“Data really powers everything that we do.” Jeff Weiner
With the advancement in technology, and path breaking innovations on the internet, there is now more data floating around than ever before. Along with the easy availability of data, there came a need to understand, and analyze this data to get some information that may be useful in improving the performance of an organization.
This need is fulfilled by data science. Data science, a recently coined term, is used to describe a mixture of subjects and their applications in making decisions with data available.
The subjects that are a part of the data science umbrella term are Math, coding, Business Intelligence, Statistics, Probability and many more. In more recent times, it is expanded to include NLP, ML and many more. Data science is the hottest skill out there as it is well paid and the position itself comes with a lot of awe and respect. In this blog, we are going to show you the learning path to becoming a data scientist. Follow this roadmap to figure out what you have to learn and become successful.
Companies are moving towards taking smarter decisions, and these decisions are based on data. The study of data to get some insights, form opinions and arrive at decisions is what data science is all about. Defined as the interdisciplinary study of many subjects, data science, it's processes and its technology makes it possible for us to get information to take decisions.
The data is collected from various sources, cleaned, processed, stored and analyzed. This information is in the form of trends or patterns. Now, it is also a part of data science to project the results such that it is understandable to everyone in the organization through data visualization tools.
The above-mentioned stuff is what a data scientist does. And that's also found in the job description of a data analyst. So what's the difference between a data scientist and a data analyst? Learn key aspects of both roles. Data scientist analysts and many other data roles all need three things that are an absolute must.
They are - Domain, Programming, and Math. But, the level of skill each role requires changes the nature of the job description and salary. As compared to an ML engineer or data analyst, a data scientist must have all three types of skills and should be equally proficient in as many of them as possible.
The data scientist stands apart from other roles because the data scientist tries to work out solutions to issues or problems that are nebulous in nature and there is no clarity on the issue. The job is to find a workable solution to a business problem where there seems to be no clear solution. The job prospects of data science
There is no shortage of data scientist jobs available. What does lack is the number of qualified candidates to fill the vacancies. The candidate with the right skills must also have the ability to display them and be able to communicate well. Quite a few of them have a problem with the skill set that is expected of them as a data scientist.
The easiest way to gain these skills is by attending an online course on data science. You can learn all the basic skills here. And then, you must practice and become an expert with the tech before trying for a job.
I am switching my career! What do I do? As a fresher, you won't have any domain knowledge. But if your career move into data science is a lateral one, then there are two scenarios that may play out. One is you completely change your domain. In this scene, you are equal to a fresher and have to learn all the skills and tools that you need. Additionally, you have to work on your domain knowledge as well.
The second scenario involves you searching for a job in the same industry. In addition to having domain knowledge and business workings of the industry you are working in, you may also have come across some issues. There might even be tools that you have previously worked with. This means that the company need not spend its resources for a long onboarding process and you'll be a successful data scientist.
Data science is a huge field. But not all roles here pay top dollar. The data scientist is one of the best-paid jobs today. The salary of a data scientist in India is Rs. 7,00,000/- per year on average. To get a good salary, you must have all the required skills, a few of the desirable skills are a plus. Also, the salary of a data scientist depends upon the location, employer, experience, and education. One important thing to do while trying to get the job is to negotiate a good salary package.
Learn data science skills-> specialize in one skill.-> Practical experience-> Strong resume-> Portfolio->Strong usage of SM-> Interview
The flowchart explains to you the process of landing a data scientist job. The first thing you need to do is learn as many skills as possible that are hallmarks of a data scientist. Remember that not all skills are needed to be mastered for all data science oriented role. There is a whole unending list of all data science skills.
“Two roads diverged in a yellow wood,
And sorry I could not travel both And be one traveler”
Every data science role and within that, every job profile may vary. The definition of data scientist and their duties and skills required vary from organization to organization. How are you to know what to learn and what not to learn? There need not to anything on the not to learn list, but you may deem it not necessary to learn now. We’ll provide you with a set of metrics with which you can take a decision on what to learn first. Here we go: Choose the industry
Every industry requires a different skill set, different job requirements. Thus, there is no question of generalizing that you want to be a data scientist. This is akin to multitasking with no end in sight. Literally, you’ll end up in the middle of nowhere. Select the industry where you wish to work. If you are looking to make a lateral career move, choosing the industry where you have previously worked will be a value addition in terms of domain knowledge.
If you are new, go through the job description and find something that sounds appealing to you. This will limit the number of skills that you need to learn. Along with the topics, you will get a chance to study up on the domain and gain some knowledge of how the businesses in the industry works. You can do this by going to job sites like LinkedIn or Glassdoor and applying industry filter and job postings.
