What’s more, the data science field is growing rapidly. According to LinkedIn, a data scientist is now the most lucrative position in tech. In addition, the U.S. Bureau of Labor Statistics predicts that demand for data scientists will soar by 19% by 2020, a rate that’s much faster than average.
Data scientists are not only in high demand, but they’re also hard to find. Recruiters have a tough time filling data science positions, and the problem is only getting worse. 75% of companies said they were struggling to hire qualified data scientists this year and last, up from 53% in 2016 and 44% in 2015, according to a survey released earlier by New York-based Burtch Works Executive Recruiting.
What is Data Science?
Data science is the field of study that combines domain expertise, programming skills, and knowledge of maths and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence.
Data scientists are responsible for collecting data, organizing it into a format appropriate for analysis, analyzing the data using various statistical methods and tools, and finally communicating the results of their research. Data scientists have some overlap with data analysts in their job duties. However, data scientists typically have more excellent skills in programming and modeling than analysts. It doesn’t matter whether you have a degree or not to become a data scientist.
Main Areas in Data Science
These are the main areas in data science:
Data engineering
This is the process of taking raw data from any number of sources, wrangling it into a usable format, and loading it into a database. This step is critical to the data analytics process and is often the most time-consuming part of data science projects. In addition, data engineers may be responsible for creating the infrastructure that allows other team members to analyze and visualize data in meaningful ways.
Data Visualization
Data visualization is creating charts and graphs that allow data scientists and analysts to visually display information in a way that is easy to understand. Once you have cleaned data, it’s crucial to gather insights from it quickly. Data visualization helps you communicate complex ideas through graphics, charts, maps, and other visuals.
Data Analysis
Data analysis involves exploring patterns and trends and then applying those findings to make informed business decisions. Data analysts typically work with a specific set of tools to analyze large volumes of structured data (data stored in tables). But since data analysis often requires combining multiple types of datasets, analysts may also be tasked with integrating various sources into one database or cloud-based system.
Learn the Basics:
Coursera
You can start with some of the free courses available on Coursera, edX, and Udacity. These will teach you the basics of what you need to know to get started in data science.
In addition to the technical skills, you’ll also need to learn to communicate your findings effectively. Presenting your results in an easy way for everyone to understand can be just as important as coming up with those findings. For example, knowing how to make good visualizations is essential because it makes it easy for people who don’t have any coding experience to understand what you’re saying.
To get a feel for what data scientists do, try Kaggle competitions. These are fun, structured ways to work on real-world data science problems. It’s a great way to practice your skills and meet other people in the field. Also, many companies use Kaggle competitions as an interview tool.
What is 360 Training?
360training.com is an eLearning company that serves over 4,000 clients across various industries. As an award-winning learning solutions provider, 360training.com is uniquely positioned to help individuals and businesses with online training needs.
Why choose 360?
They are leading e-learning providers for highly regulated industries. The courses are developed by subject matter experts who have years of experience with compliance standards. They offer courses that meet federal and state regulations and requirements, including OSHA, EPA, DOT, and GHS/HazCom.
360 understands the importance of quality course content that meets or exceeds industry standards because they are part of your industry.
The courses are available 24/7/365 in multiple formats, including desktop, mobile, and virtual reality (VR). You can take our courses at your own pace, time, and on any device with internet access.
TEFL Academy
TEFL Academy’s data science training course combines an overview of the fundamentals of data science with a focus on practical application. You will learn how to use Excel, SQL, Python, and Tableau to extract value from your organization’s data.
As organizations amass more and more data, they need experts who can interpret it, analyze it, and communicate their findings clearly and compellingly. Data science is part art, part science – you need to understand what questions to ask and then analyze the results.
Our data science training course will introduce you to the foundations of data science and equip you with the skills needed to work as a junior analyst within your organization.
Build a Portfolio
The portfolio is the most important thing for getting a data science job. You need to build a portfolio, and it has to be public.
The first step is to develop an idea, which isn’t very easy. But you can use this list of Kaggle competitions or open-ended projects to get started. Again, I recommend starting with something easy and working your way up.
When you solve your first project, describe it in a blog post and make sure to put the code on Bluehost- affiliate! Then, you can share it with anyone who wants to see it.
If you already have a blog, make sure not to put too many posts about unrelated topics there because this will make the blog look messy and unfocused.
After completing the first project, continue doing more projects until you feel confident enough to apply for jobs. Make sure that each project is described in a separate post on your blog. Also, remember that the more tasks you do, the better chances you have of landing a job as a data scientist!
