How to start your career in Data Science: An Essential Guide

How To Jump-start your career in Data Science

In This Article We Are Going to Discuss How To Jump-start your career in Data Science: An Essential Guide

Want to start your career in data science? Are all the resources available online daunting? Should I code in Python or R? Where do I get datasets? How many statistics do I need to know? These are the questions that haunt a beginner’s mind. 

A career in data science is truly lucrative and one in high demand. But the journey may seem very tedious. Don’t worry! Fireblaze AI School has created this useful roadmap to make a productive data scientist out of you. It contains essential tips to get your gears rolling as a Data Science beginner.

Before you dive into the steps, let us learn about some requisites of the big data industry –

3. SQL 
   
   You say Data, I say SQL! It’s tough to imagine a company that deals with big data, not have a database server architecture in place. To take full advantage of these data servers, SQL must be a part of any data scientist’s toolbox. SQL is essential to analyze, manage and execute queries on big data.  

4. Machine Learning 
   
   Simply put, machine learning means identifying patterns in data and producing predictive statistical models. This is done in order to make better decisions from existing datasets. But how, you ask? All this using pure statistics and algorithms with minimum human intervention. An organization can single out profitable ventures and also detect unforeseeable risks by building such models. In Python, libraries like sci-kit learn are essential for machine learning exercises.  

5. Art of storytelling 
   
   If all this sounds a bit mundane to you, don’t worry. Data Science also involves being inquisitive. New-age data scientists have perfected their narration and showcasing skills. The combination of good visualizations and storytelling is vital for gaining insights for a business. Even a layman must be able to interpret the story that your insights convey. A data scientist may be able to identify patterns and anomalies in data. But a good data scientist is able to creatively explain it to you with data visualizations and narratives. Matplotlib, seaborn, Plotly etc. are some excellent visualization packages available in Python. Apart from these, data scientists nowadays also make use of visualization tools like Tableau and Power BI to create attractive dashboards.

Still interested? Now let’s learn some steps you need to take to build your foundation as a prospective data scientist.

Step 1 – Don’t stop learning  

Now, don’t confuse learning with studying. Learning doesn’t mean simply taking up a data science degree. Many successful data scientists have been born out of self-learning. Find something that will motivate you to sharpen your skills regularly. Don’t forget to make it a habit. Following are some vital kick-starts! 

1. Leverage the online resources available 
   
   The demand for data scientists is growing exponentially. It is also in line with the vast number of courses and professional degree programs available on the internet. But don’t go about enrolling in everything you can set your hands on. Take it one step at a time. Remember that established teachers will have their own style of getting the most out of big data. But in this domain, you will have to learn what approach is most comfortable for you. 

2. Attend workshops 
   
   Hands-on workshops are probably the best thing to happen for beginners. The “learn by doing” formula of training is well suited for data science. Participants also get to engage with industry professionals. You get to know the ins and outs of how a day in a Data Scientist’s world looks like. 

3. Don’t bite off more than you can chew 
   
   It is commonly observed that beginners get frazzled by the myriad of tools and languages available at their disposal. But choose one and stick to it. A good place to start is Python. Graph plotting, web scraping, complex machine learning algorithms, character recognition, you name it, Python is the one-stop solution to them all.  

4. Read relevant material
   
   “Never stop learning” is easier said than done. One needs to absorb each and every source of knowledge they can find. Some of the most useful sources of this information are newsletters, blogs and podcasts run by the most established Data Scientists. Such material can help you gain insider information on your domain of interest. They are also handy in job hunting and career opportunities. 

Step 2 – Boost your network 

Once you have gotten a little hang of the domain by following step 1, you can go on to build connections and post something new you learnt habitually. This is in line with our community’s motto, “Your network is your net worth”. Any employer would be excited to see your contributions in the field of data science. Following are some pointers to keep in mind –

• The data science community in India is constantly growing. Competent individuals who have been in the data science game for a long time are helping out newbies to hone data science skills. Accustom yourself to reach out to such mentors.
• If you find yourself only taking up new courses, let me assure you that it is not enough. Put yourself out there by joining peer groups and attending webinars. 
• Ensure that you garner the skills that will help you in your individual career path. Don’t just take up new courses because “everyone is doing it”. Also, remember that skills matter more than that high-end certification you’re eyeing. 
• Share your work, no matter how small. Don’t worry if it’s repetitive. Try taking in all the feedback you can from well-established data scientists. This also helps other beginners like yourself learn new concepts.
• Make GitHub your steady companion and commit all your work. But do it routinely, not once in a blue moon.

Step 3 – Keep Practising 

Want to apply your knowledge in an effective manner? A good approach is to find out the kind of job roles you would like. You can then factor in the responsibilities to build a relevant skillset. The theory is definitely important. But to make yourself suitable in the job market and enhance your portfolio, you need to hone these skills by applying them.

1. Build projects 
   
   By developing your own projects with the datasets available, you learn how to ask the right questions and coming up with solutions of your own. Sometimes, you may not be able to understand why a certain statistical method is being used or why a specific test is being applied to test your model. And the only thing that helps in trying to understand the process is PRACTICE.  

2. Kaggle competitions 
   
   Kaggle is not simply a one-stop for their vast reserve of practice datasets. Kaggle also organizes competitions periodically. It serves as an indispensable reserve to share ideas, connect and engage with data scientists -established ones and amateur ones alike. If competitiveness is what drives you, then be on the lookout for Kaggle’s challenges.

Tips for newbies on How Jump-start your career in Data Science

• Prepare a good resume with essential skills. Statistics, SQL, Machine Learning, EDA are some must-haves! 
• Inculcate the habit of telling a story with data. Improve your data visualisation and communication skills. Generate insights on all the data you can get your hands on.
• Find a big brother in the industry. It is very important to get the right guidance and to understand how your mentor stepped up the ladder in the Data Science domain. Do not hesitate to ask questions and generate feedback. 

Tips for professionals switching their careers 

• Use the industry experience you have gained as your trump card. Whether you’re in the retail domain, or simply a healthcare professional, data scientists can be moulded from any industry. You definitely stand out of the pack if you have relevant domain knowledge and understand the ins and outs of your industry. 
• Brush up on statistics and improve your Excel proficiency. 
• Focus on learning Python rather than R as it has more significance in the job industry.