How to Become a Competent Data Scientist in 2022?

Data Science is the current buzzword in the current tech industry!

Is data science the right career for you?

If so, how do I become a data scientist?

What are the skills required?

Read this first,

Right now, data science is everywhere. One after the other, tech companies across the world are turning to data science to resolve the most diverse problems out there. This situation has placed data scientists in an advantageous position in terms of employment and payment.

You could take the first step towards a successful career in data science with the aid of NIT DATA’s career guide for data scientists. Our guide provides an in-depth overview regarding the occupational skills that you should learn, the best data science training options, how to become a data scientist and career paths in data science and more.” 

There are a lot of ways to be a data scientist, but since this is typically a higher-level job, data scientists are traditionally highly educated, holding degrees in mathematics, statistics, and computer science, among others. This has started to change, though. 

How to Become a Competent Data Scientist in 8 Steps:

• Develop the Relevant Data Skills 

If you have no data work experience, you still have a chance of becoming a Data Scientist, but you need to build up the proper experience in order to pursue the Data Science career. 

Data scientist is a highly-skilled job; you will need to build a wide foundation of knowledge in a related field before reaching this degree of specialization. That might be mathematics, engineering, statistics, data analytics, programming, or IT–some data scientists even started in finance and baseball scouting. 

Data scientist related skills:

• Engineering
• Mathematics 
• Programming
• Statistics 
• Data analysis 
• Information Technology

But no matter what area you begin with, it needs to involve fundamentals: Python, SQL, and Excel. These skills will be critical for working with and organizing the raw data. It also does not hurt to become familiar with Tableau, the tool you will be using frequently for creating visualizations. Consider a Python course in Hyderabad by NIT DATA where you can build all the python programming skills along with the career guidance.

Keep your eyes open for opportunities that can help you begin thinking like a data scientist; the more that this experience allows you to do with data, the more that’ll help with your next steps. 

• Master Data Science Fundamentals

A data science bootcamp or program may be the perfect way to get, or to strengthen, your foundations in data science.     

Expect to learn core skills such as how to collect and store data, analyze and model data, and visualize and represent data using each of the tools in a data science toolkit, including specialty applications such as the visualization programs Tableau and PowerBI – among others. 

By the end of your data science training  in Hyderabad, you should be able to leverage Python and R to create models that analyze behaviors and predict unknowns, and you will be able to repackage data in a usable format. Many job listings will mention an advanced degree as a requirement for data science positions. 

    Data Science Fundamentals:

• Collecting & storing data
• Analyzing & modeling data
• Building models that aid in predicting unknowns 
• Visualizing, repackaging & presenting data in user-friendly forms     

• Learn Key Programming Languages for Data Science 

Data scientists depend on a range of tools and special software programs developed especially to clean, analyze, and model data. In addition to the commonplace Excel, data scientists must know statistical programming languages such as Python, R, or Hive, as well as query languages such as SQL. 

One of the most essential tools for the Data Scientist is the RStudio Server, which supports a developer environment to run R on the server. The open-source Jupyter Notebook is another popular app, comprising statistical modeling, data visualization, machine learning functions, and much more. 

• Nurture Practical Skills 

Once you learn the basics of programming languages and the digital tools that Data Scientists use, you can start using them, practicing the skills you just learned, and building them further. 

Try taking on projects that use a broad set of skills: using Excel and SQL to manage and query databases, and Python and R to analyze data using statistical techniques, building models that parse behaviors and provide new insights, and using statistical analysis to make predictions about unknowns. 

You could even practice presenting your findings to others in order to enhance your communication skills. You will also want to practice working with various types of data: text, structured data, images, audio, even videos. Each industry uses their own types of data to help leaders make better, more informed decisions. 

• Develop Visualizations & Practice Them     

Using programs such as Tableau, PowerBI, Bokeh, Plotly, or Infogram, practice building your visualizations from scratch, finding the best ways to allow data to speak for itself. 

Excel comes in handy during this phase as well: while the core premise behind spreadsheets is simple–make calculations or charts by connecting information within its cells–Excel remains incredibly useful over 30-plus years later, and is virtually indispensable in data science. But creating pretty visualizations is only the start. As a data scientist, you also have to be able to present your findings to live audiences using those visualizations. 

These communication skills might be natural for you, but rest assured, everyone can get better at them through practice. Start small, if needed — give presentations to one friend, or even to your pets — before moving to group settings. 

• Build an Effective Data Science Portfolio 

Once you have done the initial research, received training, and put those new skills to work building a impressive array of projects, the next step is demonstrating these skills through developing the polished portfolio thatll get you your dream job. 

In fact, your portfolio could be the single most important component to your job search. NIT DATA’s data science course, for instance, is designed to provide project-based experiences, helping students develop impressive portfolios consisting of completed real-world projects. It is one of the best ways to distinguish yourself on the job market.  

NIT DATA is renowned in facilitating tech aspirants with the industry-approved data science, cloud, RPA SAP training course in Hyderabad, plus job prep and placement support.

• Raise your Profile

A well-executed project you complete independently can be a great way to show your skills and impress prospective hiring managers. 

Pick something that really interests you, ask a question about it, and then attempt to answer the question with data. 

Document your journey and present the results – in beautiful visuals – with clear explanations of your process, emphasizing your technical skills and creativity. Your data must be accompanied by a compelling narrative demonstrating the problems you solved — emphasizing your processes and creative steps taken — so the employer can understand your value.

• Apply to Data Science-related Jobs     

There are a lot of roles in the data science space. After picking up essential skills, people usually move to specialize in different subfields, like data engineers, data analysts, or machine learning engineers, among several others. 

The Takeaway

Find out what a company is prioritizing, what they are working on, and make sure that matches your strengths, goals, and what you envision doing in the future. 

Career in data science is a great option that is intriguing as well as rewarding. The demand for data science jobs are just going to explode in the coming decade.

However, it is a challenging role as well, you might be able to get into it in short & also for long-term success, you have to really build a strong foundation in this futuristic domain. All the best.