Choosing a Career in Data Science
Reading Time: 5 minutesIf you are someone who is passionate about Data Science and do not know where to start, this article is just for you. I was in your shoes once up on a time and now I am proud of the path I have taken and the things I have learnt in the field of Data Scienc
How Valuable is Data science?
Computer Science and engineering is a very vast field where anybody can get lost easily. Choosing the right path in this field can be a daunting task especially if your main aim is to settle with a good placement package. Finding the right subfield that suits your taste requires a lot of curiosity and research.
Data Science which was just a spec at the start of the tech era is one of the most valuable assets in today's growing age of technology. The cloud has become the one and only hub of information, information collected from even the remotest corners of the world. When all the information is stored in one place what more could be valuable than digging in and grasping all the secrets of the worldly patterns?
It is research and openness to opportunities that can create interest and growth in a person.
— Rathna 🙂
How I Started My journey as a Data scientist…….
Pursuing a career in the Computer science field was difficult when my passion was to become a doctor initially and yes I was a biology student and CSE was not even my optional subject. Due to a few unfortunate reasons, I had to be open with my range of choices and not take a narrow road but also had to take a peek at the alleys. That was when I developed an interest in Data science.
Data Science is that kind of field that can keep you hooked like an unsolved mystery, the truth waiting to be unwrapped by someone.
The beggining…
I started my search sprint in this field. I looked up many youtube videos. I listened to the experiences of people who are already in this field, and finally, I was able to come to a few conclusions getting a vague road map to this goal of mine.
Data Science is not for someone who wants to just learn one skill and work in a company, and it is not definitely doing the same task repetitively like an average coder in a firm. Data science involves real-world data where. Where the data changes every minute according to the changes in the world. Digging up some really valuable insight that can be used for business purposes requires a surplus amount of coding skills.
It is a field that requires thorough consistency and perseverance because a huge amount of data can be pretty overwhelming!
A good deal of patience with exploratory skills can help you crack any dataset.
Data science is growing pretty fast that nowadays many future events can be predicted using the pattern and trends found in the dataset.
Business owners would literally kill to hire a great data scientist who can help them unwrap these secrets to them. Make sure that is you 🙂
The various category:
There are various subdivisions under Data science :
Data Engineers
Where do you think all the real-world data is obtained from? Let me break down this process for you in layman's language.
Abundant data is stored in the data warehouse. Engineers need to navigate through this huge data warehouse to get the right dataset for the specific situation. They also use web crawler and web scraper to obtain the data needed.
Data Analyst:
This is where the real process of analysis starts. You take a vague scan of the dataset, to familiarize yourself with it. Then you clean the dataset off of any empty data and inconsistent data as their presence will skew the insights leading to huge losses. Then using various tools and technology you try to find meaning, pattern, trends, and causes of any pattern, you may also find outliers in your data (data that is irrelevant). You make a project of all your analysis and conclude your finding and present it.
Data Visualization:
This process involves the presentation and communication part your insights can be of no use if you are not able to present them in the right manner.
You create graphical visual content of your insights using Excel sheet, Tableau, and Power BI and present it to your stakeholders to make better decisions.
Data Scientist:
All these data and your insights and used to create Machine learning models which can actually predict future events with the current data. This process is done by a data scientist.
The Road Map:
Data Analyst
It is suggested that first, you become an adept Data Analyst and then move forward to become a Data Scientist by improving your skills.
Kaggle
is a website that every data analyst and data scientist should know. Kaggle not only provides a platform for data analysts to present their skills but also provides millions of free datasets that can be used by anybody, furthermore Kaggle also hosts many competitions in the field of data analysis and rewards the winner. You can also earn badges that will give your resume a good boost.
Excel:
To become an expert Data Analyst I would suggest starting off with MS Excel. Excel is almost used in 98% of industries. Excel not only stores data but also is used for the calculation and graphical representation of your data.
Take a small Dataset from Kaggle and upload it in excel and start your analysis. Ask many questions about the patterns and trends you notice in the data set and note them down and try to find answers for those questions from the dataset or by using google, next use graphs, and charts for concluding your insights in a simple and smooth manner.
Learn by Doing it !
That's right the only right way to learn is learn by doing it.
Data science requires is not a subject you can learn by seeing multiple lectures and attending workshops. The only way to understand the deepest meaning is by doing it by yourself, analyzing by yourself and asking questions.
After Excel…
SQL: is an intelligent tool used for the analysis of huge real-world data sets. Excel will not support huge data sets and SQL comes in that time of need.
Python: various python libraries can also give u better analysis tools. Pandas, NumPy, Matplotlib, Seaborn, etc are few of the common libraries used by data analyst. It is not necessary you have to learn about all these libraries and byheart all the codes. All you need is the basics and the rest you can google, among every developer out there there is at least one developer who might have had your doubt.
Tableau, Power BI, and looker: These are Data visualization tools that help you to make advanced interactive charts and graphs. These are specialized tools for Data visualization and the presentation of your insights to the stakeholders really holds the key. Condensing your technical insights to laymans language helps communicate better with your stakeholders. Like Leonard DaVinci said ‘Simplicity is the ultimate sophistication’
The world needs better Data scientists…
Why?
Without Data you are just another person with opinion
— W. Edwards Deming
Thank you so much for reading this article hopefully you found some information to start you data science future. As I said before Data Science is a field that requires thorough consistency and perseverance. A huge dataset can be pretty overwhelming to crack. Where you just see multiple columns and at least 1 million rows of the dataset, anybody is bound to flip out that is when you take a break to gather yourself up to crack the mystery with a fresh mind.
This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.
© 2023 Rathna 09
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