How to Survive Your First Week as a Data Scientist
Whenever we access information online, we actively take part in a feedback loop that also creates new information about our own behaviors. From the moment someone makes a search in a web browser, engages with social media, checks the weather on their smartphone, or sends a text to their friend: they generate data. That data is ever-growing and makes for a treasure trove of insights that businesses rely on to stay ahead and better serve their customers.
As the tech industry and its related job sectors evolve, businesses continue to grow, produce more data, and increase their search for candidates to join their companies in data-focused roles. Today’s increased need for data science experts is palpable—with sources like the Bureau of Labor Statistics projecting a 15% growth in roles over the next decade—significantly higher than the average across all occupations.
Individuals with data science expertise are needed in virtually every field, not simply in technology, but in an expanse of industries including government security, healthcare, retail apps, and everything in between. And while certain data science work will likely be automated within the next decade, there continues to be a definitive need for professionals who can help devise data-oriented solutions and help companies put them into practice.
As an incoming data scientist, it’s invaluable to come in aware of the importance of your role: follow the tips in this brief week-one guide to manage your new responsibilities with confidence and some extra preparation.
Challenge your expectations
Staying up to date with newer industry trends is a must for businesses to achieve peak efficiency and remain at the forefront of the competition. You can maximize your value to the new team by studying the new developments in data analytics and machine learning.
As a result of the various big data issues, there is a growing range of innovative solutions. Here are some of the developments currently making waves in data science.
The entire process of data science includes model building, identification of the problem, data collection, data processing, system exploration, and analysis. A critical hurdle in managing the mass of data you will handle on a daily basis is time. While no degree of automation can entirely displace all the steps necessary to complete the data science process, automation can help simplify some of the steps and keep things moving along quickly. Data science automation is making huge strides – leveraging AI and machine learning to analyze vast amounts of data more efficiently.
2. Graph analytics
Graphs are a popular tool for data scientists to communicate their observations with the company stakeholders. The logic behind using graphs is to represent the complicated data abstractly and in a visual format that is more digestible and offers clear insights. Graph Analytics are analytic tools used to determine the strength and direction of relationships between objects in a graph. There are four main types of graph analytics:
- Centrality analysis: Creates estimates for how important a connection is for the effectiveness of a network. It finds the most influential people in a social network or most frequently accessed web pages algorithmically.
- Community detection: Distance and cluster density of relationships can be used to find groups of people interacting frequently in a social network. Community data deals with the detection and behavior patterns of communities.
- Connectivity analysis: Determine how strongly or weakly two connections are linked.
- Path analysis: Examines the relationships between connections. Mostly used in shortest distance solutions.
3. Privacy by design
As data security and user privacy concerns become more common, PBD is a concept that integrates privacy into the creation and operation of new devices, IT systems, networked infrastructure, and even corporate policies. Building and integrating solutions in the early phases of a project identifies any potential problems at an early stage to prevent them in the long run from affecting the stakeholders or user base.
Due to the rapidly evolving nature of the problems facing today’s data scientists, a surplus of data science-related online communities has sprung up, allowing users all over the world to share observations, problem fixes, and new cutting edge analytics tools. That said, don’t forget to look to your colleagues for insights as well—and then consider reaching out beyond your immediate team members.
John Sukup, a data science strategist and mentor at Springboard, notes that often, “an integral part of the data science team is often a colleague that doesn’t actually ‘sit’ on that team: the Subject-Matter Expert. Being able to engage with these individuals is crucial to the success of a data science or ML project, so keeping communication channels open and active with them is very important.”
Get To Know Other Data Science Students
Stacking your system
Give yourself a head start by acclimating to the programming languages and machine learning toolkits that your new team prefers. As a data scientist, a bulk of your programming experience is likely using either Python or R.
Though in recent years Python has taken strides to become the de-facto industry standard, rising quickly in popularity, sporting integrations for numerous programming languages, and incredibly popular libraries such as TensorFlow, SAS, sci-kit-learn, and Apache Spark.
