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You're reading this because you're thinking about joining the ranks of aspiring data scientists. And who can blame you? Data science is a growing field, even a decade after its now-infamous “sexiest job” accolade from Harvard Business Review. The US Bureau of Labor Statistics currently predict The employment rate for data scientists will grow 35 percent between 2022 and 2032. Compare that to the average job growth rate, which is just 5 percent.
It has other things going for it:
- It is well paid (again, the BLS found an average salary of $103,000 in 2022)
- It is accompanied by a high quality of life (above average job happiness). according to Career Explorer)
- There is job security despite the recent round of layoffs, because there is a lot of demand for the position.
So there are many reasons to want to enter this field.
Source: https://www.bls.gov/ooh/math/data-scientists.html
But data science is a very broad field, with many different job titles and skill sets that you should know before you start. This article will walk you through the different directions you can take and what you need to know for each of them to get into data science.
To make a successful transition to a data science careerYou will need to follow a structured approach:
- Evaluate your data science skills and identify gaps.
- Get hands-on experience in the areas you are weak in.
- Network. Join data science groups, attend meetups, and contribute to forums.
Let's dig deeper.
Evaluate your starting position
What do you already know and how can it be applied in data science? Think about: any programming knowledge, statistical skills, or data analysis experience you have.
Next, identify gaps in your skills, particularly those essential for data science. SQL is really a must, but programming in Python or R, advanced statistics, machine learning, and data visualization are also extremely beneficial.
Once you have identified these gaps, seek relevant education or training to fill them. This could be done through online courses, university programs, boot camps or self-study, with a focus on practical learning.
Practical experience
You shouldn't limit yourself to watching videos and reading blog posts. Hands-on experience is crucial in data science. Participate in projects that allow you to apply your new skills in real-world scenarios. These could be personal projects, contributions to open source platforms, or participation in data contests like Kaggle.
If you have some basic starting skills, you may want to consider looking into internships or freelancing to gain industry experience.
More important, Document all your projects and experiences in a portfoliohighlighting his problem-solving process, the techniques he used, and the impact of his work.
Grid
Breaking into data science often comes down to who you know, in addition to what you know. Find mentors, participate in meetups, conferences, and workshops to learn about new trends, and participate in online data science communities like Stack Overflow, GitHub, or Reddit. These platforms allow you to learn from others, share your knowledge, and get noticed within the data science community.
If you like become a data scientist from scratch, it makes sense to think about the skills you will need to develop as a tree. There are “core” skills that are common to all data science jobs, and then each specialty has “branch” skills that continue to branch out into increasingly specialized roles.
There are three main skills that every data scientist needs, no matter which direction they go:
Data Manipulation/Dispute Using SQL
Data science basically boils down to managing and organizing large data sets. To do that, you need to know SQL. Is he essential tool for data manipulation and discussion.
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Soft skills
Data science does not happen in a vacuum. You need to play well with others, which means improving your interpersonal skills. Being able to communicate complex data findings in a clear and understandable way to non-technical stakeholders is as important as technical skills. These include effective communication, problem solving and business acumen.
Problem solving helps address complex data challenges, while business acumen ensures data-driven solutions are aligned with organizational goals.
Constant learning attitude
Data science is different than it was five years ago. Just look at where we are today with ai compared to 2018. New tools, techniques, and theories are constantly emerging. This is why you need a continuous learning mindset to stay up to date with the latest developments and adapt to new technologies and methodologies in the field.
You will need self-motivation to learn and adapt, as well as a proactive approach to acquiring new knowledge and skills.
While there are common skills, as I described above, each role demands its own specific skill set. (Remember? Branches.) For example, statistical analysis, Python/R programming skills, and data visualization are specific to more specialized data science jobs.
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Let's look at each data science-adjacent role so you can see what you need.
Business/Data Analyst
Yes, this is a data science role! Even if the naysayers disagree, I still believe you can treat it as a stepping stone at least if your goal is to get into a data science career.
As a data or business analyst, you are in charge of bridging the gap between data insights and business strategy. It is perfect for those who have the ability to understand business needs and translate them into data-driven solutions.
As basic skills, you will need business intelligence (no surprises), strong analytical skills, proficient in data query languages, predominantly SQL. In this role, Python and R are optional because the main task is data wrangling.
There's a display component but depending on your job, it may mean creating dashboards in Tableau or charts in Excel.
Analysis of data
This role focuses on interpreting data to provide useful information. It's a great job for you if you like translating numbers into trading stories and strategies.
You will need firm control statistical analysis and data visualization. (although again, these can be dashboards and/or Excel charts). You will also need mastery of Analysis tools as Excel, Tableau and SQL. Python/R is again optional, but remember that they can really help with implementing statistics and automation.
Machine learning
Machine Learning scientists develop predictive models and algorithms to make predictions or decisions based on data. These roles are suitable for those with a strong interest in ai and model building.
There are no surprises when it comes to basic skills: you will need a deep knowledge of algorithms, experience with machine learning frameworks such as TensorFlow and PyTorch, and strong programming skills. Python and/or R are no longer optional but essential.
Data engineering
This role allows you to focus on the architecture, management, and maintenance of data pipelines. It is a good option for people who enjoy the technical challenges of managing and optimizing data flow and storage.
To get this job, you will need eExperience in database management, ETL processes and mastery of big data technologies such as Hadoop and Spark. You will also need proficiency in data pipeline automation using technologies such as Airflow.
Business Intelligence
In business intelligence, it's about creating visualizations. It's great for storytellers and people with a strong business sense.
You'll need to be a pro with dashboard technologies like Tableau and Qlik, as those are the tools you'll use to develop your visualizations. He will also need data manipulation skills (read: SQL skills) to help optimize data queries that speed up dashboard performance.
As I mentioned earlier in the article, data science is a rapidly evolving field. New jobs and roles open up all the time. Going back to my tree analogy, I like to think of it as adding new branches to the main trunk of data science. There are now cloud engineers, SQL specialists, DevOps roles, and more all still connected to that data science pathway. Therefore, this article provides only a brief idea of the directions you can take with data science.
More than that, you should also remember that data science comes with challenges associated with that six-figure salary. There is a steep learning curve and the learning never ends. New technologies, trends and tools come fast and strong, and if you want to keep your job, you need to stay up to date.
All that said, it is a great career option. With the three core competencies I mentioned under your belt, you will be well equipped to take on any role of data science that attracts you.
Nate Rosidi He is a data scientist and in product strategy. He is also an adjunct professor of analysis and is the founder of StrataScratch, a platform that helps data scientists prepare for their interviews with real questions from top companies. Connect with him on Twitter: StrataScratch either LinkedIn.
