The Best Career Path for Data Science

Data science is among the most popular and highest-paying tech jobs. Because most businesses today rely on data for decision-making, the opportunity to build a sustainable career in data science is massive. There are many routes to build a data science career depending on your skills and desired area of specialization. This field typically combines mathematics, statistics, and computing to generate meaningful insights and take actionable steps. 

Career Path for Data Science: Major Skills Required

No matter your chosen path in data science, there are general skills you need to succeed in this field. These skills are rudimentary and necessary to build a firm footing in this industry. Note that it takes time and a lot of practice to master these skills. 

Programming 

This is a basic requirement for any data science path. Computers have their own languages and you need to speak the same to convert your analysis into action. The most broadly used programming language in data science is Python. Other languages are R, SQL, C, C++, and others. 

Statistics and Machine Learning

Statistics and mathematics feed machine learning algorithms. At the least, you should have a rudimentary knowledge of maths and statistics. You also need to understand various algorithms and how they work so you can apply them to datasets for evaluation. 

Deep Learning and Big Data

Becoming skilled in deep learning is necessary as you advance in your data science career path. Deep learning is a higher version of machine learning. It focuses on complex problems and datasets. Knowledge of big data is also important and data scientists need to understand the journey through time.

Data Manipulation and Analysis 

This skill involves tweaking datasets by exploring different ways to manipulate and analyze the data. This skill requires knowledge of various pre-processing operations. SQL is useful to hone your data manipulation techniques. 

Model Deployment 

Almost every role in the data science career path involves designing, building, deploying, and maintaining models. Being skilled in model deployment includes the ability to deploy models in real-time production floor data. 

Communication Skills 

This skill is highly important because as a data scientist, your role is often in-between other roles in the organization’s network. Since your work interdependently, you need to be able to communicate your findings effectively to people who often do not even have a technical background. This skill is highly important because they are the ones to work with the solutions you provide. If they cannot make any meaning of your communication, that solution is as good as non-existent.

Logical Thinking 

There is a lot of experimentation in the career path for data scientists, but there is a method of trial and error. If your thinking is not structured, your method will be misapplied and the outcome of your analysis will be flawed. The ability to think in a logical sequence and critically is pivotal to using data for problem-solving and market predictions.

Data Visualization 

There is only so much wordsplanation can do in data science. That is why visuals are a necessary feature of data reports. When used right, anyone can understand the message being passed by charts and graphs from analyzed data. There are several tools to hone your data visualization skills such as PowerBI, QlikSense, and Tableau. Some open-source Python libraries like Seaborn and Matplotlib also feature prominent data visualization tools. 

Hunger for Knowledge 

A data scientist is a lifelong learner. The dynamic nature of this field means that a sense of curiosity and desire to learn are handy skills and not just nice-to-have attitudes. Not only is the field relatively new, but is also evolving. A lot of things are not set in stone yet. Complacency towards industry-related news, events, and updates may get any practitioner outdated faster than laziness would. Learning new things and applying the knowledge is a skill that will keep you on the budding side of a data science profession.

Career Path for Data Science: What Are Your Options

There are different options in the data science career path and it is normal to be uncertain of what each role entails. However, you need to understand each data science career path to determine if it is suitable for you. Sometimes, there’s not much difference between the roles, especially at the entry level. But the more you specialize, the more one role will vary from another.

Data Analyst 

A lot of people enter into the data science field as data analysts. This entry-level role involves creating systems to collect data from different in-house and external sources and analyzing the data to derive useful insight for the organization. Most data analysts start their role as junior data analysts. A data analyst often has to collaborate with other departments of an organization to develop actionable insights.

Since this role often involves relating technical information to non-technical people, data analysts need excellent communication skills to get their job done. They also need to learn some programming languages like Python, SQL, and R. As a data analyst, you must always be ready to improve data systems through innovative data collation, analysis, visualization, and communication techniques.

Software Engineer

Software companies often have to work on their users’ data to build new products and improve existing ones. Software engineers often need to understand programming and data analytics to execute their job functions. If you love coding and data, you don’t have to adopt an either/or approach as you can marry the two together as a software engineer, depending on the company you work with. Software engineers typically work with data analysts on the back end to ensure that their product scales and remains secure.

Software engineers also need to understand some programming languages like Python, Java, C, and C++. They also need to develop experience in the following areas; deep learning, machine learning, debugging, communication, and other relevant skills.

Data Scientist 

Data scientists extract meaningful insights from data and weave a story with the data. They also create machine learning and deep learning predictions. Their job includes discovering data trends and patterns and visualizing data. The less technical aspect of a data scientist’s job includes developing marketing strategies from insights and diagnosing business problems in tandem with stakeholders.

