Did you know that the unemployment rate for data scientists is negative! That is because there are more data science jobs than qualified data scientists in the job market.
Data Science has been considered one of the most lucrative areas in the technology industry in recent years. Harvard Business Review had gone one-step ahead in 2012 and called ‘data scientist’ as one of “the sexiest jobs in the 21st century.” Looking ahead at the future of data science, one can applaud the foresight of that tagline.
We are in the middle of the 4th Industrial Revolution (Industry 4.0), which is mostly being driven by Data Science. This ever-growing field involves the collection, analysis, and exchange of data, lots of data! The demand for professionals for various data science jobs is at an all-time high, so is the gap in supply. BLS suggests that professionals with backgrounds in biology, finance, and computer science will have incredibly powerful leverage to grow in a career in data science.
Data science is not an easy field, and you need to be very persistent to be successful in this field. Avoid thinking of Data Science as magic, where all problems can be solved using data-driven methodologies. Be aware of what data science jobs entail; don’t be swayed by what you see or read. So to help you answer your question – How to become a data scientist – let’s focus on the basics first, and know what is the entire fuss about data science!
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What is Data Science?
Data science is a multidisciplinary field where a blend of data inference, algorithm development, and technology is used to solve analytical problems or provide insights on data. Data science can be generally divided into two groups:
Data insights – One of the main aspects of data science is to uncover findings from the gathered data. Data is as important as the person who is dealing with it. One must know how to dive into the data and find as much information from it in order to be useful.
Data products – It can be anything from software to any technical asset, which processes the data to provide meaningful information, helping form informed business decisions. Data scientists play an important role in building these data products and providing a platform where one can easily analyze the right data to get the right output.
To learn more about data science, read our blog on – What is data science?
How to become a data scientist?
One does not become a data scientist overnight. It takes a lot of learning, experience, and understanding of the concepts. Before you travel on your career journey to become a data scientist, there are some questions that you need to ask yourself:
Do you love numbers, figures, and graphs? Data science is all about numbers and figures. If you don’t like playing with numbers, you will find it frustrating later in your career.
Can you program without difficulties? Data scientists should be comfortable with various programming languages like R, Python, etc.
Are you willing to learn and start at an entry-level before reaching a proper data scientist role? If you are someone who has no experience in data analysis, you may have to start in an entry-level position.
When you have a positive answer to the above questions, you can go ahead and start acquiring the skills associated with data science. Let me share some infallible tips that can help you make a career in data science.
1. Make Sure Data Science is for You
As mentioned above, data science is no play, and you must be very persistent to be successful in this field. Bear in mind that you can’t solve all the data-related issues with just a magic wand. Data science is a deep and vast domain and requires extensive knowledge and a specific skill set to excel. Understand different job roles in the field. A data analyst is different from a data architect, while a data scientist’s role is different from that of a data engineer.
To know more about the job profile and responsibilities of a Data Scientist, refer to this article on What is Data Scientist?
2. Build Your Own Projects & Portfolio
Employers won’t pay you to do something you’ve never done before. If you are currently working, try to apply everything you are learning in your work. On the contrary, if you are not working, start developing your own projects that involve all the new data science tools you know.
You should have around five projects that are relevant to the job role of a data scientist, such as data cleaning projects that include data preparation, data, munging, and data cleaning, data storytelling, and visualization project, group projects, etc.
Also Read>> Data Science vs. Big Data vs. Data Analytics
3. Showcase Your Projects and Their Results
Start displaying the value your projects added to your workplace. If you are not employed anywhere, showcase your projects and their impact on a personal blog, GitHub, a video on YouTube, Twitter, or any other digital medium that helps you to make your skills go viral. Show your bosses and acquaintances what you have learned and strikingly communicate your results through graphs and data visualizations.
4. Gain Some Professional Experience
Before starting a career in data science, you must gain relevant experience, as data science jobs require a high level of mathematical and statistical knowledge and maturity. You would be able to effectively apply these sophisticated techniques only after you have gained relevant work experience.
5. Develop a Professional Online Presence
Update your LinkedIn profile, share your projects, engage with similar profiles to maintain an excellent online presence or upload tutorials to YouTube. You can also write blogs sharing your experiences and give your opinion on current issues in the field of data analysis (for example, new technologies, new algorithms, etc.).
6. Master the Skills
You need to acquire the right knowledge of data analytics tools, technologies, and programming languages to practice data science skillfully. A 2018 Kaggle study reveals that Python, SQL, and R are the most popular programming languages that a skilled data scientist must learn. Apart from these, you must have an impeccable command over –
- SQL and NoSQL databases
- Relational algebra
- Parallel databases
- Parallel query processing
- SQL (Structured Query Language)
- Data Visualization
- Machine learning
- Statistical modelling
- Experimental design
- Bayesian inference
- Decision trees
- Data optimization
- High-quality visual representation
- R packages domain like ggplot2
- Ability to modulate the data into actionable decisions
- Graphical display tools
Also Read>>Skills That Employers Look For In a Data Scientist
As a data scientist, you should have impeccable analytical skills, software management, appropriate communication strategies, and new measurement theories and applications, with the aim to –
- Perform data analysis with maximum reliability
- Anticipate difficulties that may be encountered in the process
- Select advanced data science tools to meet these challenges
- Provide maximum value with the interpretation of results
Also Read>> Top Data Science Interview Questions & Answers
7. Learn Data Visualization
Understand the basics of good data visualization and reporting. You don’t need to become a good graphic designer, but you need to know how to create data reports to present to a layman. Some helpful resources to learn data visualization are –
- Data Visualization and Communication with Tableau by Duke University on Coursera
- The Complete Tableau Bootcamp for Data Visualization by Udemy
- Microsoft Excel – Data Visualization, Excel Charts & Graphs by Udemy
8. Update your knowledge, pick up a relevant course
Never stop learning. Even if you find your first job as a data scientist, never stop upskilling yourself. Read blogs and academic articles to improve your technical skills and your innovative capacity. Don’t forget always to keep a balance in your professional skills. Here are some skill-based courses that can help you in sharpening your existing skills.
