- Increased demand for data scientists also continues in 2021
- Understand what it's like to be a data scientist in 2021
From big e-commerce companies like Amazon, Walmart, to the social media giants Facebook and Snapchat, to hospital administration, Everyone is hiring data scientists! But, What is it that makes this role the “sexiest job role of the 21st century”? We will discuss each and every aspect of this work in this article..
If you are someone who is excited about this job and wants to create a future in this field in 2021, This is the place to be! Don't worry if you think the coronavirus has removed the job requirement for a data scientist!, However, has made everyone realize the power and importance of predictive algorithms!
If you are starting your journey in the field of data science, It is comprehensive data science learning path for 2021.
the learning path for 2021 is the definitive and most comprehensive collection of resources put together in a structured way. This learning path is for anyone who wants to pursue a career in data science. Then, whether it's new, have a few years of work experience or are a mid-level professional, It is data science learning path is for you.
Table of Contents
- Who is a data scientist?
- Other data-driven roles
- Qualities of a data scientist
- What skills to master in 2021 to become a data scientist?
- Salary of a data scientist
Who is a data scientist?
Data science is a combination of data analysis, algorithmic development and technology to solve analytical problems.
A data scientist works on complex and specific problems to bring non-linear growth to the business. For instance, make a credit risk solution for the banking industry or use vehicle imaging and assess the damage for an insurance company automatically.
In simple words, a data scientist is a problem solver who uses data to solve problems that create business value.
A typical life cycle of a data science project looks like this:
- Turn the business problem into a data problem
- Hypothesis generation
- Data collection or extraction
- Exploratory data analysis and hypothesis validation
- Data modeling
- Model deployment
- Present your work to the user / client / final interested
But a data scientist may not be involved in all of these steps.. Let's look at some of the data science-based roles.
Other data-driven features
Would implement the results obtained by the data scientist in production using industry best practices. For instance, implement the machine learning model created for credit risk modeling in banking software.
Data engineers are responsible for storing, pre-process and have this data used by other members of the organization. Create the data pipelines that collect data from multiple resources, transform them and store them in a more usable form.
Some of the tools most used by data engineers are SQL, NoSQL databases, Apache Airflow, Spark, Amazon Redshift, etc.
You can read the data engineering articles here and see if your interests correlate more with data engineering.
Run the business and make day-to-day decisions. You will communicate with the IT side and the business side simultaneously.
Business Analytics professionals must be proficient in presenting business simulations and business planning. Much of your role would be to analyze business trends. For instance, web analysis / price analysis.
Some of the tools that are widely used in business analytics are Excel, Painting, SQL, Python. The most used techniques are: Statistical methods, forecast, predictive modeling, Y narration.
You can read the business analysis articles here.
Then, Do you think you can become a data scientist? Let's look at some of the qualities of a data scientist!!
Qualities of a data scientist
Before choosing data science as your field, must see if it matches your passions, career goals and make sure it makes you happy in the long run. Let's take a look at some of them:
- Crunching of love numbers – Are you crazy about the numbers? For instance, Are you up for a riddle, guesses and estimates at any time of the day? Are you naturally attracted to odds and statistics? Part of being a data scientist is calculating numbers frequently, if you love it, you're lucky!
- Enjoy solving unstructured problems – It is very rare for a data scientist to actually come across a structured problem statement, instead, deals with unstructured data. Are you someone who assists in this area?
- You are curious – asking why it comes naturally to a good data scientist. Some of the best data scientists would stop anyone and ask for a justification if they are unclear: Why did you ask this question? What was your thought process? Why do you assume? are just some examples of these questions.
- I long for problem solving – Data Scientists Need a Gift for Problem Solving. Most of the problems companies would face would be unique to them and it would take a smart solver to solve them.
- Enjoy in-depth research: A great data scientist is always digging deep to understand the hidden secrets of data. You need a researcher's perspective to be a good data scientist. When was the last time you spent hours and hours immersed in solving a problem? Can you do that over and over?
- I love to tell stories – A data scientist must be a fluent presenter. What good is all the hard work if you can't influence your stakeholders? Communicating with data and presenting data-backed stories is one of the most important elements in the life of a data scientist..
What skills to master in 2021 to become a data scientist?
Data Science Toolkit – The most important skill to acquire at the beginning of your journey as a data scientist is the basics of data science and machine learning.. Start from the most common and frequently used data science tools: Python and its libraries like Pandas, NumPy, Matplolib y Seaborn.
Data visualization and SQL – Once you've clarified the basics, must start with the most important skill set of a data scientist. Get familiar with the different data visualization tools and techniques, como Table. During this time, you should also start your SQL journey.
Data exploration – Data is hidden with important information. Getting this information out in the form of insights is data exploration. It's the most essential skill to learn to explore your data with Exploratory Data Analytics (EDA). along with this, you will also need to understand the important statistical concepts required to become a data scientist.
Machine learning basics and the art of storytelling – Now let's get to real machine learning!! After acquiring all the above skills, it's time to start your machine learning journey. In this duration, you will need to cover basic machine learning techniques and the art of storytelling using structured thinking.
Advanced machine learning – Are you done with the basics? Time to go up the notch! You're ready to cover advanced machine learning algorithms. You will also learn about function engineering and how to work with text and image data..
Unsupervised machine learning: Dealing with unstructured data can be challenging, So let's jump to the solution! It's time for you to learn about unsupervised machine learning algorithms like K-Means, Hierarchical Clustering y, Finally, Delve into a project!
Recommendation engines – Curious how Netflix, Amazon, Zomato offer such amazing recommendations? It's time for you to dig deep into the recommender systems. Learn different techniques to build recommendation engines. Learn to use different projects.
Work with time series data – Organizations around the world rely heavily on time series data and machine learning has made the scene even more exciting.. In this duration, you will learn to work with time series data and different techniques to solve problems related to time series.
Introduction to deep learning and computer vision – Deep learning and computer vision are at the forefront of the most current projects in the field of artificial intelligence, whether they are autonomous cars, mask detection cameras and more. During this time, will begin your journey in the field of Deep Learning. You will learn basic deep learning architectures and then solve different computer vision projects.
Natural language processing basics – Wondering how social media giants like Twitter, Facebook and Instagram process incoming text data? Now is the time to focus on the field of natural language processing (PNL). Here you will learn more deep learning architectures and solve projects related to NLP.
Model deployment – What is more essential than building a data science model? Implementing it! Now, Finally, must be aware of the model implementation. Learn different ways to implement your models. Spend time exploring streamlit for model deployment, AWS, and you can also implement the model with Flask.
The salary of a data scientist
Making a career change to data science to get a pay raise is totally justified. But nevertheless, it's not as simple as you might think. There are certain things, such as work experience and your current domain, who will play a MASSIVE role in your post-transition salary decision.
Taking figures from the popular and relatively accurate website called Glass door, this is what the salary situation looks like for a data scientist:
As you can see, the average salary in 2020 is approximately INR 10,00,000 by year.
If you bring a little more experience and have relevant domain experience, can look for a higher position (although this is a bit weird if you have no prior data science experience):
As we said, it all comes down to the relevance of your previous experience. Most of the time, if you are moving from another role to data science, you will see the first graph.
In summary, data science is the most emerging field today and data scientists are creating a better future for humanity. Are you someone who is drawn to this field? I have mentioned all the things you need to know before pursuing a career in data science in the year 2021.