Create a Great Resume
The resume is important, but it is only a tiny part of the job search. It’s not your primary focus, and it’s certainly not the only thing you need to do to get a job in data science. Treat your resume like a brochure and focus on getting people to read it.
You need to build a solid online presence.
Most companies will care about what you can do and will first look at your projects, public code, and blog posts.
If they like what they see, they will look at your resume. This means that building a strong online presence is crucial for getting a job in data science. Many people with great resumes did not get jobs because they didn’t have much of an online presence.
The best way to build an online presence is by using “resume planet” to create an exciting resume.
Networking
Networking is a critical aspect of any job search. You can find out about job openings, learn more about different companies, and even get introductions to people who could become your colleagues.
To get started, try to go to networking events related to the field you want to work in. This can be anything from Meetup (search for “data science” or “machine learning”) to conferences (e.g., PyData).
At the event, think of yourself as a journalist: you’re there not only to network but also to ask questions and take notes. For example, you might be curious whether a company is hiring data scientists. Or you might want to know what sort of projects they do in their groups. Or perhaps you want to find out how they use Python or R in their daily work.
After you meet someone and engage in a conversation, exchange business cards, and then send an email right after the event — while it’s still fresh on your mind — thanking them for their time and mentioning something specific about your conversation.
Start With Some Small Jobs
The best way to get started is by getting your hands dirty. Try out small tasks/jobs and understand them well. For example, you could join Kaggle or even take up sample datasets from Dataquest and try solving them using R or Python.
Fiverr
Many people are trying to get into data science from non-traditional backgrounds. But, unfortunately, a lot of the stuff you see on job postings isn’t what you need to do to get a job in data science.
The best way to get into data science right now is through Fiverr. It’s a marketplace where you can offer your freelancer or contract worker skills. Data science jobs are broken down into smaller tasks, and it’s easier to find work there than anywhere else.
Roughly speaking, there are two main types of data science jobs on Fiverr:
Preprocessing Data
You receive a dataset and are asked to prepare it for analysis by cleaning and transforming the data. This includes dealing with missing values (e.g., imputation), scaling variables, and normalizing data according to standard methods (e.g., unit normalization). This can be done in languages like R, Python, or JavaScript, but a simple spreadsheet will suffice in most cases.
Data Analysis:
You take the cleaned dataset and analyze it using the appropriate statistical tests and machine learning algorithms for the problem at hand.
Upwork
Upwork is a platform that connects freelancers to companies looking for talent for short-term projects. It has some of the largest numbers of data science and AI jobs, but it can be harder to figure out what the company is looking for because they don’t have access to your resume, which they would in the case of job boards.
If you’re an experienced freelancer, you probably have a good idea of how Upwork works. If not, here are some tips:
· Ensure your profile is complete and fill out all relevant information about what you’re looking for and your experience.
· Include a good picture that looks professional and shows who you are.
· Take advantage of the ability to include samples (or links to samples) with your profile. This could be an assignment you’ve done in the past or something as a hobby or personal project.
· Use keywords that employers may be searching for when they look at your profile. Some examples are machine learning, SQL, R, Python, pandas, and NumPy.
What Challenges do Data Scientists Face?
If you’re looking to get into data science, many obstacles might discourage you. So to help any aspiring data scientist, we created a survey and asked hundreds of professionals in the field what they thought were the trickiest parts of getting into data science.
The results show that the biggest challenge is finding a position and getting the necessary experience to build a portfolio. More specifically, 27% of our respondents think that creating a portfolio is the most challenging part of transitioning from other roles into data science, followed closely by finding an entry-level position (24%) and networking with other data scientists (19%).
So how do you overcome these challenges?
First, if you are starting out in data science, it’s essential to have a portfolio that showcases your skills. Here are some tips for building one:
Start practicing today:
If you need help getting started or have never worked with big data before, join a free course where you can learn about the basics of data science and start working on real projects
Work on end-to-end projects:
Try working on individual tasks like cleaning up datasets or analyzing data using different tools. Once you’re comfortable with this, try putting all your skills together and developing end-to-end projects.
What is Interesting Most about Data Science, and Why?
If you’re trying to break into the data science field, here’s some good news: There are a lot of jobs and not enough people to fill them. But that’s also bad news — because it means you’re going to have lots of competition for those jobs.
If you’re still in school or just landed your first job after graduation (or are looking for a new one), the most important things to do are the same: Learn as much as possible and network with people who can help you land a job.
If you’re in school, consider taking an internship or two with companies with a lot of data. You don’t want just to be working with Excel spreadsheets; it’s more interesting if you get to work with Big Data or on data visualization projects.
There is no substitute for getting your hands dirty and working on real-world problems.
