Being a data scientist is currently one of the best careers in the 21st century because this technical era calls for “big data” at every point in the growth process. From small/medium size to large-sized businesses and organizations, there is an infinite amount of data that is to be sorted, interpreted, and applied for carving out valuable results. As such an abundant amount of data and information can’t be handled by average humans, companies hire data scientists that are skilled in gathering, organizing, and analyzing the data for helping people in every business niche.
Data scientists typically have technical backgrounds in education, however, individuals with relevant skill sets and knowledge about the same are eligible to become data science engineers. Also, majoring in the areas of math and statistics works as a brownie point for an individual. If you are intrigued by this hot job profile, then let’s dive into the role of data scientist along with all the requirements to have a career in the same.
Who is a Data Scientist?
Data scientists are the individuals that perform the extraction and interpretation of data and information for strengthening the overall goals of business organizations and providing better ROI. They get indulged in constant manipulation of data to modify them from the raw stage to cleaner, more interpretable presentation. Majorly, large-scale organizations that acquire a huge amount of data or businesses that have deployed the use of machine learning or AI hires data scientists.
Any individual can be a data scientist irrespective of their background or field, all that is required is the relevant knowledge and a little bit of hands-on experience. A data scientist usually works with a lot of different teams or individuals in an organization that may include data engineers, business intelligence specialists, and architects, etc. for achieving various goals at times. Following are some of the most common tasks data scientists perform in any organization:
- Curate the data and frame the detailed analytics-based problems for providing direct and fruitful impacts to the organizations.
- Executing the collection, filtering, transforming, and processing of unstructured data from various sources to carve out robust strategies.
- Constructing statistical models and implementing machine learning algorithms for performing the in-depth analysis of the already-processed data.
- Using the data models and mapping the patterns to find out the solutions and opportunities for future growth of organizations along with solving the current problems.
- Communicate and collaborate with various teams and describe them developed models to work in a better way.
Must-have Skills to Become a Data Scientist
A good data scientist must have a moderate level of expertise in different fields such as data mining, data analysis, programming, mathematics & statistics, machine learning, business, data hacking, data visualization, database & (big) data that works in parallel with each other. Following is a brief introduction about the most crucial skills to become a data scientist that one can acquire to kick-start his/her career as a data science engineer:
Mathematics, or probability, statistics, and linear algebra specifically lie in the heart of data science.
There are various tasks that involve the use of probability & statistics to a greater extent, such as tasks that involve the segregation of data on the basis of sex, age, etc. Also, having a mathematical background helps in working on data or building data products that require a mathematical mindset and a calculative thought process.
Furthermore, after structuring the data into a meaningful format, users are required to be well-versed with statistics for analyzing and visualizing the same. Linear algebra gets widely used in machine learning and also helps in uncovering few characteristics of users in a big data set.
For finalizing the small and quick products or tailoring several data systems together, the knowledge of coding is a must. Data scientists will be able to clean and organize unstructured data. Some of the frequently used and integral programming languages and technologies used in this field are Python, R, SAS, SPSS, Perl & SQL/NoSQL.
3. Machine Learning
Machine learning (ML) is a method of training computers or machines to learn and fetch information by themselves on the basis of dynamic data feeding. A lot of online stores and marketplaces, self-driving cars, etc. in the 21st century rely on the abilities of machine learning to enhance the user’s experience and help businesses get more traction.
It also aids organizations to enhance the processes in near real-time and reduce the cost spent on human inputs. These things collectively make ML an integral factor for data scientists because it allows them to make high-value predictions and make real-time decisions.
4. Knowing About the Databases
Since every data scientist has to access, modify, and store data in the databases, they are required to have a detailed knowledge of some of the widely-used databases such as MySQL and NoSQL. Other databases such as MongoDB and Apache Cassandra are also good for learning and gaining hands-on experience.
Specifically, having good command of SQL can do wonders for you as a data scientist. Not only will it help in accessing and communicating with data, but will also aid in working with them and getting amazing insights. Moreover, it compacts the command to make them to the point that eventually saves a lot of time and reduces the amount of programming one has to perform for various queries.
5. Big Data
Big data is a term that refers to large datasets generated from various sources at very high frequency. As such data cannot be controlled or managed by ordinary database management systems, robust tools like Apache Hadoop and Apache Spark are used. Therefore, data scientists need to learn about these tools.