There are many available online platforms that allow you to brush up on the specifics of optimizing your Python skills for data science. In addition, there are numerous tools for use in automating data organization, build predictive models, or transforming raw data into customer consumables.
Get yourself organized by arranging your toolkits according to their use: volume, variety, and velocity. Taking note of what tools and methods your team prefers early on and being open about asking them their opinion, will make adjusting to your new tasks that much easier.
Think about the data “story”
Data collection, analysis, and now graph analytics processes are used to arrange a tangled mass of data into manageable chunks, but one of the bigger responsibilities you have as a data scientist is using those chunks of raw data to present data insights to users or stakeholders, rather than raw data itself.
John echoes the importance of having a solid grasp around the bigger picture of your company’s goals. “Focus on understanding how the business works and what metrics are most important to the C-suite,” he says. “You can make hundreds of models, but if they don’t tie back to the KPIs important to moving the business forward, they are essentially useless.”
Providing data insights as an insightful business narrative tool is thought-provoking, rewarding for both companies and data scientists, and those looking to break into a career in data science can expect to see more of this trend.
Don’t forget that all the independent users generating the data, and the data as a collected whole, tells a story about the user base. Your first few weeks on the job are likely to be far less analytical- and more centered on wrangling large amounts of data in order to find your footing.
You can also embrace this grassroots outlook on data when you approach your preliminary management and team meetings over your first few weeks.
Live to learn—and get flexible
On the first day of work as a data scientist, you can expect to have interactions with the team and exchange ideas. Data science is a teamwork game. It will lead to successful projects if the challenges are handled by dividing responsibilities among individuals in an effective way.
You can also expect to deal with some problems which you haven’t tried until now. Be open to looking to senior team members who can help guide you away from common pitfalls, advise you in new algorithms that you may be unfamiliar with, and in general help you adjust to your new role.
When first getting to know your leadership or management teams to whom you’ll be reporting, John recommends taking the time to establish a relationship of trust with your direct manager right away. “Both people need to feel comfortable sharing both positive and negative results with each other. Not every project is going to be a success and that’s just the nature of the beast,” he says.
In your first month, there may be a lot of dead ends, detours, or rocky roads, but data scientists should possess the tenacity and grit to stay organized in their research. “You’re called a data ‘scientist’ for a reason,” continues John. “A lot of your time is spent in experimentation and ‘moving the needle’ incrementally. I think it’s wise for a new data scientist to set expectations right away and be honest.”
Data scientists don’t simply need to understand programming languages, database management, and how to transpose data into digestible visuals—they need to be naturally curious about their surrounding world and have an analytical eye. They must know how to communicate in several different modes – with their team, with their stakeholders, and with clients. Data scientists need to have a strong technical background, but they also need to have great intuition about data, exploring questions like:
- Are the features meaningful, and do they reflect what you think they should mean?
- Given the way your data is distributed, which model should you be using?
- What does it mean if a value is missing, and what should you do with it?
Final key takeaways
In the quickly evolving tech world, it can feel like there are shakeups and big changes every quarter, and especially when joining a whole new team, it can feel like jumping in the deep end. It is important to remember all the skills, training, and experience that led to you filling your new position.
To summarize, here’s a quick rundown of all the top tips that will help you make the absolute most out of your first week, and set up a solid foundation for your career as a data scientist:
- Keep up to date on all the new trends, developments, advancements, and research being done in the field of data science, especially automation in machine learning
- Don’t sleep on setting up your computer to take on the piles of new data you will be introducing to all your systems. Optimize yourself.
- Don’t forget to be a person! Your fellow scientists, stakeholders, customers, and data generators are people just like you. Don’t forget that when you build your models.
- Be curious. The willingness to explore, to “get your hands dirty”, to try new and difficult things will make you invaluable to any team.
Since you’re here…
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