From big E-commerce companies like Amazon, Walmart, to social media giants Facebook and Snapchat – all the way up to hospital management – 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 job in this article.
If you are someone that is excited by this job role and wants to create a future in this field in 2021 then this is the place to be! Don’t worry if you think that the coronavirus has killed the job requirement of a data scientist instead it has made everyone realize the power and importance of predictive algorithms!
If you are beginning your journey in the field of data science, this comprehensive data science learning path for 2021.
The learning path for 2021 is the ultimate and most comprehensive collection of resources put together in a structured manner. This learning path is for anyone who wants to make a career in data science. So whether you are a fresher, have a few years of work experience, or are a mid-level professional – this data science learning path is for you.
Data science is a combination of data analysis, algorithmic development and technology in order to solve analytical problems.
A data scientist works on complex and specific problems to bring non-linear growth to the company. For example, making a credit risk solution for the banking industry or use images of vehicles & 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 data science project lifecycle looks like this:
But a data scientist may not be involved in all of these steps. Let’s look at some of the data science-based roles.
He would Implement the outcomes derived by the data scientist in production by using industry best practices. For example, Deploying the machine learning model built for credit risk modeling on banking software.
Data Engineers are responsible for storing, pre-processing, and making this data used for other members of the organization. They create the data pipelines that collect the data from multiple resources, transform it, and store it in a more usable form.
Some of the most commonly used tools by data engineers are SQL, NoSQL databases, Apache Airflow, Spark, Amazon Redshift, etc.
You can read Data Engineering articles here and see if your interests correlate more to data engineering.
Run the business and take decisions on a day-to-day basis. He’ll be communicating with the IT side and the business side simultaneously.
Business Analytics professionals must be proficient in presenting business simulations and business planning. A large part of their role would be to analyze business trends. For eg, web analytics/pricing analytics.
Some of the tools used extensively in business analytics are Excel, Tableau, SQL, Python. The most commonly used techniques are – Statistical Methods, Forecasting, Predictive Modeling, and storytelling.
You can read the business analytics articles here.
So you think that you can become a data scientist? Let’s looks at some of the qualities of a data scientist!
Before choosing data science as your field, you must see if it matches your passions, career goals, and make sure it makes you happy in the long term. Let us look at a few of them –
Data Science Toolkit – The most important skill to gain 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 such as Pandas, NumPy, Matplolib, and Seaborn.
Data Visualization and SQL – As you have cleared the basics, you need to begin with the most crucial skillset of a data scientist. Familiarize yourself with different data visualization tools and techniques such as Tableau. During this time, you should also begin your SQL journey.
Data Exploration – The data is hidden with important information. Bringing out this information in the form of insights is data exploration. It is the most essential skill to learn how to explore your data with Exploratory Data Analysis (EDA). Along with this, you will also need to understand the important concepts of statistics required to become a data scientist.
Basics of Machine Learning and the art of storytelling – Now let’s get down to actual machine learning! After gaining all the above skills, it’s time for to you start your Machine Learning journey. In this duration, you will need to cover basic ML techniques and the art of storytelling using Structured thinking.
Advanced Machine Learning – Done with basics? It’s time to turn up the notch! You are ready to cover advanced machine learning algorithms. You will also learn about feature engineering and how to work with Text and Image data.
Unsupervised Machine Learning – Dealing with unstructured data can be challenging so let’s jump into the solution! It is time for you to learn about unsupervised machine learning algorithms like K-Means, Hierarchical Clustering, and finally deep dive into a project!
Recommendation engines – Curious how Netflix, Amazon, Zomato give such amazing recommendations? It is time for you to delve into recommendation systems. Learn different techniques to build recommendation engines. Learn using different projects.
Working with Time Series Data – Organizations around the world depend heavily on time-series data and machine learning has made the scenario even more exciting. In this duration, you will learn how to work with Time Series data and different techniques to solve time series related problems.
Introduction to Deep Learning and Computer Vision – Deep Learning and Computer Vision is at the forefront of the most happening projects in the field of AI be it Self driven cars, mask detection cameras, and more. In this time, you will start your journey in the field of Deep Learning. You will learn basic deep learning architectures and then solve different computer vision projects.
Basics of Natural Language Processing – Do you wonder how Social media giants like Twitter, Facebook, Instagram process incoming text data? It is time to move your focus to the field of Natural Language Processing (NLP). Here you will learn more deep learning architectures and solve NLP related projects.
Model Deployment – What is more essential than building a data science model? Deploying it! Now finally you must be aware of model deployment. Learn different ways to deploy your models. You’ll get to spend time on exploring streamlit for model deployment, AWS, and also get to deploy the model using Flask.
Making a career switch to data science for getting a salary bump is entirely justified. However, it isn’t as straightforward as you might think. There are certain things, such as work experience and your current domain, that will play a MASSIVE role in deciding your salary post-transition.
Taking figures from the popular and relatively accurate website called Glassdoor, 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 per year.
If you bring a bit more experience to the table and you have relevant domain experience, you might look at a more senior role (though this is a bit rare if you have no prior data science experience):
As we said, it comes down to how relevant your previous experience is. More often than not, if you are transitioning from another role to data science, you’ll be looking at the first graph.
To summarize, Data Science is the most emerging field today and data scientists are creating a better future for humanity. Are you someone that is attracted to this field? I have mentioned all the things you must know before building a career in data science in the year 2021.
Happy Learning!
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