6. Apache Hadoop
Though Hadoop is less popular than Spark, it is heavily preferred by a lot of organizations. Data scientists often encounter situations in which the space consumed by the stored data and information crosses the memory limit of the computer device. Under this condition, they have to transfer the excess data to other servers, and this is where Hadoop plays its role. It enables users to instantly transfer the data to various points on a system, and also allows data exploration, data filtration, and data sampling for better management.
7. Apache Spark
Apache Spark is emerging as one of the most popular technologies for the management of big data globally. It offers functionalities similar to Hadoop but comes with a much faster interface for quick processing. It is possible because Spark caches the computations in memory instead of reading and writing them on disks. It is tailor-made for data scientists to enable them to run the complicated processes much quicker along with disseminating data processing while dealing with huge data.
Data scientists are also enabled for the management of unstructured data for using them on machines for better data feeding. In addition, it also prevents all types of data losses along with providing faster processing speeds. An individual can execute analytics as well based on all the data intake.
8. Data Visualization
Every organization irrespective of its size produces a good amount of data regularly. Since this data cannot be understood by other members of organizations, the dataset needs to be converted into a visually appealing format for easy understanding. Typically, the data is converted into the form of charts and graphs because it highlights the key takeaways along with representing the trends more subtly.
Therefore, data scientists should be aware of data visualization tools such as ggplot2, Tableau, etc. Knowing these tools will enhance the explanation of data outcomes in a better way and will provide a format that is easy to comprehend. Therefore, teams can directly start working with the data by instantly grasping the insights and staying ahead of the competition.
9. Unstructured Data
The primary goal of data scientists is to work with unstructured data, which means undefined content that doesn’t follow the database pattern. They are unclear texts or code lumps tied together and unable to deliver any type of quality information. Because of the complexity, they’re often referred to as “dark analytics”.
Structuring the unstructured and carving out proper information out of them is a crucial process for data scientists. Therefore, they should be skilled in understanding and manipulating the unstructured data derived from various platforms.
Steps to Become a Data Scientist
After knowing all the must-haves for a data science engineer, let’s dive into the steps required to become a data scientist. Keep in mind that before proceeding with the below-mentioned steps, an individual should first acquire all the aforementioned skills.
1. Apply the Skills
Right after grabbing the relevant skills, an individual should get some first-hand experience by working directly with the technologies. Having hands-on experience will make candidates stand out from the crowd.
2. Get a Real-world Project
Now that an individual has learned all the required skills, worked on several projects and has also passed multiple tests, it’s time to get started with real projects as an intern or a junior data scientist.
Every big and small company releases notifications for internship programs. Individuals should never miss these opportunities and keep on applying till they get the relevant job. The demand for data scientists is exponentially increasing day by day, so there are high chances for anyone to get placed successfully.
3. Increase your Connection
Even after getting a job, an individual should not restrict himself from looking for better opportunities. For this, they should make strong networking with the help of various platforms such as LinkedIn and Quora by sharing valuable information and showing expertise.
Can You Become a Data Scientist Without a Degree?
Yes, becoming a data scientist is possible without a degree, but never without the required skills and knowledge about mathematics. Many of the top data scientists are from diverse backgrounds that have transitioned themselves into data science from other industries like machine learning, data analysis, or software engineering. Users can take the help of various online boot camps and courses and can test their technical skills. Also, they can connect with other members learning the same for knowledge sharing and better direction.
- The one that is extremely crucial for an individual to stand out from the crowd is the projects and internships they have done related to data science. This shows the level of skill set one has. So, always try to approach dynamic projects that will help in learning the concepts deeply along with a proper understanding of the subject.
- Create a personal website for showcasing your projects/portfolio, internship details, certificates, achievements, about yourself, etc. Representing yourself and all the associated information properly provides an outstanding impression to the recruiters of the big companies.
- Try connecting with more and more people in the same niche through offline and online modes. Keep in touch with industry experts and contact them for queries and to achieve bigger goals.
Being a data scientist is one of the hottest career options with a better scope for the future. Coming decades will have more robust opportunities for this challenging role. However, one must learn deeply about data science to get started. Further, they should develop their skills in the required fields to get a job in the same. It includes having knowledge about probability, statistics, linear algebra, programming, ML, AI, databases, big data, etc. that requires both theoretical and practical knowledge.
Follow the steps mentioned in the blog for becoming a successful data science engineer in the future. All the best!