Choose the type of position you want On the job search websites, search for a few key terms like ML engineer, data scientists, and even a data analyst. Just go through the profile of the job listings and figure out which job title may be the best fit for you. Many times, a data scientist job profile may be found under the other titles. Analyze quantitatively the skills you would need. And, remove the jobs that don’t appeal to you and those with a long list that you won’t be able to master within the time frame you have fixed for yourself.
List out the skills you need The next step is to draw out the common skills among the job listings and make a checklist of the skills. Write down the skills that appear multiple times in a job profile. This will give you a starting point. You can always add skills to this list when you are done with the more common skills.This way, you’ll be able to minimize the list and it will be less daunting to approach learning.
Study Study and work on your skills relentlessly. Get certified as a Crampete Python developer. Gain certifications wherever possible to have a strong resume. Take an offline course on data science that are led by industry experts. This will cover the fundamentals and few other most commonly sought after data science skills. The fundamental skills are an absolute must for every data science position. Some of the widely used tools are used across multiple industries and are a good bet to learn.
Practice First start with coding skills. This will give you maximum benefit a syou can learn other concepts and simultaneously learn to apply those concepts in practice. Get involved in some projects, keep practicing the concepts and applying solutions. Get training data and work to figure out solutions. These will help you find a foothold into the subject in practice.
Write a model resume This step can be done concurrently with other steps once you develop a skill profile. Use this resume to figure out how strong your resume is, how to add value to your resume. Create different resumes for the target positions you had selected in step 2. Work through these resumes to identify your strengths and weakness.
List out the courses you have taken and the skills that you have learnt. Keep updating this periodically. Come back to this part whenever you feel doubts and reassure yourself that you are on the right track and you have been learning useful skills.
Add projects Work on projects where you have to do everything by yourself. Collect, clean and store data. Then retrieve this data and analyze t.Present this data using visualization tools.Meaning, take a data set, formulate questions and try to find answers that can be applied in real-time.
Repeat steps 5-7 for every new skill learnt For every new skill you learn, practice translating it into coding to apply it. Practice till you are comfortable with it. Then try to find a use case where it can be applied in your projects and add skills to your resume. This is a great way to keep track of every skill you learned, not just on paper, but in practice. And now, here are the topics that you will most likely learn and are useful market tools. The list is not exclusive. You can add or delete any skills that you don’t find in your skill profile.
Fundamentals Matrices and algebra- this is a must learn and comes in handy while representing data sets with multiple vectors. The concept is used in various Machine Learning and Deep Learning algorithms.
Big O notation- Learn to classify algorithms as their inputs grow. Classify them depending upon how the space requirements and time taken increases with the inputs.
Probability- This is another fundamental concept that must be mastered by every aspiring data scientist. It is a handy skill in the face of predictive modelling and many other scenarios.
Relational databases- learn relational algebra and SQL for manipulating databases based on SQL.
Sharding- partition data horizontally rather than vertically. Know to scale vertically and horizontally. OLAP- answer multi-dimensional queries with online analytical processing.
ETL- Know to perform extract, transform and load operations. Extract- get data from multiple sources and validate the data; Transform- apply functions to data and transform it into the format accepted by the end system. Clean the data to remove noise and redundancies; Load- Know to load the data that has been transformed into the end system
Version control system- This is considered a fundamental skill. Data scientists work in teams and work on the same data and often build upon each other’s work. Learning VCS like Git, Github is a must. The fundamentals are very important and there is no data science without them. You need to learn the subjects like math and statistics in detail, but only the portions where it is necessary for performing data science operations
Statistics Learn important topics like exploratory data analysis, Probability theory, Bayes theorem, skewness, continuous distribution, estimation, hypothesis testing and so on.
Programming Learn coding to translate the data science concepts to instruct the computer to execute the task given to it. You cannot become a data scientist with programming.
Working with data Cleaning and validating is the initial process. Then you need to have the curiosity and creative thinking to be able to gain insights and patterns from the analyzed data.
NLP This is one of the most popular branches of AI. It is the attempt to make computers understand natural(human) language. There are assumptions and confusions about AI and data science. Eliminate your doubts about Artificial Intelligence and Data science with our article on the same.
ML Machine Learning is yet another trending field that has found application in data science. For those who think ML is data science; NO, it is not so. Learn the top differences between ML and Data science. Learn the ML algorithms that have found useful in data science.
Some of the tools are listed here. Check out our blog Top data science tools that employers expect you to know for more details MS Excel Python, Java Why Data scientists love python R, RStudio Why choose R for your data science project? RapidMiner Hadoop HadoopSpark, Storm
Takeaways from the article.
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