Things you must know before you start working as a Data Scientist

Do you aspire to be a data scientist? It can be intimidating if you have decided to be one. From Google to Facebook, every company today wants to join the race in machine learning and data science. And there is no denying that data science is indeed one of the fastest-growing fields in technology.

The global machine learning market is expected to reach USD 23.46 Bn by 2023. A career in data science offers good perks if you know about the right tools and technology.

What techniques do you need to focus on? How many stats do you need to learn, and how many codes do you need to know. If you aspire to be a data scientist, you need proper guidance and thorough knowledge to make right choices. And this is precisely what you’ll find in the next pointer below.

Here’s a compiled list of things you must know before you start working as a data scientist.

1. Programming Knowledge

Programming provides an effective way to communicate with machines. To become an efficient data scientist, you will need to choose a programming language of your choice. Python and R are two popular names that come with their set of pros and cons.

Python is a resourceful programming language having a multiple set of data science libraries with rapid prototype features. On the other hand, the R language is known for its visualization feature and stats analysis.

If you need to choose – Python is an easier language for complex machine learning tasks due to its support and higher availability of libraries.

2. Fundamentals of Data Science

For a career in data science – you must be aware of machine learning skills and principles of data science. For this, you must understand the difference between machine learning concepts and deep learning. You must be well versed in the difference between data science, data engineering, and business analytics.

In addition, you must be well aware of all the tools and terminologies used in data science. Moreover, it is vital to know about the supervised and unsupervised learning concepts. In fundamentals, knowledge about the classification and regression of problems is a must. Start with the ML concepts.

3. Choose the Right role

There are many roles to choose from in the data science industry – from data visualization expert to machine learning expert and data scientist to data management. Besides, knowing the latest technologies like facilities management can expand the scope of your job role so that you can focus on building high-quality models that have quality, efficiency and precise performance.

Depending on your experience and background, choosing a role would be easier. If you are not clear about the role – you can talk to experts and figure out the roles in the industry.

Take mentorship from people and understand the core concepts. Moreover, it is crucial to figure out what you want and are good at – choose a role relevant to your preference.

4. Probability and Stats

Stats is essential before you work on high-quality models. Machine learning also starts as stats and then advances in the later stages. For a data scientist- it is a must to know median, mode, descriptive stats, and standard deviation. In addition, knowledge of various probability distributions, populations, and samples is essential.

5. Data Visualization

A data visualization expert knows how to build a story out of visualization. For the concept – you must be familiar with bar charts and pie charts. On a more advanced level – you must know about thermometers and waterfall charts.

These concepts are handy while dealing with the stage of exploratory data analysis. Every language that you will learn will offer a great set of libraries for advanced charts. Besides knowing the handy skills, you can master the skills of data visualization through the advanced courses.

6. Focus on Practical Applications

It is essential to focus on practical applications while undergoing training. It will help you understand the core concept and give you a deeper insight into how things work when applied in reality. Therefore, make sure you do all the exercises to understand the applications.

In addition, work on open data sets and apply to learning modules. Also, look at solutions by experts who have worked in this field. Moreover, the most practical way to build your data learning and machine learning profile is to participate in data science competitions.

7. Work on your Communication Skills

Most people don’t associate communication with data science roles. They expect that a technically profound person will ace the role of data scientist, which is untrue. Communication skills are vital when you’re working in any field that includes data science.

Besides giving you a head-up in work, it lets you share your ideas freely with a co-worker or your peer group. Moreover, effective communication will let you prove a point when you need to explain something regarding the working of an application. Thus, it plays a key role and is a significant factor in the field of data science.

8. Basic Database Knowledge

Data is not available in tabular form. As a beginner, you will start your machine learning journey in an excel file or CSV. However, SQL is the essential skill you need to acquire as a data scientist. Knowing data storage techniques and the basics of big data will help you gain big in the field.

Most organizations hire SQL professionals that can help with day-to-day tasks. Besides, database knowledge will give you an edge over your co-workers, which is an advantage. You can take up a comprehensive SQL course for this.

9. Model Deployment

You can’t ignore the importance of model deployment in your pathway to a thriving data science career. Once you complete the project – it is time to reap the power of the machine learning model. In short, you need to know the basics of model deployment.

Moreover, it is one of the most significant steps from the business viewpoint. Even if it is not the profile requirement, you must be aware of model deployment. You can deploy machine-learning models using the flask or coding. Additionally, you can deploy an image classification model using code.

10. Python Coding

Python is the essential coding language required for a role in data science along with Perl and Java. Because of its flexibility, you can use the language in all the steps involved in data science models or other deployment processes.

Python integrates with various data formats, and you can easily import SQL tables into your codes. Moreover, it allows you to create data sets. You can find the data sets you need from search engines. Apart from Python, Apache–spark is also gaining popularity. Data scientists can prevent the loss of data in data science with Apache spark.

11. Focus on your Resume

Your resume is the most significant thing that will get you a job as a data scientist. Following this, you have to make sure you include all the important skills in the document. Prioritize your skills according to the job role and not what you know.

Mention all the data science projects to prove your skills. Besides, keep in mind that skills are more important than certifications for your data science job. Also, regularly update your skills side by side and not once in three-four months. It’s essential to keep all your fonts and formats similar in your resume.

12. Structured Thinking

A successful career as a data scientist requires you to acquire logical and structured thinking apart from degrees, knowledge, and the right skills. You will need to break down a problem into multiple parts and effectively solve an issue as structured thinking.

As the lead, you need to look at the problem from a different outlook. Also, acquire natural curiosity to ask questions and learn new concepts. Curiosity is a necessary skill set to be a great data scientist. However, learning never stops – so it is vital to master the skills.

13. Teamwork

A data scientist can’t work alone. In your role, you will have to work with different company executives and co-workers to develop strategies, manage and create better products. Also, work with the marketing team to launch better-converting campaigns.

Besides this, you will have to work with everyone in the company including – customers. In addition, you will collaborate with team members for use cases to know about your business goals and data, which will help you solve different problems related to a project. Thus, teamwork is an essential quality for the role of the data scientist.

14. Take Proper Guidance

Finding the correct guidance is significant. As data science and machine learning concepts are new, so are its alumni. There are many ways to become a data scientist, and the easiest way is to spend money to get the best degree in the field.

But certification does not guarantee you the best results or better job prospects in the field. You need to find a mentor in this field who will guide you all along the way. Moreover, you can ask questions and find solutions when you know you have proper guidance. Therefore, it is vital to understand the essential skills for the role of the data scientist.

Final Recap

The demand for a data scientist is enormous, and many companies are investing significant time in hiring a data scientist. Apart from education, data scientists need to work on skill sets and essential languages.

In the technical skill set – Python coding, SQL database, Machine Learning, AI, and Apache Spark are some vital technologies a data scientist should know.

Also, to be successful in your role – you will need a strong understanding of the industry and its requirements. Being able to differentiate problems and find solutions on time is also critical. With proper guidance and experience – you can do wonders in this field.