Content Developer Associate at almaBetter
There is a global shortage of skilled Data Science professionals, creating a golden opportunity for beginners, graduates, and working professionals to build a career in Data Science. While it is not easy to switch your career to Data Science, with proper guidance, one can seriously consider a career in the field. This blog is a detailed guide for anyone looking to switch their career to data science.
Data Science is a constantly evolving field that offers several fascinating roles. Hence, professionals must learn and adapt new-age technology to utilize data. Once you switch your career to Data Science, it is essential to select a suitable position, as different roles have different responsibilities and demand diverse skills. Let’s have a look at the most popular Data Science roles.
Let’s begin with the most famous role: Data Scientist. As a Data Scientist, you will be responsible for understanding the challenges of the business and providing the necessary solutions by using data processing and data analysis techniques. In other words, a Data Scientist is the master of all trades and offers several different services, such as the automation of data collection and management processes, cleansing, processing, and integrating data, and collaborating with engineering, business, and product teams.
The role of a Data Analyst is quite similar to that of a Data Scientist and sometimes overlaps. Data Analysts are responsible for several tasks, such as transforming, visualizing, and manipulating data. Moreover, Data Analysts are also responsible for optimization as they regularly create and modify algorithms that compress information from big databases without corrupting the data. Since Data Analysts are masters in visualization, they are also sometimes required to prepare data for business communications.
As the name suggests, Data Engineers are responsible for designing, building, and upholding data pipelines. Data Engineers work closely with Data Scientists. Data Engineers are responsible for testing ecosystems for organizations and preparing them for Data Scientists to run their algorithms. Moreover, Data Engineers must handle batch processing of newly collected data and match its format with the stored data.
Machine Learning Engineer
Machine Learning Engineering is one of the most desired roles in Data Science. Therefore, Machine Learning Engineers are also in high demand today. The role of a Machine Learning Engineer is quite challenging but rewarding. They are responsible for working around machine learning algorithms such as clustering, classification, and categorization.
In addition to designing and building machine learning systems, Machine Learning Engineers must run tests (such as A/B tests) while observing the different systems’ performance and functionality.
The role of a database administrator is self-explanatory; they are responsible for the proper and fair use of databases. Database Administrators have the authority to grant or revoke the services of databases. Moreover, they are also responsible for database backups and recoveries. Another critical responsibility of a database administrator is to implement security measures for a database.
If you are planning to change your career to Data Science, the role of a Data Scientist is one of the most common and exciting roles to choose. Data Scientists work with a massive amount of data, with several challenges and responsibilities. Let’s have a look at what the life cycle of a Data Science project looks like and what are the roles and responsibilities of a Data Scientist.
Recognizing and understanding the problem
As simple as it sounds, the first step of a Data Science project is to analyze the data and understand the problem. Data Scientists and their teams can speed up the process by tackling the situation once they find the cause of the problem. A skilled Data Scientist will thoroughly go through the objective and expected requirements and spend ample time on this process to achieve the desired results.
Once the requirements have been updated through the initial process, Data Scientists start working on collecting the needed data. Data sources can vary from the company data warehouse to web scraping and more.
Data cleaning is the most time-consuming process in the life cycle of a Data Science project. A Data Scientist will manipulate, mug, and wrangle the data in this process. This is an essential step and the time consumed during it is worth it because the health of your data will directly impact the output model.
Exploratory Data Analysis (EDA)
In this process, a Data Scientist analyzes each feature or multiple features in the dataset to check how they behave. Sometimes they also examine the relationship between one feature with other elements. A skilled Data Scientist will also be ready for a lot of visualization and be prepared to gain crucial insights before moving on to the next phase.
Some Data Scientists call this art not a step. Feature Engineering is basically an iterative process where a Data Scientist examines all the features one by one to apply operations to improve the model’s performance. This is a tiring process as it requires a lot of trial and error. For example, a Data Scientist can merge powerful features to improve the model’s overall quality.
Building the Model
One of the most important yet relatively faster processes is model building. This stage is all about deciding according to your needs. For example, do you want a model that can return the importance of features? Or a model with high accuracy? A skilled Data Scientist will always have a model-building strategy in mind before reaching this step.
The ultimate goal behind any Data Science project is to deploy an error-free model in the real world. Once you complete the model-building and evaluation process, the next step is to deploy the model. During this process, a Data Scientist usually works closely with Data Engineers or Machine Learning Engineers.
As mentioned earlier, Data Science is a vast and challenging field, so it obviously requires a wide range of skills. The skills we discuss are the fundamentals you need to learn to excel in any Data Science and Analytics role. Technical skills are extremely important to succeed in this exciting field. Let’s examine the technical skills you need to start your Data Science career.
