- Questions You Should Ask
- Maths and Stats
- Machine Learning
- Communication and Visualization
- Data Munging
- Know the Trends
- Undergraduate Degree
- Consider a Specialization
- Create Your Resume
- Get the first Entry-level job/Internship
- Additional Data Science Certification
The world is moving towards complete digitisation and is expected to generate copious amounts of data in the future. However, to make sense of this data we need specialists who can read, model and organise data in coherent detail. Data science has emerged as an effective means of handling data to extract meaning out of random numbers and figures.
Clearly, Harvard Business Review wasn’t bluffing when they suggested data scientists to have the “sexiest job of the 21st century” – not only is it one of the most crucial profiles in the market today but is also among the highest paid ones. If you are wondering what it takes to become one, we have laid down the steps to become a data scientist.
Step 1: Questions You Should Ask
A career in data science requires constant learning and upskilling and it cannot be an impulsive decision. If you are planning a long sprint in this direction, make sure you have a suitable background and aptitude. Start by asking yourself the following questions to find out if this path is for you.
– Do you have an educational background in computer science, information technology, mathematics, statistics or a similar branch of study?
– Do programming languages excite you?
– Are you a proactive learner who is willing to pick up the tricks of the trade ahead of the market?
– Do you enjoy handling complex data sets to understand patterns?
Data science might be a rewarding career choice but it requires concerted efforts. A course in data science can help you master the essentials and make you industry-ready.
Step 2: Maths and Stats
If you are from a non-technical background and still want to pursue a career in data science, fret not. You can up your chances of becoming a data scientist by developing skills in the field of applied mathematics and statistics. Market research shows that a considerable number of data scientists hail from a business or economics background. If you are an aspiring candidate with a similar educational background, brush up your skills in mathematics and statistics as a preparatory step.
- Machine Learning
- Statistical Modelling
- Exploratory Data Analysis
- Regression Analysis
Step 3: Machine Learning
Master the basics of machine learning as it is one of the most crucial components of data science. It is used for a number of data science applications, ranging from reporting forecasts to identifying data modelling patterns. Familiarity with machine learning tools and techniques will help you to master other data science tools with ease. Once you pick up the basic machine learning tools and functionalities, designing and using algorithms for data modelling will become easier.
Step 4: Programming
Programming is one of the main requirements in a data science profile. Learn to code so that you can read and analyse data sets. Pick up programming languages like Python, R, SAS and more. Python remains one of the most widely used programming languages owing to its flexibility. Among the querying languages, SQL is prominent, so learning both these programming languages will help you launch your data science careers successfully.
Step 5: Communication and Visualization
- Storytelling skills
- Convert data-based insights into decisions
- Matplotlib, Tableau, Qlik Sense, Power BI
Step 6: Data Munging
The next step to become a data scientist should be learning data munging. It is a process of looking through messy data sets to identify and discard redundant data. This cleanup process is a preparatory step towards data analysis. Data munging helps data scientists to analyse and present data in a readable format.
Step 7: Reporting
For a data scientist, if data analysis is half of the job, the other half is reporting. Business decision-makers refer to data reports to drive business and generate revenue. But for the data to make sense, it must be put into data visualisation tools like charts, Tableau, d3.js, Raw and more. Data scientists must familiarise themselves with the principles of data communication systems and visual encoding to present data in an easy and readable format.
Step 8: Practice
The best way to fine-tune your skills in data science is by applying that knowledge to practice. Once you have mastered all the theoretical knowledge, start working on projects that replicate real-world data complexities faced by companies. Alternatively, you can also intern at leading data science companies or join bootcamps to get hands-on experience on real data science applications.
Step 9: Know the Trends
Stay updated on the recent developments in the field of data science. The amount of data generated by the world is increasing each day and in keeping with this exponential growth, data science is also evolving. Data scientists must learn ways of enhancing data tracking and analysing applications to ensure resource optimisation. Constant learning is crucial for data scientists to stay on top of their game. Look for educational and professional development opportunities that will advance your career in data science.
