Things You Should know for a Career in Data Science

Last updated on December 21, 2022

Data science continues to be the buzzword and a career in data science is very exciting and rewarding at the same time. A data scientist does not only have the prospect of a high salary but also has the opportunity to impact businesses directly. The job is quite thrilling in itself. Presently, the data science market is worth $38 billion with lucrative salaries being offered to top data scientists. It is expected to grow even more in the coming years and there will be even more demand for skilled data scientists.

The applications of data science are immense and it spans across industries from customer behavior analytics in e-commerce and retail to computer vision applications like classification and detection of humans and objects. Data science is currently omnipresent and things will be even more exciting in the coming years. The field is still relatively at a nascent stage.

If you want to embark on a journey to a data science career, there are a few things you should know. Knowing these things helps you understand what you can expect from the field and how you can build a thriving career.

What is Data Science?

It is all good to be excited about data science but you should always have your basics clear. You might have read different definitions of data science but you might need a little explanation. Data science combines computer science, statistics, and particular domain knowledge.

The general fundamentals of computer science and statistics can be understood well through studying and practicing. However, gaining domain knowledge takes considerable time, effort, and research. You don’t necessarily need to have a mastery of all the verticals of data science, but having a good understanding of all will be helpful in the long run.

The field of data science is quite vast in itself. It includes everything from basic data reporting to advanced predictive modeling with the use of AI. Data science techniques that deliver more business value typically tend to be more complex.

Role of a Data Scientist

A data scientist’s role is quite expansive and depends vastly on the kind of project you work on. The exact role a data scientist plays in an organization depends on how mature their data initiatives are. For a typical data science project, here are the general role data scientists play:

●      Problem statement understanding:

While this might sound simple, it is far from it. Understanding the problem statement is extremely critical and the entire project depends on it. The concerned team and data scientists go over the requirements and objectives of the project. This step requires good stakeholder management and communication skills. Any good data scientist would be willing to spend a considerable amount of time at this step.

●      Collection of data:

After obtaining the requirement and forming the hypothesis, data scientists are then required to mine the data. The data source can be through web scraping, data warehouse, etc.

●      Data cleaning:

This step in a data science project consumes the most time. The data scientists need to munge, manipulate, and wrangle the data. They deal with outliers, correcting data types, missing data values, and several other operations. The effort and time required in this step are worth it since healthy data reflects a healthy output model.

●      EDA:

Exploratory Data Analysis is the stage in which features in the dataset are analyzed to check their behavior. Significant data visualization is involved in this step. Crucial insights can be gained at this step that will be helpful later on.

●      Feature engineering:

This process is iterative in which data scientists go through the features one by one and apply operations for improving the model’s performance. It involves trial and error.

●      Model building:

While this step is fast, it requires proper planning. All considerations will be laid out and recommendations from data commercialisation and  analytics will be given great importance in building a strategic structure. The model-building strategy needs to be evaluated.

●      Deployment:

After building and evaluating the model, the final step is to deploy it. Data scientists usually work with machine learning engineers or data engineers at this step.

Prominent roles in Data Science

There are lots of roles within the vast field of data science. You should be diligent about different roles before you get into the field. Some major data science roles you must know of include:

●      Data scientist:

These professions work on particular and complex problems to help the company grow non-linearly. This includes using vehicle images for automatically assessing the damage for insurance companies and making credit risk solutions for the banking industry.

●      Data engineer:

This role involves implementing the outcomes that data scientists derive in production with the use of best practices in the industry. This includes the deployment of a machine learning model for credit risk in banking software.

●      Business analyst:

These professionals assist the business in running smoothly by helping the management take day-to-day decisions based on data. This role involves simultaneous communication with the IT and business end of things.

Several other roles exist in the field such as data analyst, data analytics manager, statistician, BI professional, MIS professional.

What do you need to get started?

The best way to get started is to enroll in data science training in Chennai. Here’s what you can learn.

●      Get acquainted with data science:

At the beginning of your data science journey, you need to get accustomed to the different terms associated with data science, the role of data scientists, and try getting more familiar with Python.

●      Mathematics and statistics:

These are what form the core of data science. Some key concepts you need to cover include inferential statistics, probability, and getting an idea of how EDA is performed. You should also learn linear algebra basics.

●      Basics of machine learning:

You may need to be introduced to the basic techniques and algorithms of machine learning, including logistic regression, linear regression, SVM (support vector machines), Naive Bayes, decision trees, etc.

●      Ensemble learning:

Once you get a hang of the basics of machine learning, you have to know about the advanced topics in machine learning. Know what is ensemble learning and the different techniques used. You can gain some practical experience by working on datasets.