Important Aspects to Starting the Career in Data Science
Data science is the on-demand technology that creates massive career opportunities around the world because of the huge explosion of data used by global users. There is a big skill gap for the data scientists and related job roles in the market for managing that abundance of data in a proper way. Many myths are there to proclaim the data science is a complicated one for a better career. In this blog, we provide clear insights along with the common reasons and mistakes behind the failures in data science career and solutions to find a better way for a bright future.
We have formulated nine basic features that help students to get into the tremendous world of data science without wasting time, money, energy, and motivation and these features categorized under three main factors as follows
When Applying for a Job
During a job interview
While Learning Data Science
While Learning Data Science
Following are the main aspects under the tenure of a learning period to save time and money:
Spending more time in learning it by practical experience
Learning too much theoretical concepts in order to understand mathematics-related concepts like linear algebra and statistics, and machine learning concepts like algorithms, derivations, etc. without implementing it in practical is simply a waste of time. Because Data Science is, an applied field and skills are upgrading only by practical experiences. To avoid these mistakes, enroll in Certified Training Center in Chennai, which gives more hands-on practical insights while studying in academically and learn to handle issues in the big picture by partitioning in small pieces.
Start with less algorithms for coding
When start the career, one really does not need much algorithms to code in one scratch. If they practice in that way, it became the major commodity for future projects. There are many machine-learning libraries such as Scikit-Learn (Python), Caret (R) and cloud based solutions to make the coding work simple one. In addition, it is more important to know how to apply a proper algorithm in the right place.
Begin from the fundamentals instead of jumping into the deep end. The desire for entering this field to do build technology with Deep Learning and Natural Language Processing for Driving Cars with Automation, Advanced Robotics, and Computer Vision could be the best choice. However, it is very much important to be master in fundamental things like classical machine learning algorithms, and systematic approach to design machine-learning projects.
When applying for a job
The next step is to find the best opportunities in job searching process. Maximize the opportunity for an employer to select you from the crowd by following the below suggestions
Avoid presenting too much technical jargon in a resume
Do not suffocate your resume by giving many technical stuff, which makes a recruiter feel unnecessary of it. Write the resume to the point and highlight your skill with bulletin marks. It should be more concise, especially in the time of entry-level positions.
Some tips to write the eye-catching resume
Do not list out the programming languages whatever you have studies as theoretical alone. Moreover, describe it with explaining the results.
To avoid distraction for a recruiter, emphasize the important skills and give the space to outshine it.
Use a standard version for creating a resume so that alignment may not change when read by a recruiter.
Do not overestimate the academic valued degrees
Try to avoid overestimating the graduated degree, as it is usual factor of today’s world. Because academic degree taught mostly in theoretical while, data science field is complete opposite to it and it is important to focus on working with deadlines, client management, and technical roadblocks. Rather supplement it with course work from recognized training institutes with real time project access, highlight the internships even at part-time, and connect with local data scientists in LinkedIn.
Avoid Searching Too Narrowly
Do not search only “Data Scientist” job as an organizations are in need of many new fields with smaller role as per the candidates experience and skill set.
Search job by required skill like Machine Learning, Data Visualization, SQL, etc.
Search by job responsibilities as predictive modelling, Data Analytics, and testing, etc.
Search by trending technologies like Python, Scikit-Learn, R, Keras, etc.
Search by Job titles as Data Analyst, Machine Learning Engineer, and Quantitative Analyst, etc.
During the Interview
There are some suggestions to present yourself unique during the interview given as follows:
Prepare well as to discuss deeply about the trending projects
Always be ready to answer the “how would you” type of questions raised by the recruiters. Point out the necessary examples to handle the situation instead of giving a hypothesis on it. One must understand clearly that the whole workflow of data science projects. Organize the methodology before presenting it, review insights from the past projects and internships, are the added advantage for the candidate to outshine your skill in the crowd.
Prepare as per the need of applied industry
Do not underestimate the value of your well-known domain knowledge when facing the interview.
For a banking-related position, highlight the basic finance-oriented concepts of your domain.
For a strategy, related jobs, practice with case interviews and ways for making profitability through your domain
For start-ups, prepare more for marketing and ways for gaining competitive edge using your domain.
Improved Communication Skill
In order to communicate with the senior engineers, data scientists of your team, you should have a strong communication skill to work comfortably in cross-functional environments. Maximize your skill by learning analytical terms with various mathematical and technical backgrounds. In addition, learn to present technical stuff to non-technical clients by easily understandable language preferences and make sure you are giving productive information. Precise with delivering the answer to the point. Analyze datasets, key insights, and highlight the findings are the best way to show yourself unique in the interview. These are all noticeable more by interviewers during the interview.
Conclusion:
Even the Data Science field is a difficult one, there are many ways to overcome and succeed yourself and make yourself shine in the crowd. Follow the above-mentioned suggestions that we find helpful and easy to begin a career in data science. Finally, if you are waiting for the right time to seize the opportunity, the time is now. Contact SLA to know more about the Best Data Science Training in Chennai and get the benefit of the features provided by us.