How to Build an Easy Data Science Career Path

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Data science is one of the fastest-growing fields according to a survey conducted by LinkedIn in 2017. Glassdoor and PriceWaterhouseCoopers ranked the data science profession among the best careers in the United States and Europe based on the package it commands and the number of job openings.

This market research is proof that a career in data science is not only rewarding but also highly in demand. Well, it is also not a smooth sail. Among data science professionals, there is a distinction between those with a data science certification and those without.

The former is more recognized and sought after compared to the latter, which is proof of how well recruiters value certifications. 

How is Data Science growing? 

Data science has grown in scope beyond big data. Its multidisciplinary nature is today coming out clearly as it becomes important in all sectors. The scientific approach of manipulating data has become critical to solving complex problems and decision-making in enterprises. Data scientists analyze data to extract insight from huge data sets, both structured and unstructured. 

For this reason, the data science field has experienced incredible job growth and is expected to escalate even further in the years ahead. The U.S Bureau of Labor Statistics has estimated an increase of jobs in data science by 11.5 million by 2026.

With data scientists having an average annual salary of $120.931 according to Glassdoor report, opportunities abound in data science roles like machine learning engineer, data scientist, sales development representative, and big data developer. 

What is Data Science?  

Today, nearly every interaction with technology generates data. For instance, a purchase on Amazon, shipping from overseas, or interacting with your favorite social media platform.

Even Google will not only remember what you have bought but also keep your search history. This allows Google to customize content and advertisement to fit your needs.

Data science benefits companies as well as individual servers. Mckinsey Global Institute found retailers that use big data to have an increased profit margin by up to 60%.

Data science uses past and current data to predict future performance or outcomes. It consists of a set of practices that guide the analysis and interpretation of data so as to solve business problems.

These practices include but are not limited to algorithms of statistics, database management, programming, and machine learning. Data science has found its application in most industries including healthcare, finance, manufacturing, and sales and services.

Information drawn from data is used to provide organizations with insights that will help them form business strategies and gain a competitive edge.

Career Path for Professionals Willing to Launch Their Career in Data Science 

Data science might be a highly in-demand skill but it is certainly not the easiest to master considering that it requires a wide range of skills, some very specific. Still, it is a fact that acquiring data science skills will boost your career big time. Here are some tips to guide you along. 

  1. Establish your interest

Data science is broad in itself. The first and most important step is to figure out the data science role that interests you. It may not be very clear at first therefore consider going for the more general roles. Still, keep an eye on the rewarding roles in the industry and see if there is anyone of them that tickles your interest. Decision-making is a process and it may take you some time to decide so don’t feel compelled to make a decision right at the start. 

Here are some of the most promising data science careers that you can aspire to pursue:

  • Data Scientist: A typical day in the life of a data scientist involves finding, cleaning, and organizing data for businesses. It involves analyzing large volumes of complex data sets to establish hidden patterns that can be used to make strategic business decisions.
  • Machine learning engineer: These professionals create data funnels and provide companies with software solutions. It requires good statistics and programming skills that come handy when designing machine learning systems.
  • Application Architects: This role involves tracking the operation of systems and applications used in business and ensuring that they are working in sync to achieve business goals. Application architects design the architecture by building user interfaces and the overall infrastructure of applications.
  • Enterprise Architect: These experts are charged with the responsibility of aligning an organization’s strategy with the technology required to meet organization goals. This requires a complete understanding of an entire business structure and its technology.
How to Build an Easy Data Science Career Path 1
  1. Build your skillset 

The data scientist skills arsenal includes common technical skills and technical skills directly related to data analysis. Some core skills you should consider when just starting your career include: 

  • Programming languages. Most if not all technology roles require knowledge of programming languages, it is a given. For data science, Python and R would be the most appropriate languages to learn in addition to learning how to navigate libraries like Pandas and Scikit-learn and tools like MATLAB and TensorFlow which are commonly used in data science. 
  • Database management. Data science is about data and being familiar with both relational and non-relational database management systems is essential. Knowledge of  SQL, ETL, Hadoop, Apache Spark, and other tools will come in handy. 
  • Data visualization. Supports the conveying of information and insights from data analysis using graphs, images, and even text. It is important to learn programming skills and be familiar with tools like Tableau, Raw, and Cognos to be able to visualize data. 
  • Data wrangling. Data wrangling involves the processes that data goes through before it goes into the analysis phase. These include processes like acquiring, transforming and mapping raw data, and then cleaning it. 
  • Statistics and probability. These two skills are important for exploring data and determining patterns, relationships, trends, as well as identifying anomalies. They are also used in forecasting and predictive analysis. 
  • Prototyping. This is the process of building a model for a system or application. Data scientists need to ensure any application that is developed reflects what is expected and can provide the required solutions.
  • Machine learning. This is also a core data skill as it uses algorithms to establish patterns in a given set of data. Mastering algorithms of statistics, python, and ML is a requirement in machine learning. 
  • Communication. To be able to present insights from data analysis in a meaningful form that is easily understood and make recommendations is an invaluable skill for any business-oriented data scientist. 
  1. Earn relevant certifications 

Consider taking a system-specific certification training to gain relevant skills required for an entry-level position in data science. Earning a certification is an important self-preparation decision. Consider the following certifications to validate your competency in the core data science skills include:

  • Data science with Python 
  • Data Science with R
  • Microsoft Certified Solutions Expert (MCSE) 
  • IBM Data Science Professional 
  1. Take up an entry-level position when you get the chance 

This is where most people begin if not in an internship position. If the opportunity arises, go for it! Note that pay packages range for different junior positions based on the company that is hiring, one’s qualifications, and most certainly one’s experience. 

The roles of a data scientist involve looking at available data in the company to answer some business questions. For instance, one may be asked to analyze a company’s sales data after a recent market campaign to assess its effectiveness. While the required skills vary from position to position, generally, one has to be competent in:

  • Intermediate level programming using R or Python, and other popular packages
  • SQL queries skills
  • Data cleaning and visualization
  • Use of statistics and probability
  • Good communication skills to understand and explain the process to people with no technical background in Data Science.

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