In an era of technological advancements, the career of “Data Scientist” is gaining immense popularity. If you are interested in becoming a data scientist, it’s a good idea to understand what your job description might be. A data scientist is a professional who has expertise in statistical analysis, programming skills, and domain expertise (or understanding of a specific field and how data is gathered and used in that field). They use this combination of skills to extract valuable insights and patterns from vast amounts of data.

What Do Data Scientists Do all Day?

Data scientists are a little like modern-day detectives. They start by collecting large sets of structured and unstructured data from various sources. These data sources include surveys, databases, online repositories and API’s just to name a few. Next, they employ their analytical skills to clean and preprocess the data, removing –as much as possible– any inconsistencies or errors. Once the data is ready, they use statistical techniques, machine learning algorithms, and mathematical models to uncover meaningful trends and patterns. In a nutshell, data science is using data to make an impact.

As an example of what a data scientist might do to create impact, let’s look at the automotive industry so we can drill down to some specific examples:

Insight discovery:

An automotive data scientist might be interested in discovering what functions in a vehicle are most likely to create customer satisfaction levels that keep a car buyer coming back to the same make and model again and again. She might start with the assumption that the most important part of customer satisfaction is getting into a car every morning and having it start. Using data collected from multiple sources, she could create a model to show that one predictor of a car starting consistently is scheduled maintenance of the vehicle. Her data could provide insight to manufacturers and dealers that would encourage creating systems designed to increase the likelihood that a customer takes his car in for regular oil changes and tire rotations.

Creating data products:

Maybe our data scientist is interested in creating data products instead. She might use her expertise to create a personalized recommendation system for car buyers. By analyzing customer preferences, historical purchase data, and demographic information, a data scientist can develop algorithms that provide tailored recommendations for prospective buyers. These recommendations could include specific vehicle models, features, or options that align with the customer's preferences and needs, increasing the likelihood of customer satisfaction and purchase conversion.

Making product recommendations:

Finally, data scientists can become skilled at using data to create product recommendations. One example might be a personalized vehicle insurance plan. By analyzing a combination of factors such as driving behavior, vehicle usage patterns, and demographic information, data scientists can develop algorithms that generate customized insurance recommendations for individual drivers.

What are critical skills for a data scientist to develop?

Apart from their technical expertise, data scientists possess strong problem-solving abilities. They are adept at formulating research questions and designing experiments to find answers. They also have a keen eye for detail and possess excellent communication skills, enabling them to effectively convey complex findings to non-technical audiences, business decision-makers, and more.

To become a data scientist, one must have a solid foundation in mathematics and statistics, along with programming skills. Proficiency in programming languages such as Python or R is essential, as these languages offer powerful libraries and tools for data manipulation and analysis. Staying up-to-date in their knowledge and working hard to stay abreast of latest advancements in the field is also a given, since technology evolves rapidly.

Distinguishing Between a Data Analyst and a Data Scientist

Although the terms "data analyst" and "Data Scientist" are sometimes used interchangeably, they represent distinct roles within the world of professionals. While both work with data, their responsibilities and skill sets differ.

A data analyst primarily focuses on examining data to identify trends, draw conclusions, and support decision-making processes. They analyze structured data using statistical methods, generating reports and visualizations to present their findings. Data analysts are proficient in tools like Excel, SQL, and Tableau. A critical skillset is the ability to efficiently extract, clean, and analyze data.

On the other hand, a data scientist is involved in more complex and strategic tasks. They possess advanced knowledge of machine learning algorithms, data modeling, and predictive analytics. Data scientists design and build models that can make predictions and drive actionable insights. They are skilled in programming languages, statistics, and data visualization, enabling them to develop sophisticated algorithms and communicate their findings effectively.

While data analysts focus on providing descriptive and diagnostic analysis, data scientists go a step further by offering predictive and prescriptive insights. Data analysts work collaboratively with data scientists, providing them with the necessary input and understanding to develop advanced models and algorithms.

Data scientists play a crucial role in today's data-driven world. They possess a unique combination of technical skills and analytical thinking, allowing them to extract valuable insights from large datasets. Highly curious, and possessing superb communications skills and an impressive inner store of grit, a data scientist is an individual with a unique skillset—one that employers seek out and compensate well.

How Do I Start Making Big Bucks as a Data Scientist?

If you want to become a data scientist, there are several key areas of education you should consider to build a strong foundation. You'll never be "finished" learning as a Data Scientist, but here are some foundational things you'll need to develop skill in before you start looking for your first great job:

Mathematics and Statistics:

Data science heavily relies on mathematical and statistical concepts. Strengthening your understanding of calculus, linear algebra, probability, and statistics. These subjects form the backbone of many data science algorithms and techniques.

Programming:

Proficiency in programming is essential for data scientists. We recommend you begin by learning Python. It’s a critical first step, and the reason Temzee training starts with a beginner Python course. This language has extensive libraries and frameworks specifically designed for data analysis and machine learning.

Data Manipulation and Analysis:

Familiarize yourself with data manipulation libraries like pandas in Python or dplyr in R so you canlLearn how to work with data effectively. You’ll need to know how to clean and preprocess data, handle missing values, and perform exploratory data analysis. Machine Learning: Gain a solid understanding of machine learning algorithms and techniques.

Data Visualization:

Learn to use libraries like matplotlib, seaborn, ggplot2, or Tableau to create informative and visually appealing visualizations. Developing these skills is critical to visualizing data to extract meaningful insights.

Databases and SQL:

Data scientists often need to retrieve and analyze data stored in databases. Learn SQL (Structured Query Language) to perform efficient data querying and manipulation.

Big Data Technologies:

Big data tools and frameworks like Apache Hadoop, Apache Spark, and cloud-based platforms like Amazon Web Services (AWS) or Google Cloud Platform (GCP) will be your data playground, so you’ll want to understand how they operate. These technologies are commonly used for handling and processing large-scale datasets.

Do Projects:

Practice is crucial for developing your data science skills. You’ll need to start finding ways to apply your knowledge in real-world scenarios. Work on personal projects or participate in online competitions and platforms like Kaggle to gain hands-on experience.

Continuous Learning:

Stay updated with the latest developments in the field of data science. Follow blogs, and pay attention to important data scientists on Twittter or other social media platforms. You’ll gain lots of insight as you attend webinars, join online communities, and explore resources like research papers, online courses, and books. In short, apply your sense of curiosity and poke your head into all of the possible dark holes to see what’s lurking in there. That instinctive curiosity is at the core of what makes you a great data scientist.