Let’s first start with the scientist part of the data scientist. A scientist is a professional, who makes discoveries e.g. Newton discovered the gravity theory. Newton was curious to see the apple falling and he wanted to know “why?”. He thought of some possibilities (also known as a hypothesis) as to why this must have happened? He then used physics’ principles to validate the possibilities till he arrived at the real reason – the theory of gravity.
This is what Goldman did in the LinkedIn scenario. This is what Google founders did while developing the Google search engine. They possibly started with the problem –
Therefore, we can say that data science is the process of extracting important insights from the data. We are living in the big data era where data science is becoming an important part of every organization as it helps in processing large volumes of data that is generated from multiple sources. Data science is considered a vast discipline in itself and it consists of specialized skill-sets such as mathematics, statistics, programming, computer science, and so on.
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Data science is all about extracting the data and interpreting it in a more understandable and simple manner to the end-users.
How Can We Improve the Relevance, Context, And Meaning of Search?
One of the hypotheses must have been PageRank based on keyword relevancy, age of the page, links from authoritative sites, etc (nobody knows all the parameters, maybe they will again publish a paper someday). As you all know, they used a combination of statistical models, machine learning, and home-grown tools (the inspiration for Hadoop) to validate their hypothesis. What came forward as a result of this effort – Google. They worked with data- in this case, piles of hyperlinked pages and images over the internet. A data scientist works with data.
Every data scientist need not be A Goldman or Larry Page and Sergey Brin but the approach and the essence of the role remain the same. Data is available in every organization – the size and scale may differ? But who does not want to find a new way to reach out to its customers? Who would not like to generate new ways of generating leads? Who does not want to have better revenue forecasting? If you can use this data effectively to help a business grow, you are a data scientist.
How To Become a Data Scientist In 2021?
To become a data scientist, you can follow the most common and traditional way that most data science aspirants follow. It is simply to earn a bachelor’s degree in computer science, math, business, or any other relevant field.
After getting a bachelor’s degree, you can go on to pursue your master’s in data science or again a relevant field. And most importantly, you should be on the lookout to gain experience in the data science field.
Here is a guide to becoming a data scientist in 2021.
Study Data Science or A Related Field for Your Undergraduate Degree
In order to set your foot in the entry-level position as a data scientist, you can pursue an undergraduate degree in data science or a computer-related field. Getting a degree will help you add structure, internships, and recognized academic qualifications to your resume. However, if you have already done your undergraduate in a completely different field, then in such cases, you will have to focus on gaining skills and knowledge of data science through online courses or boot camps.
Learn The Necessary Skills Needed to Become a Data Scientist
A few of the skills that you will have to focus on are as follows:
- Machine learning techniques
- Data visualization and reporting
- Data mining, cleaning, and munging
- Data warehousing and structures
Opt For a Specialization
Now, there are several specializations that you can opt for as a data scientist and develop skills in areas such as machine learning, research, database management, and artificial intelligence. Opting for specialization is good if you want to grow financially and professionally.
Apply For a Job as A Data Scientist at The Entry Level
After gaining the necessary skills, knowledge, and specialization, you should start looking for entry-level data scientist roles. Getting your first job as a data scientist can help you get the right exposure and practical experience.
Data Scientists Certifications and Post-Graduate Learning
Earning additional data scientists certifications and a postgraduate degree or a diploma can help you get an excellent blend of data science knowledge and practice. Moreover, getting your hands on a few data science credentials can help you add value to your resume and stand out from the crowd. A data scientist with a credential in the data science domain is likely to have higher chances of getting hired over non-certified data scientist professionals.
Here are a few certification options that you can consider:
CAP is a certification offered by the Institute for operations research and the management sciences (INFORMS) and is specially designed for data scientists. Certified Analytics Professional (CAP) is a globally recognized and accepted certification.
Moreover, getting your hands on the CAP certification will allow you to access a broad range of data science resources, knowledge, and great networking opportunities with other CAP holders. This certification has set a standard for professionals in the analytics domain by improving the credibility of the professionals by showcasing that they have the necessary skills and knowledge of the analytical framework.
This certification is offered to SAS enterprise Miner users performing predictive analytics. SAS Certified Predictive Modeler using SAS enterprise miner 14 is globally recognized for adding credibility for the SAS Predictive Modeler knowledge of the certification holder. The certification allows the candidates to get the necessary understanding and mastery of the SAS Enterprise Miner.