Basic Data Science
- The Data Scientist’s Toolbox by Johns Hopkins University on Coursera
- Data Science: Linear Regression by Harvard University on edX
- Linear Regression and Modeling by Duke University on Coursera
- Data Science Professional Certificate by IBM on Coursera
- Programming for Data Science with Python by Udacity
- Python Data Structures by the University of Michigan on Coursera
- Applied Data Science with Python Specialization by University of Michigan
- Programming for Everybody (Getting Started with Python) by the University of Michigan on Coursera
- Statistics with Python Specialization by the University of Michigan on Coursera
- Machine Learning by Stanford University on Coursera
- Machine Learning for Data Science and Analytics by Columbia University on edx
- Mastering Data Science and Machine Learning Fundamentals by Udemy
- Machine Learning Specialization by The University of Washington on Coursera
Must Read – What is Machine Learning?
- AI For Everyone by IBM on Coursera
- Welcome to Artificial Intelligence by Udemy
- Expand Your Knowledge of Artificial Intelligence by Udacity
- Intro to Artificial Intelligence by Georgia Tech Masters on Udacity
Must Read – What is Artificial Intelligence?
How will certification or professional training in data science help?
A majority of data scientists are from science, math, or statistics background, having a master’s degree or a Ph.D. However, to think that a master’s degree or a Ph.D. is mandatory is a misconception. One can become a data scientist without having a master’s degree or a Ph.D.
Professional training always helps and you can enjoy many benefits from it. It is great to have the above-mentioned skills. However, if you are someone who has no experience or have just started your career in data science, a certification can surely help you. It will help you to get better job prospects and improve your salary.
9. Stay up-to-date with the Data Scientist community
You should have saved some of the Data Scientist community pages to your bookmarks, keep visiting these websites to find out what’s going on, and you can also get information on the type of data science jobs they offer.
So the question would arise, how do you practice data science before you have a job in the field?
Well, you can practice on projects from open-source data, participate in contests like HackerEarth and HackerRank, etc., volunteer or intern. The best data scientists will have project experience and an unseen benefit that will give them a better chance of being hired by companies.
11. Acquire the desired non-technical skills
Communication skills – Data scientists should have good communication skills to translate their technical findings to other non-technical teams or stakeholders. This way you can help non-technical teams like marketing and sales to understand your findings, with the help of which they can make informed business decisions.
Business understanding – Data scientists should have a good understanding of the business to find the right information from the gathered data. Also, their data wrangling and interpretation activities should be aligned with the business goals of an organization.
To learn more about Big Data, read our blog – What is Big Data?
12. Learn the tools of the trade
A data scientist is nothing without his/her tools. There are a lot of tools and software which has been developed over the years to help professionals build machine learning models or analyze data.
It’s important to have knowledge of multivariate calculus and linear algebra; which most of you have learned during graduation if you had math, science, or engineering background. For others, you can easily learn them through online educational courses. The other skill that you should have is an understanding of programming languages like R or Python. Python is the most used language of data scientists. It is also advantageous to have knowledge of database querying languages like SQL, Hive, etc.
Career Path of Data Scientist
Every expert in the field has a starting point from where he moved up the career ladder. Here is an abstract picture of a career progression of a data scientist (career paths may vary from person to person according to the level of expertise and knowledge):
- Data Scientists – With further experience, you will be the one who is responsible for creating value out of the analyzed data or performing the advanced statistical analysis to provide effective action points for businesses to work on.
Average Salary – ₹ 6.2 LPA
Image – Data Scientist salary range
- Senior Data Scientists – They are the data scientists with years of experience and they handle all the data science projects of the company, providing their valuable insights. They are the ones that usually connect data with the success of a product or service. They are usually VPs and Heads.
Average Salary – ₹ 27 LPA
- Data Analysts – When you start your career in the data science field, you will be working on low-level data extraction from databases, develop basic visualizations or analyze A/B results. This position usually acts as the entry-level into the data science field.
Average Salary – ₹ 5.4 LPA
- Data Engineers/Architects – As you gain experience, you will responsible for developing complex algorithms for further analysis of data or responsible for the development of blueprints for data management systems.
Average Salary – nearly the same as data analysts.
To gain more perspective about data science and to stay updated with the relevant news, these online resources can be of great help –
- Facebook Research
- Datascience 101
- Data Science Central
- Inside Big data
Concluding Remarks –
I hope I have responded to your query – How to become a data scientist! It is vital to keep a tab on the new developments in the field of data science, considering its highly dynamic nature. Every aspect of data science jobs is subjected to grow and evolve over the years. Programming languages, software, tools, and technologies that shape data science will be continually changing and becoming more robust. To stay more market-relevant and have a fruitful career in data science, keep up with the dynamics of the market.