But, how do you explain the data science role to friends and family?
There’s a common misconception that you need data science degrees or work experience to be a data scientist. While it certainly helps, the truth is that skills like analytics and data literacy can be learned on the job. In the last two years, we’ve interviewed hundreds of candidates from all walks of life, from recent graduates to seasoned executives. When evaluating applicants for our team, we look
for three key things: technical ability (such as SQL and R), curiosity (a passion for learning), and communication skills (the ability to explain complex concepts in plain English).
So what does a data scientist do?
In short, data scientists turn numbers into insights. They work with large datasets to find patterns and trends that help companies make more informed decisions. For example, at Shopify, we have a team of data scientists who build predictive models to predict customer lifetime value or churn risk. These models help us better understand our customers and personalize their experience with our products by sending them targeted emails with relevant content at the right time.
How do you Get Started?
Think about what you’re really interested in and go from there. If you really like programming, then you should be doing more programming. If you like working with people, then perhaps you should be doing more interviews with users.
I think that self-taught data scientists should start by learning how to program. Learn Python or R and start applying them to your own projects. Most of the entry positions require some knowledge of statistics or machine learning, so learn those as well.
The next level is getting a job. Then I’d focus on building up a portfolio of interesting projects and publications that prove that you know what you’re doing.
The first step in your journey is always the hardest one to take. But once you’ve taken it, everything else will seem much easier in comparison!
What Advice is good for a Person Getting Started in Data Science?
There are several good introductions to the field and lots of good blogs. Hadley Wickham’s R for Data Science is one example. If you’re interested in machine learning, I recommend elements of statistical learning, which is available for free online, and An Introduction to Statistical Learning with Applications in R.
I’d also recommend programming. If you’re just starting out, don’t worry about getting a particular language under your belt; instead, focus on learning how to program. Writing code is a key part of data science — it isn’t something you can outsource. There are many options here, but if you need a place to start and are comfortable with Python, check out Zed Shaw’s Learn Python the Hard Way. Regardless of which language you choose, make sure that you have some way to share your work (e.g., a GitHub account) so people can see what you do.
Finally, I would recommend finding a project that interests you and getting into the weeds on it. A lot of the skills that are important in data science aren’t specific to the field — things like cleaning data or manipulating strings in a programming language — so there isn’t one thing that.
Reasons Why Data Science is Popular
Data Science is an emerging field and it has a promising future. But why is it so popular? Let’s read about the reasons in detail.
Companies are Collecting Tons of Data
Data is a very valuable asset for companies. It helps them to understand their customers better and hence, they can services their customers more efficiently. For example, when a customer buys a product from Amazon, Amazon stores information about that purchase. With this information, Amazon can learn what products are popular among its customers, which products they buy together (co-purchase), and suggest items that the customer might be interested in buying next time.
High Salary
Data Scientist is the sexiest job of the 21st century and it’s paying off quite well. The average starting salary for a data scientist is around $95,000 and can go up to $165,000.
The skills required by a Data Scientist are also niche and this means that there is a dearth of professionals already working in this field. This in turn means that Data Scientists can demand high salaries and still get them.
The popularity of Data Science has resulted in a significant rise in the number of jobs associated with it. In fact, according to an Indeed report, job postings related to data science have increased by 341% over the last five years.
The Volume of data is increasing and this is the main reason why data science is in demand.
Increasing Variety of Data
There are many types of data, e.g. structured, unstructured, semi-structured etc. In a structured database, information is organized in rows and columns whereas in unstructured data, there is no organization.
There are various sources like social media, videos, images from where we can get unstructured data which can be analyzed by using the techniques of data science to extract useful insights for business and society.
Increasing Velocity of Data
The world has become fast-paced and so does the volume of data that is being generated every single second. The velocity refers to the speed at which new data is generated. It’s not just about how much data you have but also about how quickly it arrives.
The huge amount of data generated every second needs to be analyzed as fast as possible which can be done by using machine learning models that could generate results instantaneously without any delay.
Apply for Jobs
For entry-level candidates, it is vital to have a solid academic background and a few relevant internships. Beyond that, you need to prove that you can take the initiative, solve problems, and do what it takes to get the job done.
Here are some tips for breaking into data science:
Apply for jobs and keep trying. There are probably ten other people who applied for the same position for every job you apply for. It is important not to get discouraged by the competition but instead try to stand out from the crowd. You might want to consider finding a recruiting partner like Find My Profession who can help you with your application process.
At the end of the day, data scientists are expected to be good at a lot of things. Data science is a fairly broad and heterogeneous field, which makes it challenging to develop a standardized and streamlined career path.
