How to start career in Data Science as a fresher

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Lately, data-based jobs have been growing considerably, encouraging young people to educate themselves and acquire the requisite skills for getting jobs in different fields such as business intelligence, logistics, machine learning, data science and data architecture. Over the years, data science has emerged as a core job that has seen visible growth and offered career opportunities to different professionals who are skilled in analytics, coding, statistical analysis, math and data visualization. Looking at the tremendous growth of data science, these days many professionals are seeking to step into this field right from the start by pursuing a Data Science Course with Placement Guarantee. Although this is an essential step for building a career in Data Science, there are other essential steps one needs to take to start a career in data science as a fresher.

STEP 1: Determine What Needs to be Learned

As part of the first step towards your data science journey, you need to figure out what all you are required to learn for establishing a strong career in data science. Many of you may think that you will be expected to have knowledge of advanced mathematics or deep learning to be eligible for a career in data science. However, that is not the case. To kickstart your data science career, all you need is the basic knowledge of a programming language and basic mathematical knowledge. Data Science comprises various disciplines such as mathematics and statistics, computer science, data management, data analysis, AI etc. Therefore knowledge of linear algebra, probability, calculus, measures of central tendency, standard deviation, measures of dispersion, statistical tests, etc, can be highly beneficial. Furthermore, one can learn about data analysis tools like Google Data Studio, Looker, Tableau and data analysis tools such as Datawrapper, Microsoft Power BI, Grafana, Google Charts and Chartist. js,, Datawrapper, FusionCharts, MS Excel, Infogram, Plotly etc can give you an added advantage. 

STEP 2: Get hold of Programming Languages

To be a professional data scientist, one needs to be proficient in at least one of the programming languages. The most extensively used programming language among all is Python followed by Java, JavaScript, SQL, C/C++, Scala, MATLAB etc. Python is considered as the most preferred one as it’s an open source programming language having inbuilt libraries such as NumPy, Matplotlib, Pandas, TensorFlow, Scikit-Learn, PyTorch that help in data management and processing task.

STEP 3: Build Online Professional Portfolio

To grab the attention of employers and gain their confidence, it is essential to build a strong professional portfolio that can reflect your past accomplishments and experience of working with Data Science. In absence of any prior experience, it is always better to develop your own projects and work on them using all the data science tools you have learned. For building a strong professional portfolio, it would be great if you work on a minimum of five data science projects that can help you strengthen your knowledge on data preparation, data munging, data cleaning etc. In addition to this, you can mention internships you would have taken to learn deeper about data science.

STEP 4: Learn Continuously and Take a Data Science Certification

For becoming a proficient data scientist, you need to ensure that you never stop learning. With passage of days, you just need to keep upscaling yourself as per the present market demands. One of the best ways to do this is to look for a data science course that has a comprehensive syllabus including knowledge on all the important and latest concepts of data science and also offers students to get hands-on experience of data science through projects. Additionally, you can go through blogs as well as academic articles for enhancing your technical skills and expanding your innovative capacity.

STEP 5: Expand your Professional Network

DataA Data Science career for freshers requires budding data science professionals to join relevant community pages and visit them regularly. This can be quite beneficial as it allows you to interact with like minded data science enthusiasts, exchange knowledge and learn about the latest trends and developments. Some of the popular and beneficial data science communities are IBM Data Science community, Reddit, Kaggle, Data Science central, etc.

STEP 6: Develop an Impressive Resume

For building a data science career for freshers, it is highly recommended that prospective data science professionals work hard on building a winning resume. One can do this by highlighting important keywords, mentioning past experience of working on data science projects or any other relevant experience.

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