What Can You Do After Data Science Training?

Data is ruling the modern world. It’s created and collected every single second for recommending you products and Binge-worthy content online. Companies across the sectors are using data to make crucial decisions regarding product development, future expansion, investments, and improving existing products and services.

But how is data helping businesses? Data in its raw form is nearly meaningless and of no use. To derive meanings from it, it needs to go through a process of extraction, sorting, cleaning, analyzing, and finally interpretation. This process requires trained professionals who can help make sense of the data.

Data science training can help you extract insights from the data. It’s a starting point for all aspiring data scientists. It focuses on advanced programming, data analysis, data manipulation, data mining, data exploration, machine learning algorithms, Statistics, and deep learning skills.

In addition, it teaches about predictive data modeling and data processes. So, the training will help you learn how to analyze data and understand its hidden meanings.

Critical Skills For Data Science Graduates

Critical Skills For Data Science Graduates

In a data science bootcamp, you will get proficient in a broad set of skills that will make you eligible for numerous roles in the field of data science. Here are some skills you should learn.

  • Python Programming: It is a popular programming language for data science, used in data mining and data analysis. It has numerous useful libraries and frameworks like Pandas, MatPotlib, Seaborn, NumPy that make it suitable for reading, aggregation, data manipulation, and visualization.
  • R programming. R is a language specially made for data science. It is a collection of software facilities suitable for calculation, data manipulation, and graphical display. It can help implement machine learning algorithms quickly and allows for numerous statistical and graphical techniques, such as clustering, linear and non-linear modeling, classical statistical tests, time-series analysis, and classification.
  • Machine learning and AI: Machine learning models can automate various data scientist’s tasks while algorithms and data-driven models help simplify the data analysis process. You should learn about linear regression, supervised and unsupervised machine learning, regression, hierarchical clustering, and more.
  • Data Manipulation: You can’t use data in its raw form; so, to use it, you need to clean and transform it into a format suitable for analysis. During the best data science training, you will learn about data manipulation steps such as sorting, filtering, merging, appending, derived variables, etc.
  • Data visualization: Data visualization means representing data in the form of graphics, charts, infographics, maps, and more. It lies in between technical analysis and visual storytelling. It’s an important tool for businesses that help make sense of large volumes of data generated every day. Learn about tools such as d3.js, ggplot, and Tableau used to visualize data.
  • Statistics: Every data scientist should be familiar with statistics and probability concepts as it is crucial for creating high-quality models. Therefore, you should know concepts like linear regression, random variables, hypothesis testing, probability distribution functions, and more.
  • Deep Learning: It is a subcategory of Machine Learning and an important concept to learn. It requires excellent Python programming skills and a good grip on mathematics and linear algebra. You should also be familiar with deep learning libraries like TensorFlow, Keras, and PyTorch.

Key Career Options for Data Science Graduates

After graduating from the best data science bootcamp, you have abundant career options to choose from. Let’s look at some common career paths data science graduates take.

  • Data scientist: This is the primary role you can take after graduating from a However, the responsibilities may vary depending upon the industry and business type you’ve chosen. You will be responsible for building and designing the data analytics system for data insights. In addition, you will work together with the stakeholders to help them make informed decisions and reach the business goals effectively. Data scientist are paid an average $117,170/year.
  • Data Analyst: This is a perfect role if you are just entering the data science field. Your role will be to collect, process, and perform data analysis on large datasets to help companies make better business decisions. You will be responsible to transform data into readable form, creating reports and presentations. The average salary of a data analyst is $69,507/year.
  • Data Architect: In this position, you will be responsible for creating a blueprint for the data management systems to centralize, integrate, maintain and protect data sources. It’s an advanced role that requires you to visualize, design, and formulate data in a framework to be utilized later by data scientists, data analysts, and data engineers. You can start with an average salary of $118,868/year.
  • Data Engineers: You will be responsible for building data pipelines to collect, process, and store data from various resources. It’s a technical job where you prepare the data for analytic and operational uses. You will need to create and maintain an analytics infrastructure to help every data function. The average salary for data engineers is $112,413/year.
  • Machine Learning Engineer: It’s a job role close to both software engineering and data science. So, you will be tasked to build and design a self-learning machine to automate predictive modeling. You will have to leverage huge datasets to create and develop algorithms capable of self-learning and to make predictions. The average salary is $130,951/year.
  • Business Intelligence Analyst: You can become a BI analyst and help businesses understand where they stand amongst their competitors. You will need to dig deeper into the data, review competitors’ business trends, and help enterprises make decisions to improve their products and services and reduce costs. The average salary of a BI analyst is $85,677/year.
  • Statistician: You will use mathematical techniques to analyze and interpret data for solving real-world problems. Your job will be to gather, analyze and interpret data to help businesses. You can choose to work as a generalist, but specialization within a specific field will help you stand out to potential employers. The average salary of a statistician is $88,982/yr.

So, you can see there are numerous career paths for data science graduates. You can pick a profession based on your strengths and passion. Also, you can choose to work in the industry of your liking. Data science professionals are demanded in healthcare,  finance, banking, manufacturing, retail, security, and other data-reliant industries.

Conclusion

As data science is adopted by more and more organizations, the demand for data science professionals has soared. According to the US Bureau of labor statistics, the job opportunities in data science will increase by 15 percent. Jobs in this field attract with handsome paychecks and excellent career growth.

The multi-dimensional qualities of data scientists make them valuable in various domains.  Many professionals doing well in other areas like programming feel excited to be a part of this trending profession. In fact, on Glassdoor, it ranks in second position in the top 50 best jobs in America in 2021.

However, as mentioned, it does require some specialized training to be able to understand and analyze data. A data science bootcamp can help you learn the requisite skills and advance into a career as a data scientist. If you pair your bootcamp training along with a portfolio of capstone projects and assignments, you are more likely to land a great position.

Ready to get started? SynergisticIT’s data science bootcamp is the way forward to launch your data science career.