Thinking hard about how to become a data scientist but not able to figure out the process? Have you been a data person who has a never-ending obsession with leveraging data? This blog is for you. We will have a step-by-step discussion about how you can become a data science professional in a structured manner.
How to Become a Data Scientist?: a Step-by-step Explanation
Build data skills
Lack of experience working with data? Cut out the stress because you can still become a data scientist.
The only requirement is to form and develop the right data skills. You must understand that data science is a very high-level position and discipline. So, after you have developed a broad knowledge base, should you strive toward this field.
That knowledge base could be anything from mathematics, and statistics, to programming, IT, or data analysis. You would be surprised to know that there are data scientists who started their careers in Finance and Arts.
Irrespective of the field you start with, have a good grasp of the fundamentals that are Python, SQL, and Excel. To work seamlessly with raw data, these skills are necessary. Additionally, it won’t hurt to familiarize yourself with Tableau, a tool that you’ll constantly leverage to create visualizations. The more an opportunity lets you work with data, the higher the chances that it’ll help you with the next step.
Learn the fundamentals of Data Science
Next, you need to build a strong foundation of the data science fundamentals, as mentioned in the previous step. A data science certification course or a Bootcamp is the perfect option to choose from in this case. For knowledge purposes, you will learn how to collect and store data, then analyze and model it, and finally visualize and present it. Furthermore, you will learn to present data leveraging tools such as Tableau, Power BI, and more.
I believe by the end of the course or Bootcamp (if you opt for one), you should become proficient in using Python and R with regard to building models that analyze behavior and foresee unknowns and repackage the raw data into user-friendly forms.
While many a time, you’ll notice job postings that enlist various degrees as a requirement for the respective position, which is mostly non-negotiable, since demand outstrips supply, the proof of skills will often outweigh the credentialism or degrees.
The capability to demonstrate expertise in a domain and not necessarily follow traditional channels is more important to most HR managers out there.
You might also want to read: Future of Data Science: Will It Make a Positive View?
Mastering Important Programming Languages For a Data Science Position
You must understand that every data scientist leverages specialized tools that are particularly leveraged for data cleaning, modeling, and analysis. One of the most important requirements for data scientists, apart from knowing Excel is to familiarize themselves with various programming languages such as Python and R and query languages such as SQL.
Furthermore, you would also have to learn machine learning tools that leverage AI to provide frameworks or systems with the capabilities to learn and become precise without explicit programming. However, the tools are highly dependent on the applications, if you want the computer to identify images, for instance, trends from social media. Data science professionals use tools such as TensorFlow, Accord.Net, Apache Mahout, and more, depending on the factors.
Build Practical Data Skills through working on Data Science Projects
After learning the fundamentals and mastering the programming language, you must apply all that you gained now to ensure you build up enough practical experience to take on corporate roles.
It’s best to take up projects that require a wide range of skills from you, such as Excel and SQL to manage databases, Python or R to analyze data using statistical algorithms, build models to analyze behavior and generate insights, and leverage statistical analysis to foresee the unseen.
Always keep in mind that while you practice, you must try and touch on as many stages in the process as possible. You can commence with company or market sector research which comes in the initial process. Next, you define and collect the appropriate data for the task and then clean and test the data for utility purposes. Lastly, you apply all your algorithms to analyze and model data, ultimately packaging them into easy-to-read dashboards that enable users to interact and directly query your data. It’s better to share your findings with others to up-skill yourself when it comes to communications skills.
You might also want to read: 5 Most in-demand Soft Skills in Data Science for a Successful Career
Hone Visualization Skills and Constantly Practice
To understand practice visualization from the ground level, leverage tools such as Tableau, PowerBI, Bokeh, or Plotly and search for the ideal way to let the data do the talking. Here too, we see the importance and application of Excel into play.
It helps in calculations or presenting graphs by correlating the information within the cells. Excel is virtually unavoidable when we speak about the data science domain. Visualization is just the tip of the iceberg.
More importantly, you must have the ability to present these findings in front of various stakeholders. Communication skills either come naturally, or they require years of hard work. If you fall in the second case, don’t shy down or be demotivated because practice makes perfect. You might want to start small and take baby steps. For instance, you might present your findings before your sibling, a friend, or a pet before hopping into a large crowd.
Develop a Robust Portfolio to Flaunt Your Data Science Skills
After meticulously following the aforementioned steps and gaining practical knowledge and experience, you must focus entirely on building a perfect resume that showcases your skills, achievements, and capabilities that will ensure you land your dream job. A recruiter only knows your profile and not you or your circumstances. So, ensure you provide them with the best possible version of your application and portfolio.
It is imperative to include your works with GitHub in addition to your website. Github helps you showcase your results and process and boost your profile in a public network. A profile even lets the recruiter know about your communication skills, and you do much more than just crunching the numbers. Since data science is a broad field, it won’t hurt to showcase a variety of different techniques you are capable of, such as the ways to solve a problem and the various approaches you bring to the table.
You should back your data findings with a cogent description and exemplify the problems you’re working to solve. This way, the recruiter understands your merit and skills.
As you go about following every point mentioned, building skills, working on real-world projects, developing soft skills, and presenting your portfolio, you tend to become a true professional and understand how to become a data scientist. You are ready to take on the real-life data science problems that await you.
However, data science is a field wherein you could end up wasting years and still learn nothing if you don’t receive the proper guidance. This is how Skillslash comes into the picture. We act as the liaison between you and a successful data science career without wasting time and having regrets. We offer a diverse curriculum on data science, having Advanced Data Science and AI as our flagship program.
Our students have been placed in the best of companies with great packages. We have started a 100% Job Assurance initiative to create a great career for every data science aspirant, provided we receive the required amount of effort and determination. We don’t expect anything except for willingness and effort from the student’s end. Feel free to contact our support team to know more about our courses.