Let's discuss a few strategies that will give you a successful data science career
Data science is all about making the best out of the cluttered data. It helps to extract meaningful insights from raw data. It is basically used to make decisions and predictions. It helps in making marketing strategy by analyzing the data. A Data Scientist discovers hidden patterns from the raw data.
We create massive amount of internet data daily while online shopping, communicating, reading news, listening to music, searching for information, expressing our opinions etc. It’s not just Internet data, but we generate data in many aspects of our daily lives. Almost everything on earth that generates data comes within the boundaries of this field. You might have heard about data analysis fields such as data mining, statistics, predictive analysis etc. Data science is nothing but just an extension of these data analysis fields.
With the help of data science techniques, the companies are recognizing their clients and their requirements well. Companies are analyzing their challenges easily and also addressing them effectively. The healthcare sector are finding better solutions to take care the patients. Better opportunities are being created for the students. eCommerce businesses are transforming and benefiting immensely. Using data science and machine learning, Cybersecurity firms are detecting malwares on a daily basis. All search engines make use of data science algorithms to deliver the best result. Had there been no data science, Google wouldn’t have been the ‘Google’ we know today. Youtube wouldn't have been so big. Facebook, Amazon, Apple are all using Data Science in many ways.
What does a Data Scientist do?
A Data scientist works on data and create models that can make predictions using algorithms and other techniques. He is the one who analyses data and brings out information through it. They rely heavily on artificial intelligence, machine learning and deep learning to perform their jobs.
Now let me briefly tell you about AI, Machine learning and Deep Learning
Artificial intelligence is nothing but imparting a cognitive ability to a machine so that it can function like a human brain. Machine learning is a subset of AI which allows machines to learn and improve automatically with experience. Machine learning scientists research new methods of data analysis and create algorithms. Deep Learning is a subset of Machine Learning that teaches computers to mimic the workings of human brain in processing data. Deep learning is the key technology behind a drive less car. This model can achieve high level of accuracy and sometimes exceeding human performance.
Now let's talk about data science career and salary. Almost everything on this planet, which generates data, falls under the radar of data science. So, the need for a data scientist is also growing. And we can say that Data scientist are the future of the world. Glassdoor, which is a well known website worldwide that talks about Jobs and Salary, has ranked data scientist as the Best Job in US. LinkedIn has listed data scientist as one of the most promising jobs. It is also a known fact that Data Scientists are among the highest paid professionals in the industry. An entry-level data scientist can earn about 5-6 lakh per annum and as your experience and skills grow, your earnings rise dramatically.
Now let's discuss a few strategies that will give you a successful data science career.
1. Skills needed for a data scientist
No company would pay you a handsome paycheck unless you are capable of dealing with massive data. So, make sure you have got the right skills to get the valuable insights from huge data.
When it comes to data science, mathematical skills such as statistics and probability theory are key for algorithms implementation. and you know what? These concepts help you make better business decisions from data. So, make sure you put in a lot of effort into sharpening these mathematical skills.
In order to play with data at scale and apply certain algorithms to come up with some meaningful insights, you need to learn statistical programming language, like R or Python, and you should also learn a database querying language like SQL. Now let's find how to learn them. To master them you can use online resources that offer really good material. You can use platforms like Coursera, EdX and Udemy. Now, to be a successful data scientist, you should have data storytelling skills. So, work on your communication and presentation skills as well.
2. Gain some experience
Getting a full-time data scientist role for a freshers is not an easy job. Every company wants to have people with at least a little bit of real world experience. So, it is important to gain some experience. And for that you can start looking for on-the-job training, internships or freelance projects. This will help you gain more experience, skills and expertise. And guess what they just might result in a full-time position eventually.
3. Make yourself visible online
Have a very strong online presence so that potential employers can see what you’re doing. You should always showcase your proficiency online. And you can do so by participating actively in the community, by uploading your projects on GitHub, by writing blogs on data science topics etc. And this way, employers might reach out to you with freelance projects, internships, or interviews.
4. Build a Portfolio
As I said, strong online presence is the key to reach out to employers. Your online portfolio can do wonders in impressing the hiring manager. Building a strong Data Science Portfolio demonstrates your skills effectively and can win trust of hiring manager. Your Portfolio should convey that you have enough skills that can generate revenue and opportunities for the businesses...that you can extract insights from raw data...that you can build a system that can direct value to the customer and others in the organization.
Look, if you have a degree in a relevant field from a top-tier school, it’s relatively easy to get a job. If you don’t have a relevant degree from a top-tier school, you have to build that trust yourself. And that you can do through strong Portfolio.
You can present your data science portfolio on GitHub or on your blog. If you have a personal website, make a portfolio page that lists all of your projects. Just Remember If you are a fresher, you should have a few projects up on Github or on your blog, where the code is visible and well-documented. Believe me this strategy will help you align to the job you want.
5. Networking
Your social environment can help you a lot. Find someone who is already familiar with data science careers and can provide you guidance and advice. Talk to them about which companies they work for, discuss projects and technologies they are working on. Find out how they got their first job. What path they followed? Recruiters play important role at time when you are looking out for a job. You should try to build relationships with recruiters, they can assist you in many ways and help you get result faster.
6. Create targeted resume
Recruiters or hiring managers spend only a few seconds on each resume. They quickly filter and rank the applicants either manually or using resume screening software. They just look for specific skills and keywords. And Guess what? Many resumes are rejected because they are not optimized as per requirement. So, Don’t send the same resume to every company. Analyze the position well and customize the resume specific for that position. Believe me you’ll see results much faster.