Step 10: To pursue an undergraduate degree in data science or a closely related field
Contributed by: Debashis Gogoi
LinkedIn Profile: https://www.linkedin.com/in/debashis-gogoi/
One will need at least a bachelor’s degree in data science, mathematics, statistics, computer science to get an opportunity as an entry level data scientist. Degrees also add structure, internships, networking and recognized academic qualifications for one’s resume. However, if one has already received a bachelor’s degree in a different field, you may need to focus on developing skills needed for the job through online short courses or bootcamps.
Gain the required skills to become a data scientist.
- Statistical Analysis and Math
- Programming (Python, R)
- SQL (MySQL)
- Machine Learning Techniques
- Data Visualization
- Communication Skills
- Data Mining, Cleaning and Munging
- Data Warehousing and Structures
Step 11: Consider a specialization
In demand data scientists typically specialize in a particular industry or develop strong skills in areas such as artificial intelligence, machine learning, research or database management and then with significant experience and expertise their designation changes in the same way as for example Machine Learning Engineer, Artificial Intelligence Engineer, Computer Vision Engineer, Data Analyst, Senior Data Analyst, Data Scientist, Senior Data Scientist, Data Engineer etc. Specialization is a good way to increase one’s earning potential and do work that is meaningful to the industry and the domain.
Step 12: Create Your Resume
Once you have completed your education in data science and gathered experience working on projects and as interns, it’s time to create a portfolio showcasing the same. Update your resume, highlighting your data science skills adequately and start applying for relevant openings. You can prepare for interviews by referring to the most popular data science questions and answers.
Step 13: Get the first entry-level data science job or an internship
Once one has acquired the right skills and/or specialization, one should be ready for the first data science role! It may be useful to create an online portfolio to display a few projects and showcase the accomplishments to potential employers. One also may want to consider a company where there’s room for growth since the first data science job may not have the title data scientist, but could be more of an analytical role. One will quickly learn how to work on a team and best practices that will prepare for more senior positions.
A very important point to note here, one should also accept an internship rather than only looking for full time jobs. The objective here is to get exposure to the industry and working in real life projects on real life data. So, one should accept every opportunity one gets to prove himself/herself and to showcase one’s skills y contributing towards the industry.
Step 14: Look for additional data science certifications and post-graduate learning
There are numerous institutes having N number of courses and training on data science skills and tools. As it is said ‘Never Stop Learning because Life Never Stops Teaching’, one should always keep on learning new skills and tools for one’s own development and then later using the same for the betterment of mankind and the world.
Josh Wills said – “A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician”.
A data scientist’s responsibilities on any given day may include:
- Solving business problems through undirected research.
- Extract huge volumes of structured and unstructured data. They query structured data from relational databases using programming languages such as SQL. They gather unstructured data through web scraping, APIs, and surveys.
- Employ sophisticated analytical methods, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling.
- Thoroughly clean data to discard irrelevant information and prepare the data for preprocessing and modeling.
- Perform exploratory data analysis (EDA) to determine how to handle missing data and to look for trends and/or opportunities.
- Discovering new algorithms to solve problems and build programs to automate repetitive work.
- Communicate predictions and findings to management and IT departments through effective data visualizations and reports.
- Recommend cost-effective changes to existing procedures and strategies.
Different companies will have a different take on the work a data scientist does. Some treat their data scientists as data analysts or combine their duties with data engineers, others need top level analytics experts skilled in intense machine learning and data visualizations.
As data scientists achieve new levels of experience or change jobs, their responsibilities invariably change. For example, a person working alone in a mid-size company may spend a good portion of the day in data cleaning and munging. A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products. Based on the company, the industry and the domain, the work done by a data science professional may vary on a daily basis.
After you have followed these aforementioned steps, your data science career will be all set to take off. With an arsenal full of data science skills, landing a relevant role won’t be difficult, especially if you have worked on projects and have industry-relevant experience. However, in order to keep growing in the field, you must constantly seek challenges and keep learning. Start viewing all kinds of business circumstances as scopes for studying data – start thinking like a data scientist. Courses and certifications will help you stay updated about the latest technologies in the field and give you an edge over your competitions. Great Learning, one of India’s premier education institutes offers courses that cover all the essentials of data science and make professionals industry-ready. Check out a data science program to get a better understanding of the curriculum.
If you found our steps to become a data scientist helpful and enlightening, check out the online course, PG program in Data Science and Business Analytics, to learn seamlessly with the comfort of your own place and time.