Mathematics & Statistics
A stronghold on the fundamentals of mathematics will help you in many ways in your transition to Data Science. Mathematics is an infinite subject, and it is almost impossible to learn everything. However, learning a few subfields might make your Data Science journey easy. To have a successful career in Data Science, one must be proficient in Linear Algebra, Differential Calculus, Descriptive Statistics, and Inferential Statistics. They will help you understand complex deep learning and machine learning concepts.
As a Data Science professional, one must be proficient in programming as it will help you deal with a large number of data sets and easily visualize and model the results. There are several programming languages to learn. However, a few are an absolute must if you want to switch your career to Data Science. Python and SQL are the most popular and valuable languages you should learn before entering Data Science.
Data Science professionals utilize the power of Machine Learning to attain a better understanding of the data and automate decision-making in real-time without any human intervention. Machine Learning is where computer science meets mathematics and helps Data Science professionals to work on large data sets. Therefore, Machine Learning is essential as it is used to understand data and extract insights.
Understanding business intelligence is essential as it combines data mining, data visualization, data tools and infrastructure, business analytics, and best practices to help businesses make more data-driven decisions. Some essential tools for business intelligence are SQL, Tableau, Microsoft Power BI, and Oracle Fusion.
A massive misconception about Data Science is that you only require technical skills to excel in this field. However, soft skills are as necessary as technical skills if you want to build a career in Data Science. Soft skills are essential for Data Science professionals as they will help them collaborate with different departments and make valuable contributions. Let’s examine the soft skills you need to start your Data Science career.
Knowledge of computer science and statistics can be achieved through studying and training. However, if you plan to build a long and successful career in Data Science, you need to learn problem-solving skills and refresh your domain knowledge regularly. These skills are going to help you in the long term.
A Data Science professional works with different teams and must have good communication skills to convey their message to other departments and stakeholders in layperson’s terms.
A good Data Science professional can structure their thoughts and map them. Structured thinking comes in handy during the initial steps of the project, where the hypothesis is prepared.
A critical skill that all Data Science and Analytics professionals must have is the ability to express the data in a format understandable by the stakeholders – a story. It is this step that requires creativity.
This section will look at the tools you need to master before making a career transition to Data Science. There are several tools in the field of Data Science; however, these are the ones that every Data Science professional must learn to have a smooth career.
Excel is the most popular and accessible tool for recording and handling small amounts of data. Knowledge of Excel is necessary for Data Science professionals as this tool is efficient for day-to-day use and quick tasks.
SQL is another popular tool in the Data Science field. SQL is one of the oldest and most popular data management systems. SQL remains one of the preferred data management tools, and it is essential for Data Science professionals to have a good understanding of this helpful tool.
As mentioned earlier, Python is one of the most dominant and widely used programming languages. In addition, Python is easy, flexible, and open-source, making it the most used programming language in Data Science.
It is an essential tool for Data Science professionals as it allows them to handle large amounts of data. Tableau is preferred because of its clean dashboard and story interface.
Learning Data Science is challenging and sometimes daunting. However, one can easily learn more about Data Science with proper guidance. There are several courses and programs available. However, AlmaBetter’s Full Stack Data Science course is one of the most structured, robust, and industry-relevant programs with pay after placement model. The program is perfect for professionals trying to switch careers to Data Science. Let’s look at the unique features of this program-
Data Science is a challenging yet rewarding field. If you want a successful and long-lasting career, switching to Data Science is undoubtedly a good decision. The critical thing to consider is the challenges that come while learning Data Science. Enrolling in a structured master's degree such as AlmaBetter’s Masters in Data Science program will help you throughout the rest of your learning journey and even help you secure your desired job.
1. Is switching to Data Science worth it?
Yes, if you are a tech enthusiast and want to build your career in a tech field, then Data Science is a rewarding career with several roles to offer.
2. Is Data Scientist a successful career?
The role of a Data Scientist is also known as the most desired job of the 21st century. Data Scientists are in demand as organizations are utilizing data for better results. A Data Scientist can expect an average salary of Rs10 lakh per annum in India.
3. How can a beginner become a Data Scientist?
Anyone with a technical or non-technical background can become a Data Scientist with the help of robust Data Science programs such as AlmaBetter’s Full Stack Data Science program. You can also start learning about Data Science through free learning resources.
4. Can I become a Data Scientist with no coding experience?
While Data Science requires coding, one can quickly learn coding through free resources or programs before entering Data Science. There is no need for prior coding experience.
5. Do you need Python to be a Data Scientist?
Yes, Python is a necessary programming language for Data Scientists, and one must be proficient in Python to have a long and successful career in the field.
Read our recent blog on “How Alpine’s CIO integrated Data Science into the F1 racing team”.