Earn A Master’s Degree in Data Science
Earning a master’s degree in data science can add to your academic qualifications, and ultimately increase your skills and knowledge. However, a master’s degree is completely optional, but it can open up good job opportunities as many organizations require data scientists who hold a master’s degree.
Skills Of A Data Scientist
It’s a sea out there. Sometime back, I was trying to do some research on tools for my own understanding, and the more I researched, the more tools came up. so now I have decided to group them together in different categories for better understanding. I will publish that once I get to know all of them, well almost all of them.
Anyways, to give you a broad understanding of the areas where technology plays a role – it starts with data storage, Extraction, and loading of data (ETL/Ingest), DWH, Data mining & analytics, and visualization. There are plenty of tools and technologies available for each of these areas, however, you can’t possibly know all of them. So, as a data scientist, which ones should you know?
A few technical skills that a data scientist must possess are as follows:
- Good understanding of the data extraction and loading, where the data scientist must be familiar with the concepts of data cleansing, data profiling concepts. As far as tools are concerned, one can look at ingest tools like Sqoop. The extracted data can be stored in a DWH (Data warehouse) or in file based systems like HDFS. Basic understanding of these can prove to be handy.
- A good knowledge of concepts of statistical models, machine learning algorithms and some programming languages for Data mining, analyzing and using models to validate hypotheses.
- Python and R are the two of the most powerful languages. You can decide to choose one of these. Also, SQL is important. Data scientists deal with data and it’s not possible without knowing SQL.
Basic Statistical Modeling
An important armor in any data scientist’s arsenal. Statistical models like regression, ANOVA, ANCOVA, etc enable a data scientist to understand the relationship in data sets and use it to develop predictive models. The R programming language, part of the CRAN library, is a way to implement statistical models.
You don’t need to know the intricacies of the statistical models (as most of the Python and R libraries have implemented that). You need to understand how to use these models for solving customer problems.
A data scientist also needs to know about machine learning and how to use it for improving models. Weka in Java, Mahout (part of Hadoop stack), and Prediction API (from Google) are the programming languages to be used for machine learning implementation.
Data scientists are storytellers. They need visualization tools to showcase their solutions. A tool with Dashboard and reporting capabilities can prove to be handy. Tools like Tableau, Power BI, and Qlikview are the most powerful tools.
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In my view, this is probably the most important skill for any data scientist. You can’t be a problem solver, if you are not analytical. You don’t need to be Newton or Albert Einstein though. It’s more of an approach. What about evaluating your analytical skills – Start with this University of Kent Lateral thinking quiz.
To go a notch higher, one of the most well-rounded tests for problem-solving is from McKinsey. The Mckinsey problem-solving tests are available on their website with answers. These tests don’t need you to know statistical techniques or any programming languages.
Last but not the least, Kaggle Competitions provide the most comprehensive Analytics problem-solving opportunities, these are real-life problems and would need you to use almost all the skills of a data scientist.
It is an area and a part of machine learning research that makes use of data for modeling complex obstructions. Deep learning helps data scientists to implement various techniques to conduct business operations in an organization. For instance, a data scientist can easily process large amounts of unstructured and unlabeled data with the help of deep learning techniques.
A data scientist is required to possess pattern recognition skills to recognize the various patterns in data. Moreover, pattern recognition can be used interchangeably with machine learning. A data scientist can easily classify unseen data with the help of pattern recognition as it can identify and predict hidden or untraceable data.
Data preparation is the skill to transform the raw data into a more desirable format that is easy to understand and analyze. A data scientist must possess the data preparation skills as it is the core role of any data scientist in an organization as it helps in finding and correcting the error in the data before it can be sent forward to support accurate business decisions.
A data scientist is required to be a team leader and effectively communicate with business analysts, product managers, and engineers. Leadership qualities are something that is usually expected from a data scientist as they have an important role to play in an organization in terms of collaborating with others and keeping them updated about the business data.
Understanding Data Science with The Help of An Example
I have two accounts on Facebook. One is strictly personal, while the other one had to be created because I wanted to have a company page on Facebook. There is absolutely nothing common except for me (however even my email id and mobile numbers are different in these accounts).
Last week, when I logged into the official account, I was surprised to see some suggestions on the People You May Know section.
The first and third ones were real surprises, how can Facebook figure this out? The first one is my college batchmate (almost 30 yrs back) and we had no connection after we passed out. It was indeed a pleasant surprise. This is not an isolated case, I have discovered many such friends through Facebook
Harvard Business Review article titled “Data Scientist: The Sexiest Job of the 21st Century“, states the following, about LinkedIn in 2006, when Jonathan Goldman joined LinkedIn:
“The company had just under 8 million accounts, and the number was growing quickly as existing members invited their friends and colleagues to join. But users weren’t seeking out connections with the people who were already on the site at the rate executives had expected. Something was apparently missing in the social experience.”
