which allows large data science projects to run smoothly. You should know that you will need advanced hardware to run such a complicated algorithm.
There are several data scientists who are not very familiar with the concepts of machine learning and artificial intelligence. This includes various things like deep learning, neural networks, and even CNNs. You should master these areas as well if you wish to stand out from other data scientists and get a good chance to boost your career. These skills are bound to help you to solve various advanced algorithmic problems that cannot be solved with normal data science methods.
Having advance machine learning skills will also allow you to work in the artificial intelligence sector and work with massive data sets that a machine learning algorithm can easily mine for you.
Most businesses produce enormous sizes of data very often. This needs to be converted into a readable format and used in a way that help your peers to comprehend it easily. Your algorithms can be used to help generate informative graphs from raw data. However, you must have the skills to help you visualize the data and generate readable information. This is the main purpose of a data scientist in a company and will help you earn a good amount of money.
Data science does not often involve dealing with structured data sets, and will sometimes provide you with data sets which are entirely unstructured. There are various examples of this such as videos, customer reviews, audio files, etc. You should be able to use your algorithms to effectively sort through this data and generate results that are streamlined and readable.
Most data scientists will refer to the processes of data analytics on unstructured data sets as dark analytics since they do not understand it very well. Your mastery of unstructured data sets is crucial since it will help you to outperform your competition and use more advanced decision trees for real world applications.
This is a major aspect of becoming a data scientist since your curiosity is what will help to drive your performance further in the field. You should be able to take up means of data analytics that you have not yet gotten familiar with and dig deep into their roots to master them as well. This thirst for knowledge will be a big driving force for your career in data science.
You will also need to be aware of the industry you are working in and the business standards of it. Your major role can be to either do research or to help the business grow at phenomenal rates. Both of these would require you to have a certain proficiency in businesses and also in communication skills. A one-year program from a good business school may be a good idea to help you in this regard. This will also help you to master teamwork and get good support from your peers.
By using the above given data science fundamentals, not only will you be able to make sufficient advancements in the field, but also achieve full mastery of it to a level where your personal skills will be in demand by most organizations in the industry. Good luck for your journey.