Data Science Skill #1: Deep Learning
Motivated by smart assistants or the cool self-driven car segment or perhaps the funny videos created using deepfakes? All has been possible due to Deep Learning. It is a high growth vertical in the field of Artificial Intelligence thanks to advancements in data storage capabilities and computational advancement.
To excel in this field, you must be well versed in programming (preferably with Python) and have a good grip on linear algebra and mathematics. To start off, you can start building basic models and then jump to advanced models like CNN, RNN, and more.
Libraries like TensorFlow, Keras, and PyTorch are a must if you are want to build your career in deep learning.
Data Science Skill #2: Big Data
We are generating data at a rate of 2.5 Quintillions per day! Due to the rise of the internet, social media networks, IoT there has been a sudden boom in the rate of data we are generating. This data is high in volume, velocity, and veracity which form the 3V’s of Big Data.
Organizations have been overwhelmed with such a large amount of data and they are trying to tackle this data by rapidly adopting Big Data Technology so that this data can be stored properly and efficiently and used when needed.
Hadoop, Spark, Apache Storm, and Flink, Hive are some of the Frameworks/ Tools you must master.
Data Science Skill #3: Software Engineering
To write a high and good quality code that won’t cause havoc during the production stage, it is necessary to know the basics of some of the software engineering subjects like – basic lifecycle of software development projects, data types, compilers, time-space complexity, etc.
Writing efficient and clean code will help you in the long run and help you collaborate with your team members. Again, you don’t need to be a software engineer but being clear with the basics will help you.
Data Science Skill #4: Model Deployment
Model Deployment is the most under-rated step in the machine learning lifecycle.
Let us take an example here. An insurance company has initiated a data science project which uses Vehicle images from accidents to assess the extent of the damage. The data science team works day and night to develop a model that has a near-perfect F1 score. After months of hard work, they have the model ready and the stakeholders love its performance but what after that?
Remember that the end-user, in this case, are the insurance agents and this model needs to be used by multiple people at the same time who are NOT data scientists. Therefore they’ll not be running a Jupyter or Colab notebook on GPUs. This is where you need a complete process of model deployment.
This task is usually done by machine learning engineers but it varies according to the organization you are working in. Even if it is not the job requirement of your company, it is very important to know the basics of model deployment and why it is necessary.