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With the power of data science, businesses gain unparalleled insight into customer behavior or develop new strategies. Becoming a data scientist requires knowledge and intelligence that can only be obtained through years of hard work. 

However, there are some tried-and-true steps that you can follow to make this process easier and give yourself an advantage over competing candidates for lucrative positions in the industry. Keep reading to learn more about these smart steps that will help set yourself apart from other applicants when it comes time for hiring decisions!

Learn the Skillset of a Data Scientist 

In today's data-driven world, having the skills of a data scientist can unlock countless opportunities. Whether you're looking to advance your career or just satisfy your curiosity, learning to manipulate and analyze large data sets can be a game-changer. 

Fortunately, there are plenty of resources available to help you on your journey, including online courses, boot camps, and even full-fledged degrees like a Master in Data Science at university. By mastering the skill set of a data scientist, you'll be equipped with the knowledge and tools to uncover insights and drive innovation in any industry. So why wait? Start exploring the world of data science today!

Get Comfortable with the Tools & Technologies

When trying to succeed as a data scientist, becoming proficient in data science is a must for any professional looking to thrive in their career. However, with so many advanced tools and technologies available, it can be daunting to know where to start. The key to succeeding in data science is to get comfortable with the tools and technologies necessary to analyze and interpret data effectively. 

Whether you're working with Python, R, or another programming language, taking the time to master these tools will allow you to become more confident and efficient in your work. By investing in your professional development and staying abreast of the latest trends and advancements in data science, you can set yourself apart and position yourself for success in this increasingly competitive field.

Develop Your Analytical and Problem-Solving Skills

As the field of data science continues to grow, it’s becoming increasingly important to sharpen your analytical and problem-solving skills to stay competitive. In a world where data is king, those who can effectively analyze and interpret large amounts of information are in high demand. 

So, how can you develop these essential skills? Start by regularly practicing your analytical reasoning and critical thinking abilities. This can involve everything from tackling complex problems using algorithms and data structures to engaging in debates and discussions with others in the field. 

Additionally, seek out opportunities for hands-on experience with data analysis tools and techniques, and don’t be afraid to learn new programming languages or statistical methods. By continually honing your analytical and problem-solving skills, you’ll be well on your way to becoming a top-notch data scientist.

Master the Art of Data Visualisation

You should already know that data visualization is a crucial component of understanding and communicating complex data. But mastering the art of data visualization takes more than just knowing how to make a graph or a chart. True mastery means understanding the nuances of how visual elements can influence perception and using that knowledge to create truly informative and compelling visualizations. 

With this skill under your belt, you'll be able to turn raw data into stories that communicate meaning in a way that is both efficient and engaging. So, if you're ready to take your data science game to the next level, it's time to start working on your visualization skills.

Understand the Basics of Machine Learning & AI

In the ever-growing world of data science, having a solid understanding of machine learning and artificial intelligence is essential to stay ahead of the game. By grasping the basics of these advanced technologies, data scientists can better analyze and interpret vast amounts of data to generate valuable insights. Machine learning algorithms can be trained to predict and classify data, while AI enables machines to learn from previous experiences to make decisions on their own. 

These tools can be used across a wide range of industries, such as finance, healthcare, and marketing, to streamline processes and increase efficiency. By mastering the fundamentals of machine learning and AI, data scientists can become more confident in their abilities and produce more insightful analyses.

Know How to Communicate Insights from Data

Nowadays, data scientists play a crucial role in making informed decisions that impact a company's bottom line. As a data scientist, it's not enough to just crunch numbers and run statistical models. Data science is complex and requires a lot of different skills. In addition to traditional data analysis and statistical modeling, it also requires strong problem-solving capabilities, creativity, and technical knowledge. You need to communicate the insights you glean from the data in a way that is clear, concise, and actionable. The ability to effectively communicate data insights is what sets great data scientists apart from good ones.

