There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days.
This is the age of data. And in this age, the Data Scientists are gods!!! They are the ones which extremely diverse skill sets ranging from Data Management to Machine Learning. These multi-talented magicians are chiefly responsible for converting the data into actionable insights by using self-created predictive models and custom analysis according to company requirements.
In other words, being a Data Scientist is an extremely important job in the current data age. So much so that a Harvard Business Review
article even called it the “Sexiest Job of the 21st Century”
(And that’s incentive right there to become one!!). And it also doesn’t hurt that being a Data Scientist pays really well with an average salary of 1,022K
per year. And that is the reason this article is a complete guide to becoming a Data Scientist in 2019. This is a roadmap that you can follow if you are interested in learning more about Data Science.
But there is still a lot of confusion between the differences in the role of a Data Analyst and Data Scientist so we’ll start our article with that and move on to other topics like the Education Requirements and Skill Requirements to Become a Data Scientist.
Difference Between a Data Analyst and a Data Scientist
It’s obvious that both a Data Analyst and Data Scientist have a job description related to data. But what?!! That’s a question that many people have regarding the differences between a Data Analyst and Data Scientist. So let’s clear this doubt here!
A Data Analyst
uses the data to solve various problems and obtain actionable insights for the company. This is done by using various tools on well-defined data sets to answer corporate questions like “Why is a marketing campaign more effective in certain regions” or “Why have product sales reduced in the current quarter” and so on. For this, the basic skills that a Data Analyst possesses are Data Mining, R, SQL, Statistical Analysis, Data Analysis
, etc. In fact, many Data Analysts gain the extra skills required and become Data Scientists.
A Data Scientist
, on the other hand, can design new processes and algorithms for data modeling, create predictive models and perform custom analysis on the data according to company requirements. So the main difference is that a Data Scientist can utilize heavy coding to designing data modeling processes rather than using the pre-existing ones to obtain answers from the data like a Data Analyst. For this, the basic skills that a Data Scientist possesses are Data Mining, R, SQL, Machine Learning, Hadoop, Statistical Analysis, Data Analysis, OOPS
, etc. So the reason that Data Scientists are more heavily paid than Data Analysts is their high skill levels coupled with high demand and low supply!
Education Requirements to Become a Data Scientist
There are many paths to reach your goal as a Data Scientist and you can follow any of them! But keep in mind that most of these paths pass through a college as a four-year bachelor’s degree is the minimum requirement (Masters and Ph.D. certainly don’t hurt!!!)
The most direct path is that you complete a Bachelor’s degree in Data Science as that will obviously teach you the skills required to collect, analyze and interpret large amounts of data. You will learn all about statistics, analysis techniques, programming languages
, etc. that will only help in your job as a Data Scientist.
Another roundabout path you can take is to complete any technical degree that will help in your role as a Data Scientist. Some of these are Computer Science, Statistics, Mathematics, Economics, etc. After completing your degrees you will have skills such as coding, data handling, quantitative problem solving
, etc. that can be applied to Data Science. Then you can either find an entry-level job or complete a Masters and Ph.D. for more specialized knowledge.
Skill Requirements to Become a Data Scientist
Every Data Scientist Ninja must have their tools! And so there are multiple skills that are required for a Data Scientist ranging across different fields. Most of them are mentioned below:
1. Statistical Analysis:
As a Data Scientist, your primary job is to collect, analyze and interpret large amounts of data and produce actionable insights for a company. So obviously Statistical Analysis is a big part of the job description!!!
That means you should be familiar with at least the basics of Statistical Analysis including statistical tests, distributions, linear regression, probability theory, maximum likelihood estimators, etc.
And that’s not enough! While it is important to understand which statistical techniques are a valid approach for a given data problem, it is even more important to understand which ones aren’t. Also, there are many analytical tools that are immensely helpful in Statistical Analysis as a Data Scientist. The most popular of these are SAS
, etc. So it’s important that you have a thorough knowledge of them.
2. Programming Skills:
Programming Skills are a necessary tool in your arsenal as a Data Scientist! That’s because it is much easier to study and understand data in order to draw useful conclusions if you can use certain algorithms according to your needs.
In general, Python
are the most commonly used languages for this purpose. Python is used because of its capacity for statistical analysis and its easy readability. Python also has various packages for machine learning, data visualization, data analysis, etc. (like Scikitlearn) that make it suited for data science. R also makes it very easy to solve almost any problem in Data Science with the help of packages like e1071, rpart, etc.
3. Machine Learning:
If you are in any way connected to the tech industry, chances are you have heard of Machine Learning
! It basically enables machines to learn a task from experience without programming them specifically. This is done by training the machines using various machine learning models using the data and different algorithms.
So you need to be familiar with Supervised and Unsupervised Learning algorithms in Machine Learning like Linear Regression, Logistic Regression, K-means Clustering, Decision Tree, K Nearest Neighbor,
etc. Luckily, most of the Machine Learning algorithms can be implemented using R or Python libraries (mentioned above!) so you don’t need to be an expert on them. What you need expertise on is the ability to understand which algorithm is required based on the type of data you have and the task you are trying to automate.
4. Data Management and Data Wrangling:
Data plays a big part in the life of a Data Scientist (Obviously!). So you need to be proficient in Data Management which involves Data Extraction, Transformation, and Loading. This means that you have to extract the data from various sources, then transform it in the required format for analysis and finally load it into a data warehouse. To handle this data, there are various frameworks available like Hadoop
Now that you are done with the process of Data Management, you also need to be familiar with Data Wrangling. Now, what is Data Wrangling you ask? Well, it basically means that the data in the warehouse needs to be cleaned and unified in a coherent manner before it can be analyzed to obtain any actionable insights.
5. Data Intuition:
Don’t underestimate the power of Data Intuition! In fact, it is the primary non-technical skill that sets a Data Scientist apart from a Data Analyst. Data Intuition basically involves finding patterns in the data where there are none! This is almost like finding the needle in the haystack which is the actual potential in the huge unexplored pile of data.
Data Intuition is not a skill that you can be easily taught. Rather it comes from experience and continued practice
. And this, in turn, makes you much more efficient and valuable in your role as a Data Scientist.
6. Communication Skills:
You must be great at Communication Skills as well in order to become an expert Data Scientist! That’s because while you understand the data better than anyone else, you need to translate your data findings into quantified insights for a non-technical team to aide in the decision making.
This can also involve data storytelling
! So you should be able to present your data in a storytelling format with concrete results and values so that other people can understand what you are saying. That’s because eventually, the data analysis is less important than the actionable insights that can be obtained from the data which will, in turn, lead to business growth.
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