How to Become a Data Scientist

Nowadays, the importance of data analysis and its professional interpretation is increasing. Thus, each big company and many small start-ups are hiring data scientists to help businesses take the most from information available. So, if you are still evaluating the career path you want take in the future, you should consider this profession.

Let’s dive into who a data scientist is, functions such a specialist has and knowledge you need have to be an efficient one. This article will uncover almost everything you need to know to enter the world of working with data successfully. 

Who is a data scientist?

Shortly, data science can be defined as the process aimed at transforming raw data into useful information and finding patterns and trends. Thus, a data scientist is an expert who has the technical skills needed to solve complex data-related problems. He/she performs automatical processing of large datasets (usually from multiple sources) to perform the process of quantitative or qualitative data analysis. 

The main aim of this work is to provide insights that are helpful for decision-making. The task of a data scientist is to transform all the data into an understandable and manageable format for the end-user. 

Next, let’s take a look what are some common day-to-day tasks of a data scientist. 

Data scientist responsibilities

The main task of such a person is a qualitative and quantitative analysis of data for decision-making. 

Usually, companies need data scientists to be able to predict consumer behavior and identify ways to generate income through data processing. 

Data scientists are particularly in demand for fast-growing companies or companies looking to get new markets. As Camila Manera said, “data helps us analyze trends, build correct solutions and expand businesses.” 

Here is an example of responsibilities from a real job posting for a junior data scientist position (Company: SHI International Corp., business consultancy services):

  • Directly participate in the delivery of data-driven research projects that have a direct impact on SHI’s business
  • Contribute to highly visible, complex projects, programs, or solutions at direction of Lead Data Scientist
  • Explore the space of analytical solutions for a given problem and execute with a sound strategy
  • Acquire, analyze, and act on complex, high-dimensional data sets using appropriate statistical/machine learning approaches
  • Gather and integrate data; create ETL (Extract, transform & load) jobs
  • Explore, inspect, and clean data; engineer features
  • Train, validate, and test statistical/machine learning models
  • Deploy and maintain statistical/machine learning model-based applications and services
  • Participate in meetings with internal stakeholders to present and discuss project objectives, timelines, progress, and outcomes
  • Maintain up-to-date working knowledge in relevant areas of data science, statistics, machine learning, and artificial intelligence
  • Prepare documents & presentation materials for leadership as necessary for approval & buy-in
  • Develop analytic requirements and collaborate with your client, analytics team leaders, and Epsilon Product Engineering teams to integrate analytic solutions into Epsilon’s product platforms and your clients’ product platforms.

What is a common process of processing data by a data scientist?

If you have no idea how a process of data analysis usually looks like, here is a samle a common sequence, which can be summarized in the 5 following steps.

  • Extracting the data from the main source that can be Internet sources, documents like CSV, APIs, etc
  • Cleaning the data to be able to process it on the next steps
  • Finding the most efficient methodology or designing a new one to process the data
  • Processing the data using statistical methods 
  • Making conclusions based on this analysis and presenting the results in an understandable form.

Average Data Science Salary

Below is a median salary based on the level of proficiency (According to the 2018 Burtch Works study).

  • Junior Data Scientist – $90,000
  • Middle Data Scientist – $130,000
  • Senior Data Scientist – $170,000

Among the related roles with a similar salary level are a data analyst and big data engineer.

Requirements for a data scientist 

Becoming a data scientist is not easy and the main reason is a set of technical skills a person must have as well as the peculiarities of mindset. Let’s list the main skills that are required for such a person.

Skills a data scientist needs to have: 

  • Math and statistics. The main knowledge needed are multivariate calculus and linear algebra, statistical methods, probability theory, etc.
  • SQL. This programming language is needed to organize all the data in one place and then being able to easily use it.
  • Python. It is the most broadly used programming language for such tasks. The main reason is that Python has a great number of libraries for different data-related tasks (like Numpy, Pandas, SciPy, sci-kit-learn, Tensorflow, Pytorch).
  • R. This programming language has limited usage and its main function is related to statistical data analysis. 
  • Excel. Data analysis in Excel is not usually used at an advanced level, but it is also sometimes needed to prepare data for the end-user.

Moreover, a person is usually required to have a degree in Computer Science. However, often people with a certification from a data science bootcamp are also considered procured candidates.

Here is an example of requirements from a real job posting (Company, Epsilon, outcome-based marketing services):

  • Bachelor or Master’s degree in Computer Science, Statistics, Electrical Engineering, Mathematics, Economics, Physics, or a related scientific discipline
  • At least 3-5 years of hands-on relevant work experience in data science application
  • Mastery of the following programming languages Python, SQL; Knowledge of R
  • Experience manipulating large data sets, both structured & unstructured
  • Ability to identify, join, explore and examine data from multiple disparate sources and formats
  • Ability to reduce large quantities of unstructured or formless data and get it into a form in which it can be analyzed
  • ability to deal with data imperfections such as missing values, outliers, inconsistent formatting, etc.
  • A natural curiosity for exploring data and finding the untold story
  • Desire to work in a highly collaborative environment
  • Ability to simplify complexity, communicate clearly and teach others
  • Manage multiple data science projects concurrently
  • Experience with common data science toolkits such as Numpy, Pandas, SciPy, sci-kit-learn, Tensorflow or Pytorch
  • Strong statistics skills such as distributions, statistical testing, regression, etc.
  • Familiarity with marketing analytics, especially digital analytics and media analytics

Data scientist vs data analyst

An additional aspect to discuss within this article is what is the difference between a data scientist and data analyst, as those roles might seem very similar.

Both professions indeed have a lot in common; particularly, both specialists are usually responsible for collecting information, analyzing it, identifying patterns, and providing meaningful insights based on that.

However, a data scientist has more advanced functions as he/she needs to work with more complex data sets by using programming tools. Meanwhile, a data analyst can work with information without coding skills.

Moreover, a data analyst spends a lot of working time on creating dashboards and reports and present it to managers, stakeholders, clients, marketing specialists, or whoever needs it for making a data-based decision.

What to study to become a data scientist?

The traditional way to getting a data science is to obtain a degree in Computer Science, Math, or Statistics. 

However, today more and more young people opt for a shorter and more affordable alternative, which is online courses and bootcamps. They usually take about 6 months of intensive learning and are focused mainly on the needed data science skills.

Check out this list of best online data science courses.

Final thoughts

Data science is a challenging but very promising profession that would not be a good fit for everyone. If you feel that this is what need as your dream job, do additional research and start the learning path! 

Stacey M.
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