That’s when the idea of suggesting possible network connections (branded as “People you may know” ) came up in Goldman’s mind. Validating and analyzing multiple possibilities, he finally developed the algorithm to everyone’s liking. PYMK was launched, resulting in millions of page views and new connections. LinkedIn’s success has a lot to do with the PYMK feature. To quote the article further:
“Goldman, a Ph.D. in physics from Stanford, was intrigued by the linking he did see going on and by the richness of the user profiles. It all made for messy data and unwieldy analysis, but as he began exploring people’s connections, he started to see possibilities. He began forming theories, testing hunches, and finding patterns that allowed him to predict whose networks a given profile would land in. He could imagine that new features capitalizing on the heuristics he was developing might provide value to users.“
Goldman is a data scientist. The quote above provides the essence of what a data scientist does. Let’s dig deeper and try to understand data scientists a little better.
Let’s formalize the data science roles and responsibilities further.
Roles And Responsibilities of a Data Scientist
A data scientist is responsible for analyzing the business data to derive valuable insights. In simple words, a data scientist helps in solving business problems by making large volumes of data presentable and easy to use for decision making. Here are a few things that a data scientist do:
- Before handling the process of data collection and analysis, a data scientist gains an understanding of the problem and asks relevant questions to the stakeholders or senior management.
- Then the data scientist is responsible for determining the right set of variables and data sets.
- The data scientist then gathers all the raw unstructured or structured data from various sources such as public data, enterprise data, etc.
- After collecting the data, the data scientist transforms and converts the data into a more suitable format, which can be further used for analysis and is easy to read. The transformation process includes cleaning the data, validating the data for completeness and accuracy, among other tasks.
- Once the data is transformed into a usable format, it is then pushed down the analytic system where the data scientists identify trends and patterns in the data.
- The data scientists then try to find viable solutions to business problems.
- Lastly, the data scientists prepare insights and data analysis results to share them with the stakeholders to support the decision-making process.
Data Scientist Salary and Job Growth
According to Forbes and Glassdoor, the demand for data scientists is ever-increasing and by 2026, it is expected to increase by 28 percent. It means that becoming a data scientist can ensure a secure career with a competitive salary range.
The average annual salary of a data scientist as per PayScale is $97,004, which is expected to increase with experience.
Moreover, data science is an exciting career with global job opportunities as every organization irrespective of the industry is looking for more and more data science professionals. Therefore, if you are looking for a secure career with exciting perks and opportunities, then data science is one of the best career options.
The journey to becoming a data scientist can be overwhelming, but it is worth it in the end as the data science professions are rewarded for their skillset with competitive salaries and it also opens new opportunities to work in some of the best companies globally.
Moreover, getting specializations within the data science domain can help you excel in your data science career even further. For instance, getting a specialization in machine learning can help you gain high-level programming skills for creating algorithms that automate the process of gathering data and adjusting their function to increase efficiency.
Frequently Asked Questions (FAQs)
If you opt for an undergraduate in data science, then it will take you 3-4 years to become a data scientist, and if you plan on doing a master’s in data science, then it will take an additional 1-2 years. In total, it can take up to 4-5 years.
Any newcomer or a professional can study data science. However, you will need to take up proper data science learning courses or programs to start your journey as a data scientist.
Data science is a propelling career and can help you gain financial as well as professional growth as a data scientist.
As per Glassdoor, data science is one of the best jobs to maintain a work-life balance. Moreover, the data science roles also have the highest job satisfaction rates. Therefore, we can say that data science may not be a stressful job.
If you are from a science background in 12th, then you can pursue to become a data scientist by enrolling yourself for a diploma data science course or undergraduate degree in data science.
The period of BSc in data science is 3 years. Moreover, if you opt for a data science training program, then it can take up to 6-12 months to complete.
A few data scientist courses that you can opt for are as follows:
1. Introduction to data science- Metis
2. CS109 data science- Harvard
3. Python for data science and machine learning Bootcamp- Udemy
4. Data science MicroMasters- UC San Diego @edX
The main difference between a data scientist and a data engineer is the area they focus on. The data engineers focus more on building the infrastructure and architecture for the generation of data. On the other hand, a data scientist interprets the data and transforms it into a desirable format.