It requires a combination of analytical skills and communication skills to convey your findings and recommendations to stakeholders who may not have the same level of technical expertise as you. When you can communicate your insights in a way that resonates with your audience, you become a more effective and valuable data scientist.

Becoming a successful data scientist requires hard work and dedication - but when you reach the top, the rewards are well worth it. Showcasing your technical abilities combined with high-level problem-solving skills can open up countless opportunities. Being able to interpret and effectively communicate insights from data is an invaluable asset that employers strive for. 

The learning journey of data science can be overwhelming at times, but by learning the skillset, getting comfortable with the tools and technologies, developing analytical skills, mastering data visualization, understanding machine learning and AI basics, and recognizing how to communicate insights from data will help bring you closer to becoming a proficient and successful data scientist.

To hear about the future of the finance function and the need for bringing a data scientist into the finance environment, Finance Monthly speaks with Angela Mazza Teufer, Senior Vice President of ERPM at Oracle.

We are living in the age of data, one in which both traditional quantifiable information and unstructured data is being hoarded in huge amounts. It takes a specific set of skills to draw useful business insight out of this data, and that is why data scientists have become so crucial to the modern business.

The introduction of GDPR regulation earlier this year has forced companies to become more data literate, and has in some cases seen them appoint Chief Data Officers (CDOs) or build teams responsible for overseeing data governance.  This represents an important step towards a future where all businesses are able to make the most of their data, but it takes more than data management to turn data into value. This is where data scientists become crucial, and particularly in select business functions.

As a function that has always dealt in data and whose remit has expanded significantly in recent years, the finance team has a great deal to gain by bringing advanced data expertise into the fold.  Finance teams have traditionally been made up of people with a specific set of practical skills, including management accounting, auditing and forecasting.  While these remain important, businesses increasingly expect their finance department to play a more active role in driving organisational strategy, which requires a more diverse set of abilities. Data science is the most important of these.

What data science brings to the table

One of the biggest challenges faced by businesses is how to make sense of the enormous volume of data they collect, from customers, internally, and increasingly from third parties. Finance teams could easily spend all of their time just gathering and analysing data on business assets and performance, but the challenge today is to distil this information into something meaningful, especially as even financial reports are increasingly filled with ‘intangible’ assets that are not so clearly defined as revenue and profit, such as customer reach.

Having a data scientist embedded into the finance function will provide the specialist understanding and valuable resource to combine information in all forms, identify patterns that might otherwise have gone unnoticed, and most importantly draw out actions for the CFO or finance director to take to the board.

This also frees up other members of the team to focus on their areas of expertise rather than expecting them to pick up a whole new set of skills and take on a role they never signed up for. No matter the department, trying to ‘upskill’ an employee in data science underplays the importance of the role and makes light of the years of training and experience that specialist data scientists undertake.

Often, some of the most valuable information companies collect today starts life as an unstructured, chaotic set of data points. It ranges from concrete demographic data on their customers to news events and sometimes even weather patterns. The task of combining all of these streams of information and making sense of them requires the full-time attention of a dedicated specialist. It is certainly not something that core finance employees can accommodate on top of their existing responsibilities, nor can it be effectively undertaken without the appropriate training.

In short, it is much more effective to bring a data scientist into the finance environment and educate them on its specific needs, data types and ways of working, than it would be to pile complex data science responsibilities onto existing team members.

The future of the finance function

The changes to the role of the CFO and the growing demand on the finance function to be more forward looking and predictive have been well documented, but many organisations still find themselves in a period of transition. They understand what’s expected from them but are still setting up their teams and processes to deliver on this expanded brief.

It is enough of a challenge to forecast accurately in periods of uncertainty, without having to collect, analyse and process data from beyond the balance sheet as well, but it can be overcome with expert support.  By bringing the right mix of skills into the finance team, companies can develop the skills they need quickly and start reaping the benefits today.

About Finance Monthly

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