While data scientists share a quite similar job function with data analysts, data scientists build models that ease the analyst’s job. Of course, a large part of data science is understanding raw data and discovering compelling insights from it, so data scientists are often good at analyzing themselves. A sound knowledge of programming languages like Python or R and advanced knowledge of Apache Spark is necessary for a data scientist career path. Data scientists also often have to be self-driven and highly motivated since they tend to work independently.

Machine Learning Engineer

Machine learning engineers compute data from data analysts using machine learning algorithms built by data scientists. They then obtain insights from the processed data that inform business decisions. Their core responsibility is to write the code, build models and deploy models for machine learning. The ultimate objective is to create artificial intelligence. Machine learning engineers need a strong understanding of programming languages like R, Python, C++, and Java as well as software engineering.

As a machine learning engineer, you will carry out many trials and errors, so you need to cultivate patience. You also need to be open to different ways of thinking so you can apply various approaches to achieve your goal. Moreover, you need excellent communication skills and a good understanding of various data structures. 

Marketing Analyst 

The marketing analyst analyzes company data and user research to create a marketing strategy for campaigns. Their goal is to develop effective methods to market the company and its products at marginal cost. The ability to adapt quickly is important for this career because market trends quickly and constantly change. Marketing analysis can involve SEO, growth hacking, or even email marketing. Programming languages for this role include Python, R, SQL, and Tableau. A marketing analyst should understand data analytics, have strong communication skills, and be able to think objectively.

Statistician

This data science role involves identifying trends and patterns in data and building solutions for them. They often apply these methods to solve real-life problems across various walks of life. Python and R programming languages are often used for statistical computing and data visualization.

Business Analyst 

The business analyst role is not primarily a tech-focused role, but they also analyze and develop actionable insight from data. They are specialists in business functions and processes. They use storytelling to communicate data as actionable insights throughout the company with the aim of solving business problems. Business analysts are often the intermediary between the rest of the organization and tech teams. They need to understand SQL or Tableau programming languages. They are skilled in business processes, data visualization, and data storytelling.

Data Architect 

Data architects determine the entire framework to manage data right from collection to consumption. create the framework that hosts the organization’s data management systems. It is the data architect that constructs everything data-related by discovering structural data problems and proferring solutions. The role of data architects is foundational to data processing operations and artificial intelligence.

Clinical Data Manager

Pharmaceutical and biotechnology companies need clinical data managers to oversee every data need related to clinical trials. With this role primarily domiciled in the healthcare industry, CDMs gather, process, and analyze data on a routine basis. They ensure that reports generated are updated, accurate and valid. Their job is also to ensure that their methodology complies with ethical standards as laid out by authoritative bodies like the Food and Drug Administration (FDA).

Big Data Developer 

Data science generally involves working with big unstructured data that cannot be handled with traditional data-processing applications. Big data developers work with petabytes and exabytes of data. Big data developers design, create and maintain technical tools and systems for big data applications.

Machine Learning Scientist

As a machine learning scientist, you are to discover new approaches to collect data and develop algorithms to be used for the adopted systems. Part of your job is to develop models for deep learning whether you are working independently or as part of a team.

Data Modeler 

These are computer systems engineers responsible for designing and deploying data modeling solutions. They use frameworks like relational, dimensional, and NoSQL for their job. They collaborate with data architects to build unique frameworks for their organization by combining different data models.

Business Intelligence Developer 

Their job is to design and create strategies to help business users discover meaningful insights related to their decision-making. They use business intelligence tools to help users understand the systems they built.

Career Path for Data Science: Setting Your Salary Expectations

Financial compensation for a data science career is really encouraging. While this career path is trendy and has even been labeled the sexiest job of the 21st century, it is also lucrative and promising. According to the US Bureau of Labor Statistics, the mean annual salary for data scientists is $159,630. The state of California earns the highest annual mean wage at $147,390 with New York and Texas following at $128,200 and $102,230 respectively. 

Conclusion

Data science is a field that welcomes professionals from all walks of life. Once anyone learns and practices the prerequisite skills necessary to be an effective data scientist, the journey has just begun. There is still a need to remain on top of industry trends and to keep practicing to maintain your hold in this field. Aim to build a solid foundation and the rest of your data science journey will be relatively simple.

There are endless job opportunities, impressive salaries, and opportunities for growth on this path. The important thing is to find out the area that sustains your interest and to keep building on it. The prospect is worth every investment you make to walk on your preferred data science career path.